Matches (13)

IPL (2)

Women's Tri-Series (SL) (1)

County DIV1 (3)

County DIV2 (4)

QUAD T20 Series (MAL) (2)

PSL (1)

RESULT • 51st Match • IPL • T20 • Ahmedabad

224/6

SRH

(20 ov, T:225) 186/6

GT won by 38 runs

Schedule Table Report Videos

RESULT • 3rd Match • Women's Tri-Series (SL) • WODI • Colombo (RPS)

SA-W

235/9

SL-W

(46.3/50 ov, T:236) 237/5

SL Women won by 5 wickets (with 21 balls remaining)

Schedule Table Report Series

Anantha Narayanan

Analysing ODI careers in segments

A look at ODI batting and bowling records of players, splitting each career into three parts

Anantha Narayanan

25-Feb-2013

A few weeks back I had done the Test player analysis splitting the career into two equal halves. Almost the first comment that came was one from WaspSting suggesting that I analyse the career split into three equal parts instead. It was a good idea since it allows us to look at the player's settling down period, the peak period when the player is at his best and the winding down period. The insights which can be drawn can be more finely tuned towards these career-segments. I will do the Test analysis later but decided to do the ODI career analysis on this basis. It also meant that I had to integrate the 500-element match data into the player database. This will be the base for many future analysis.

The one-third positions of an ODI career. Perfect points to look back and forward. In real life, no single ODI player would have known that he was at these positions. However, looking back, with the aid of the massive database, the cut-off points are obvious, even for the currently active players. Please note that all comparisons are within the concerned player.

What is the expectation? In the initial career-segment of a player, he is younger, fitter, (possibly) faster in his actions and does not have to conserve himself. However he is inexperienced, learning the trade and susceptible to selectorial whims. In the middle career-segment, he is settled, carries the reputation built by him and this is expected to be his most productive and effective period. In the final career-segment, he is aging, has a non-syncing body and mind, has to compete with younger players and is also susceptible to selectorial decisions to blood newer players.

Who is likely to deliver? The younger, fitter but inexperienced one or the well-settled king-of-the-patch or the wily, wiser but older player. Our immediate expectation is that most players would have their best career-segment in the middle. But we may be in for surprises. Very difficult to generalize since so many other factors come into play. Let us see.

First, a few analysis criteria.

1. The career-segment is determined slightly differently to Tests and is more common-sense based. It is strictly based on the number of ODIs played. This would allow an evaluation of a player's contribution in batting and bowling together, if we so wish.
2. The overall criteria is 3000 runs for batsmen and 100 wickets for bowlers. 120 batsmen and 108 bowlers qualify. Reasonable population sizes are thus available for analysis. For current players, it is obvious that the last ODI they played could very well be their last ODI ever. If I do this analysis couple of years hence, the numbers for the current batsmen would undergo significant changes.
3. For batsmen, both Runs and RpI figures are analyzed independently. I am a strong proponent of RpI instead of Batting average, especially in ODIs. Since the average number of wickets in an innings is around 7, many of the middle order batsmen, despite the quick-scoring requirements in the later overs, have a good chance of remaining unbeaten. Let me also say, I would rather adopt a system which favours the top order batsmen than the middle and late order.
4. For batsmen, I have also included the career segment splits by Strike Rate in the data table which users can analyze on their own.
5. For bowlers, both Wickets and Bowling average figures are analyzed independently. There is expected to be less correlation between these two.
6. Users may independently analyze the career segment splits by Runs per Over and Bowls per Wicket included in the data table.

First an overall summary.

Batting

These values apply to the creme de la creme of batsmen, the 120 selected ones, who have scored 56% of the total runs scored. The average of average % of runs scored in the initial career-segment of career stands at 31.5%, nearly 9% below the 33.33% mark, indicating that the players, overall, have taken time to settle down: as expected. An alternate measure, which is the average of runs scored in the first half divided by the average of career runs, stands at an almost similar value of 31.4%. The values for the middle career-segment are 34.8% and 34.9%, indicating that this is only around 4% above the expected mark. The values for the final career-segment are 33.8% and 33.7%, indicating that this is only around 1% above the expected mark.

The average of the RpI ratio for the initial career-segment stands slightly lower at 94.3% indicating a 5% lower RpI across these batsmen. The middle career-segment has a figure of 103.9% which indicates that, during this crucial period, the batsmen have achieved about 4% more. The final career-segment period also sees a higher RpI value of 101.3%.

The average of the Batting strike rate follows a similar pattern: 96.8%, 101.7% and 101.5% for the three career-segments.

Bowling

A slightly different [picture emerges for the 108 bowlers. The three career-segments are closely bunched with values of 33.5%, 34.1% and 32.4% respectively, indicating a more even split than batting.

The bowling average variation is similar. 98%, 96% and 107% indicates a good average to start with, still better averages in the middle career-segment and a sharp drop in bowling performances in the final career-segment. The RpO values show similar trends: 99%, 99% and 103%. Only the bowling strike rate values show a slight difference: 102%, 99% and 105%. Remember that below 100 means a better performance.

Let us now look at the graphs. These have been designed specifically for this analysis. There are four graphs. Batting: Runs & RpI and Bowling: Wickets & Bowling average. Each graph shows the initial, middle and final career segment figures of 12 players. There are 3 players each for perfect splits, great start, magnificent middle and fabulous finish. Thus the graphs cover the best performers rather than the ones who have scored runs or wickets. Those players could be perused in the tables.

The first graph relates to Batting: Runs scored in each career-segment.

ODI runs scored by batsmen in their careers, split into three segments © Anantha Narayanan

Usually the high performance players tend to be those who have scored fewer number of runs or captured fewer wickets. There is a nice exception here. Ponting, who has a very high career run aggregate of 13704 runs has got three career-segment values either side of 4600 with a difference of just over 100 runs. That is a wonderful level of consistency exhibited over 375 matches. Tharanga and Bell have similar even splits, however over much lower level of matches. It is of interest to note that all are modern players.

On the other hand, the three who have had the highest level of performance in the initial career-segment are batsmen who played a few years back. Parore scored 50% of his runs in the first career-segment. Kapil Dev and Arnold scored nearly 50% in the first career-segment. Then all of them dropped like stones.

The batsmen who have performed best in the middle career-segment have inverted-V shape patterns. Flintoff achieved nearly 50%, Cullinan above 50% and Gower, around 45%. But it is clear that these are not the high-scoring batsmen.

Now we come back to the fantastic finishers. The three who have finished most strongly are all currently active batsmen: Dilshan, de Villiers and Watson. While Dilshan and de Villiers have had steady upward graphs, Watson had an awful start and scored only 16% of his career runs in the first career-segment, followed by above 40% in the next two. Let us not forget that all are currently active players and these figures are bound to change.

The second graph relates to Batting: Batting average in each career-segment.

ODI Runs per innings for batsmen in their careers, split into three segments © Anantha Narayanan

Ponting not only scores equally in his three career-segments, but also scores these runs at almost the same RpI value of around 37. This is a confirmation of his consistency. Bell has similar figures too. Greenidge averages either side of 41 in his three career-segments.

Parore and Kapil Dev are making their appearance again with high RpI values in the first career-segment followed by huge drops. Not so surprisingly, Pietersen joins these two and has dropped from a high RpI value of 42 to 32 recently. His recent poor ODI form is well-known.

The same, three, Flintoff, Cullinan and Gower are present in the middle graph, in the inverted-V pattern.

de Villiers and Dilshan reappear with a new entrant Virat Kohli who has moved from an RpI of around 38 in the first two career-segments to an amazing 54+ currently. Don't forget this is the RpI and not batting average.

Now for the bowlers.

The career-segment graph relates to Bowling: Wickets captured in each career-segment.

ODI wickets taken by bowlers in their careers, split into three segments © Anantha Narayanan

It is a pleasure to see Dharmasena's career-split: 46 wickets in each career-segment. It cannot get any better. Agarkar is a seriously under-rated player. He is remembered more for his unfortunate sequence of zeroes in Australia. He was a very incisive bowler in ODIs. His bowling strike rate of 33.0 places him amongst the top-20 bowlers of all time. He maintained an excellent balanced distribution of 97, 97 and 94 wickets in the three career-segments. He was a top ODI bowler irrespective of his Test credentials. Walsh was another top bowler who had a great balanced trio of career-segments with 77, 74 and 76 wickets.

Steve Waugh started like a train, capturing 98 wickets in the first career-segment. Then he accumulated only 97 wickets in the next two career-segments. One major fall it was. Abdul Razzak had a fantastic first and them a major slump with 120, 77 and 75 in the three career-segments. Styris started with 61 and then finished with 49 and 27 wickets.

Now for the inverted-V patterns. Azhar Mahmood had 34 and 26 wickets in the first and third career-segments but had a strong middle with 63 wickets. Shastri was a little more even with 42, 58 and 29 wickets. Price was similar to Shastri with 19, 44 and 37 wickets in the three career-segments.

Now the strong finishers. Umar Gul exceeded his first two career-segments of 32 and 41 wickets with a strong third at 85 wickets. Yuvraj has similar figures of 26, 26 and 60 wickets. Langeveldt also follows with 34, 17 and 49 wickets. I will leave it to the readers why the strong finishers are all current bowlers.

The fourth graph relates to Bowling: Bowling average in each career-segment.

ODI bowling averages over a bowler's career, split into three segments © Anantha Narayanan

One cannot but fail to be impressed with Vaas's consistency. Over a 324 match career, he has bowled at an average bowling average of around 28 right through the three career-segments. This is an acceptable average since much of Vaas's bowling had been mostly on unhelpful pitches. Cairns averaged around 34 through his career. Walsh has done so most successfully around the 31 mark. This selection is based on the Index value.

Look at Bishop's fall from the dizzying heights of 19, through 27 to a woeful 42 in the last career-segment. Must have been the impact of his injuries. Abdul Razzak started like a bomb at 21 and then went to a tailspin and averaged 33 in both his remaining career-segments. Ambrose was similar. Starting at around 19, he went to nearly 30 but then recovered to finish around 27 in the final career-segment.

Azhar Mahmood started and finished poorly at 50 and 61, but had a fantastic middle career-segment at 25. Ray Price started at around 60, recovered to a stupendous 24 and then finished respectably at 38. Muralitharan is the third bowler. He started at 28, moved to an unbelievable 18 and finished with a very good average of 26.

Mohammad Rafique started at around 50 and then improved continuously to go through 38 and finish at 30. Cronje followed a similar pattern. Wickramasinghe followed a different V pattern. He started at a high 42, went to 49 and finished very well at 33. Please remember that the selection is based on the ratios for the career-segments.

Now the Batting table, with no special comments, for the batsmen who crossed 7000 runs in their ODI career.

Batting Analysis		Career	Run	Initial	C-S	Middle	C-S	Final	C-S	Career	RpI	Initial	C-S	Middle	C-S	Final	C-S
Batsman	Ctry	Runs	Idx1	Runs	%	Runs	%	Runs	%	RpI	Idx2	RpI	Ratio	RpI	Ratio	RpI	Ratio

Tendulkar	Ind	18426	0.075	5450	29.6%	6565	35.6%	6411	34.8%	40.76	0.230	36.09	0.89	44.06	1.08	42.18	1.03
Ponting	Aus	13704	0.011	4641	33.9%	4549	33.2%	4513	32.9%	37.54	0.036	37.13	0.99	38.23	1.02	37.30	0.99
Jayasuriya	Slk	13430	0.096	3835	28.6%	5114	38.1%	4481	33.4%	31.01	0.234	27.39	0.88	34.55	1.11	30.90	1.00
Inzamam	Pak	11739	0.066	3934	33.5%	4281	36.5%	3524	30.0%	33.54	0.183	32.51	0.97	36.59	1.09	31.46	0.94
Kallis	Saf	11498	0.047	3565	31.0%	4092	35.6%	3842	33.4%	37.45	0.172	34.28	0.92	40.51	1.08	37.67	1.01
Ganguly	Ind	11363	0.082	3851	33.9%	4193	36.9%	3319	29.2%	37.87	0.247	38.90	1.03	41.51	1.10	33.19	0.88
Dravid	Ind	10889	0.018	3673	33.7%	3682	33.8%	3534	32.5%	34.24	0.086	34.01	0.99	35.75	1.04	33.03	0.96
Sangakkara	Slk	10842	0.166	2712	25.0%	4017	37.1%	4113	37.9%	34.75	0.427	27.12	0.78	37.90	1.09	38.80	1.12
Jayawardene	Slk	10772	0.020	3484	32.3%	3637	33.8%	3651	33.9%	30.08	0.071	29.03	0.97	30.82	1.02	30.42	1.01
Lara	Win	10405	0.101	3994	38.4%	3377	32.5%	3035	29.2%	36.00	0.246	40.34	1.12	35.55	0.99	31.95	0.89
Mohd Yousuf	Pak	9720	0.056	2978	30.6%	3512	36.1%	3231	33.2%	35.60	0.167	32.73	0.92	38.59	1.08	35.51	1.00
Gilchrist	Aus	9619	0.052	2969	30.9%	3455	35.9%	3195	33.2%	34.47	0.177	31.59	0.92	37.55	1.09	34.35	1.00
Azharuddin	Ind	9378	0.064	2828	30.2%	3244	34.6%	3306	35.3%	30.44	0.160	28.00	0.92	30.90	1.01	32.41	1.06
de Silva	Slk	9284	0.131	2737	29.5%	3701	39.9%	2846	30.7%	31.36	0.342	27.65	0.88	36.64	1.17	29.65	0.95
S. Anwar	Pak	8824	0.127	2580	29.2%	3503	39.7%	2740	31.1%	36.16	0.390	31.46	0.87	43.25	1.20	33.83	0.94
Chanderpaul	Win	8778	0.126	2578	29.4%	2722	31.0%	3480	39.6%	34.97	0.370	31.06	0.89	32.40	0.93	41.43	1.18
Haynes	Win	8648	0.065	2731	31.6%	3164	36.6%	2753	31.8%	36.48	0.221	34.14	0.94	40.56	1.11	34.85	0.96
Atapattu	Slk	8529	0.103	2656	31.1%	3282	38.5%	2591	30.4%	32.93	0.244	30.18	0.92	36.88	1.12	31.60	0.96
M Waugh	Aus	8500	0.093	2439	28.7%	3086	36.3%	2975	35.0%	36.01	0.262	31.27	0.87	38.58	1.07	38.14	1.06
Gayle	Win	8360	0.038	2747	32.9%	2944	35.2%	2670	31.9%	36.50	0.068	36.14	0.99	37.74	1.03	35.60	0.98
Sehwag	Ind	8238	0.171	2640	32.0%	2147	26.1%	3451	41.9%	33.90	0.488	32.59	0.96	26.84	0.79	42.09	1.24
Gibbs	Saf	8094	0.030	2593	32.0%	2819	34.8%	2682	33.1%	33.72	0.154	31.24	0.93	34.38	1.02	35.76	1.06
Yuvraj Singh	Ind	8051	0.131	2158	26.8%	2951	36.7%	2942	36.5%	31.94	0.324	26.64	0.83	34.72	1.09	34.21	1.07
Fleming	Nzl	8037	0.111	2776	34.5%	2231	27.8%	3030	37.7%	29.87	0.282	29.85	1.00	25.64	0.86	34.04	1.14
S Waugh	Aus	7569	0.068	2264	29.9%	2677	35.4%	2628	34.7%	26.28	0.210	23.58	0.90	26.50	1.01	28.88	1.10
Ranatunga	Slk	7456	0.064	2245	30.1%	2722	36.5%	2487	33.4%	29.23	0.239	25.80	0.88	32.40	1.11	29.61	1.01
J Miandad	Pak	7381	0.148	2264	30.7%	3006	40.7%	2111	28.6%	33.85	0.346	30.19	0.89	39.55	1.17	31.51	0.93
S. Malik	Pak	7170	0.055	2495	34.8%	2191	30.6%	2483	34.6%	28.00	0.113	28.68	1.02	26.40	0.94	28.87	1.03
Astle	Nzl	7090	0.044	2512	35.4%	2370	33.4%	2208	31.1%	32.67	0.095	33.49	1.03	33.38	1.02	31.10	0.95
S Afridi	Pak	7068	0.053	2545	36.0%	2320	32.8%	2203	31.2%	22.01	0.065	22.72	1.03	21.48	0.98	21.81	0.99
Clarke	Aus	7068	0.125	2237	31.6%	2033	28.8%	2798	39.6%	35.69	0.282	33.39	0.94	32.79	0.92	40.55	1.14
Bevan	Aus	6912	0.146	2468	35.7%	2647	38.3%	1799	26.0%	35.26	0.254	35.77	1.01	38.93	1.10	30.49	0.86
Dhoni	Ind	6908	0.056	2114	30.6%	2496	36.1%	2298	33.3%	36.74	0.174	33.56	0.91	39.62	1.08	37.06	1.01
Younis Khan	Pak	6824	0.061	2119	31.1%	2485	36.4%	2224	32.6%	29.03	0.129	27.17	0.94	30.68	1.06	29.26	1.01
Kirsten	Saf	6798	0.104	2564	37.7%	1913	28.1%	2321	34.1%	36.74	0.321	41.35	1.13	30.85	0.84	38.05	1.04
Flower	Zim	6786	0.094	2016	29.7%	2189	32.3%	2580	38.0%	32.62	0.292	29.22	0.90	31.27	0.96	37.39	1.15
Richards	Win	6721	0.148	2598	38.7%	2381	35.4%	1742	25.9%	40.24	0.391	43.30	1.08	44.92	1.12	32.26	0.80
Dilshan	Slk	6715	0.249	1540	22.9%	2101	31.3%	3074	45.8%	30.11	0.579	21.10	0.70	30.01	1.00	38.42	1.28

Thanks to Aneesh for his reference to Richards. How can we have a table without Richards? Hence I have added a few top batsmen, including Richards, to the table.

It is necessary to explain two Index values which have been determined and have been used for selection in the graphs. The Idx1 is for the runs scored in the three career-segments. It is the sum of the absolute difference between % for each career-segment and 0.333, for the three career-segments. If the three career-segments had shares of 0.28, 0.32 and 0.40, the Idx1 value is 0.133 (0.053+0.0133+0.0667). The lower this value is, the closer the three values are to the exact third fraction. For Ponting this index value is an excellent 0.011.

The second index for RpI, Idx2, is calculated differently because of the different method of determining the impact of the values. I use a ratio between career RpI and career-segment RpI for each career-segment. Hence this index value is determined as the sum of the absolute difference between the career RpI and career-segment RpI divided by the career RpI. For Tendulkar the figure is (abs(40.76-36.09)+abs(40.76-44.06)+abs(40.76-42.18))/40.76 which works out to 0.230, indicating a fair degree of variation. For Ponting the figure is (abs(37.54-37.13)+abs(37.54-38.23)+abs(37.54-37.30))/37.54 which works out to 0.036, indicating almost no variation.

This is too big a table for me to offer a lot of comments. I will let the readers to do that. I will only do a summary.

Lara(38.4%, 32.5% and 29.2%), Sangakkara(25, 37.1 and 37.9), de Silva(29.5, 39.9 and 30.7) and Saeed Anwar(29.2, 39.7 and 31.1) have had fairly varying careers.

Tendulkar(29.6, 35.6 and 34.8), Inzamam(33.5, 36.5 and 30.0), Md. Yousuf(30.6, 36.1 and 33.2) and Gilchrist(30.0, 35.9 and 33.2) have had reasonably stable careers.

Ponting(33.9, 33.2 and 32.9), Kallis(31.0, 35.6 and 33.4), Dravid(33.7, 33.8 and 32.5), Jayawardene(32.3, 33.8 and 33.9) have had very stable careers.

Ponting's RpI values have also shown a remarkable level of consistency. Dravid and Jayawardene are fine. Look at the wide variation of RpI values for Sangakkara, Sehwag, Saeed Anwar et al.

Bowling Analysis			Career	Wkts	Initial	C-S	Middle	C-S	Final	C-S	Career	Avge	Initial	C-S	Middle	C-S	Final	C-S
Bowler	Ctry	Type	Wkts	Idx1	Wkts	%	Wkts	%	Wkts	%	Avge	Idx2	Avge	Ratio	Avge	Ratio	Avge	Ratio

Muralitharan	Slk	rob	534	0.101	158	29.6%	205	38.4%	171	32.0%	23.08	0.58	28.15	1.22	17.18	0.74	25.47	1.10
W Akram	Pak	LF	502	0.062	158	31.5%	183	36.5%	161	32.1%	23.53	0.17	24.03	1.02	21.66	0.92	25.17	1.07
W Younis	Pak	RF	416	0.050	149	35.8%	138	33.2%	129	31.0%	23.85	0.28	20.68	0.87	25.91	1.09	25.30	1.06
Vaas C	Slk	LFM	400	0.022	136	34.0%	135	33.8%	129	32.2%	27.54	0.05	27.04	0.98	27.37	0.99	28.25	1.03
Pollock	Saf	RFM	393	0.112	146	37.2%	138	35.1%	109	27.7%	24.51	0.23	23.38	0.95	23.18	0.95	27.70	1.13
McGrath	Aus	RF	381	0.042	120	31.5%	135	35.4%	126	33.1%	22.02	0.30	25.48	1.16	20.44	0.93	20.41	0.93
Lee	Aus	RF	380	0.060	138	36.3%	125	32.9%	117	30.8%	23.36	0.17	21.49	0.92	23.90	1.02	24.98	1.07
S Afridi	Pak	rlb	347	0.252	72	20.7%	121	34.9%	154	44.4%	33.52	0.61	46.93	1.40	29.83	0.89	30.14	0.90
Kumble	Ind	rlb	337	0.103	120	35.6%	122	36.2%	95	28.2%	30.90	0.40	28.20	0.91	28.19	0.91	37.78	1.22
Jayasuriya	Slk	lsp	323	0.165	125	38.7%	117	36.2%	81	25.1%	36.74	0.17	34.10	0.93	37.62	1.02	39.53	1.08
Srinath	Ind	RFM	315	0.044	112	35.6%	100	31.7%	103	32.7%	28.08	0.25	24.79	0.88	29.76	1.06	30.03	1.07
Warne	Aus	rlb	293	0.125	116	39.6%	92	31.4%	85	29.0%	25.73	0.47	20.47	0.80	29.23	1.14	29.13	1.13
Agarkar	Ind	RFM	288	0.014	97	33.7%	97	33.7%	94	32.6%	27.85	0.16	30.13	1.08	25.58	0.92	27.84	1.00
Saqlain M	Pak	rob	288	0.139	110	38.2%	102	35.4%	76	26.4%	21.79	0.51	19.58	0.90	19.32	0.89	28.30	1.30
Vettori	Nzl	lsp	282	0.128	76	27.0%	98	34.8%	108	38.3%	31.50	0.52	40.88	1.30	28.42	0.90	27.69	0.88
Zaheer Khan	Ind	LFM	282	0.071	104	36.9%	84	29.8%	94	33.3%	29.44	0.29	25.52	0.87	32.33	1.10	31.18	1.06
Donald	Saf	RF	272	0.172	74	27.2%	114	41.9%	84	30.9%	21.79	0.47	26.43	1.21	17.68	0.81	23.26	1.07
Kallis	Saf	RFM	270	0.104	93	34.4%	101	37.4%	76	28.1%	31.70	0.16	29.48	0.93	34.04	1.07	31.29	0.99
A Razzaq	Pak	RFM	269	0.226	120	44.6%	77	28.6%	72	26.8%	31.83	0.53	25.43	0.80	35.48	1.11	38.60	1.21
Ntini	Saf	RF	266	0.108	81	30.5%	103	38.7%	82	30.8%	24.67	0.31	25.30	1.03	21.30	0.86	28.27	1.15
Harbhajan	Ind	rob	259	0.106	100	38.6%	74	28.6%	85	32.8%	33.40	0.35	28.29	0.85	38.97	1.17	34.55	1.03
Kapil Dev	Ind	RFM	253	0.069	93	36.8%	82	32.4%	78	30.8%	27.45	0.16	25.43	0.93	28.51	1.04	28.74	1.05
ShoaibAkhtar	Pak	RF	247	0.092	92	37.2%	84	34.0%	71	28.7%	24.98	0.49	20.77	0.83	23.75	0.95	31.89	1.28
Streak	Zim	RFM	239	0.045	76	31.8%	78	32.6%	85	35.6%	29.83	0.13	31.20	1.05	30.42	1.02	28.06	0.94
Gough	Eng	RF	235	0.096	86	36.6%	82	34.9%	67	28.5%	26.44	0.29	24.20	0.92	25.28	0.96	30.73	1.16
Walsh	Win	RF	227	0.015	77	33.9%	74	32.6%	76	33.5%	30.47	0.05	30.43	1.00	31.30	1.03	29.71	0.98
Ambrose	Win	RF	225	0.204	98	43.6%	62	27.6%	65	28.9%	24.13	0.51	19.30	0.80	29.15	1.21	26.63	1.10
Anderson	Eng	RFM	216	0.287	75	34.7%	41	19.0%	100	46.3%	30.57	0.33	25.28	0.83	32.15	1.05	33.89	1.11
Mills	Nzl	RM	205	0.153	56	27.3%	84	41.0%	65	31.7%	26.00	0.43	31.34	1.21	21.49	0.83	27.22	1.05
McDermott	Aus	RF	203	0.076	60	29.6%	75	36.9%	68	33.5%	24.72	0.49	31.35	1.27	21.20	0.86	22.75	0.92
Harris	Nzl	RM	203	0.184	86	42.4%	68	33.5%	49	24.1%	37.50	0.41	31.49	0.84	40.62	1.08	43.73	1.17
Cairns	Nzl	RFM	201	0.119	62	30.8%	79	39.3%	60	29.9%	32.81	0.05	33.48	1.02	32.95	1.00	31.92	0.97
Malinga	Slk	RFM	200	0.123	62	31.0%	59	29.5%	79	39.5%	26.58	0.19	24.08	0.91	29.07	1.09	26.67	1.00

The two index values are calculated similar to batting.

We have already seen how consistently Vaas has performed. McGrath has also been very consistent right through. 120, 135 and 126 wickets represent a very even set of career-segments. Wasim Akram and Waqar Younis have also been quite consistent. Look at how much of a variation there is for Shahid Afridi: 72, 121 and 154 wickets.Warne has also been fairly inconsistent.

We have already seen Muralitharan's inverted-V pattern exhibiting widely varying bowling average. Wasim Akram and Brett Lee have pretty good distribution. All the other top bowlers exhibit a fair degree of variation, especially Shahid Afridi and Warne.

The overall RpO index values are 0.99, 0.99 and 1.03, indicating that the third career-segment has been slightly more difficult for the bowlers. Vaas's consistency on bowling accuracy is remarkable. Muralitharan is way off and the other top bowlers have done quite well. This is probably the most stable amongst all measures across the career. Barring Murali, Warne and Kumble, most of the other top bowlers all have index values below 0.1.

The overall values are 1.02, 0.99 and 1.05, again indicating a more difficult third career-segment and an excellent middle career-segment. Surprisingly Shahid Afridi who has an excellent distribution in the RpO values, viz., 4.74, 4.52 and 4.57, varies like a yo-yo in the BpW values, viz., 44, 60 and 40.

To download/view the Excel sheet containing the ODI Players career analysis tables, please click/right-click here.

I think I have had enough of these career level analyses. Let me look at something totally different now. The next is on Wicket-keepers and then the best-10-year analysis for Test players: this may look like a career-level analysis but will need a totally different perspective.

A footnote. We do many comparisons across ages, formats, player types, playing conditions, game rules etc. We do not always have a clear set of comparing norms identified. Many of these are subjective and are personal opinions. In a way it is similar to the question which is raised after each Olympics. Who is the greatest Olympian of all times?

- Is it Usain Bolt with an unprecedented treble in two consecutive Olympics?
- Is it Michael Phelps who has captured 22 medals in 3 Olympics, 18 of which were gold?
- Is it Carl Lewis who captured 10 medals, including 9 gold, spread across 4 Olympics?
- Is it Paavo Nurmi who has captured 12 medals in three Olympics, 9 of which were gold?
not to say anything of
- Lasse Viren who did the 5000/10000 meters double in two consecutive Olympics, Blankers-Coen who won 4 gold medals in London, Jesse Owens who won 4 gold medals in a totally hostile situation in Berlin, Daley Thomson and Bob Mathias who won Decathlon in 2 consecutive Olympics, Steven Redgrave who won 5 gold and 1 bronze medals in 5 Olympics and Al Oerter who captured gold in the same event in 4 consecutive Olympics.

The bottom-line is that there is no single simple answer to any of these questions.

Full post

Test players' career-to-date average analysis

A detailed analysis of Test batsmen and bowlers using career-to-date averages

Anantha Narayanan

25-Feb-2013

This is a happenstance article. It was never planned. As I was working on the career halves article, I saw in front of me a 200-data-capsule segment for each player, each capsule containing the player's summary for one Test. Until last year my CTD values were embedded in the specific Test data segment. This could be used effectively but only for analysis related to that specific Test. Only now do I have the complete career data, including individual Test performances, for each player in one place. That paved the way for the career-halves analysis. And I can instantly say "Yes" to the career-thirds analysis. It also made me think of the way the CTD values have progressed and do some nice analysis of the CTD averages. Hence this article.

Most important feature of this article is that I have used the Test as the basis rather than the innings/spell. It is unfair to look at positions during the middle of a Test. A batsman might have had a stroke of misfortune and got out for a low score or a bowler gets caught on a flat track and has a 1 for 100 spell. However the second innings allows the batsman and bowler to make up for this, if the circumstances allowed it. And the unit of Test makes it fair across types of players. Finally it allows me to limit the data capsules to 200. If Tendulkar plays in more than 12 Tests, let me see. I can only see 6 home Tests in the next 12 months for India and do not want to see Tendulkar struggling in his 41st year, limping towards the 200th Test, leave alone my data related problems !!!

I have analyzed both batting and bowling CTD averages in this article. I have used as a criterion of top quality, could easily be some other values, 50.0 for Batting average and 25.0 for Bowling average. Let us not get anything else into this: the period, peer comparisons, the quality of bowling/batting, pitches etc. Let this be a reasonably tough benchmark so that the final numbers have a lot of weight behind them.

This is not a binary 1-0 situation. It is a waste of time saying that XYZ never fell below 50 or ABC never went above 25 in their careers. That is a one paragraph answer to a reader query. I am going to measure the % of such falls so that we can derive a number of useful insights. As I do normally now, I have a graph based on the performance parameter, a short table ordered on the career accumulations and an all-encompassing Excel sheet which covers the entire lot of players. You would always do well to download and view the Excel sheet.

First the graphs. The graphs, as nowadays often happens, are special for this article. The tally of my specialized graph-generation programs has now crossed 20. Here I wanted to show each bowler separately for clarity. Hence I could only show six batsmen and six bowlers. Anything more would have made the graphs uncomfortably tall.

Batsmen CTD Avge analysis

Batsmen with highest % of Tests with 50-plus averages
© Anantha Narayanan

One thing which the graphs clearly explain is the fact that the players may start way-out in either direction but soon gravitate to their career average and plateau around there. Unlike individual innings, there would not be any positive or negative spikes. Once a batsman has reached, say, 100 innings at 50, a score of 0 or 100 will only move his average up or down by 0.5. Similarly after 200 wickets at 25, a 0 for 100 or 5 for 25 spell would move the average up or down by 0.5.

Only two batsmen have never fallen below 50.0 in their entire career. It is that tough a mark. The first is that fighter extraordinaire, the thorn in any bowling attack for long times, the feisty Javed Miandad. Miandad played 125 Tests, spread over 17 years. He never dropped below 50.0. Please stop for a moment and reflect on the achievement. Dwell on the number of Tests played and the length of time. Hats off to one great character and cricketer.

The other is a totally different type of batsman. Coming as he does from the doughty Yorkshire stock, Sutcliffe never fell below 50.0, why let me extend it further, never fell below 60.0 in his 54-Test, 11-year career. Not a short career by any means: only looks short compared to Miandad. He built up a good average and even though he had an indifferent second half of his career, his buffer was enough to never let his career average go below 60.

Now comes Bradman. His poor start meant that he averaged 18.0 and 32.67 at the end of his first two Tests. Then he managed to reach exactly 50.0 at the end of the third Test. Afterwards he moved steadily through the 60s/70s/80s/90s to 103.0 at the end of his 9th Test. Then onwards, he dropped below 90, just once. So his % of >50 averages is 98.1, 50 out of 52. Anyone can work out that Bradman could have sustained a string of 69 consecutive zeros, after 1948, and could still have maintained a 50+ average.

Hussey is next, with 97.3%. In 73 Tests he has fallen below 50 twice, due to his recent drop in form. Worrell gets a 94.1% (48 out of 51) and Hutton 93.7% (74 out of 79). Note Hutton's pretty poor start and Worrell's stupendous start.

Bowlers CTD Avge analysis

Bowlers with highest % of Tests with sub-25 averages
© Anantha Narayanan

In contrast to the batsmen, there are five bowlers who have achieved 100% rate of maintaining a sub-25 bowling average throughout their careers. These are presented in order of wickets captured. Trueman, Barnes, Miller, Johnston and Colin Croft form the quintet. Trueman has gone through a 70-Test career without ever dropping below 25. Maybe he did not tour the sub-continent and that might have helped. But this is one heck of an achievement. Barnes never fell below 22.1, leave alone 25. But the caveat is always that the South Africans were there providing Barnes with 83 wickets at 9.9. That helped.

Miller, a very much under-rated bowler and the only all-rounder in this elite group never went above 23.2. It is time Miller comes into all discussions on top all-rounders: he should be there right in the first minute of discussion, not as an after-thought. Did someone ask me what his batting average was: a mere 37. Now comes Johnston, another under-rated left arm pace bowler, most of the times playing under the shadow of Davidson, but a wonderful bowler on his own rights. He never went above 24. Colin Croft, who could not get to play more matches, is the fifth bowler who never went above 24. He is an enigma. How did he not get to play more matches?

Finally the only bowler in this group who does not have 100%. But really does not matter. This should set right any doubts on Lillee's greatness. In a career of 70 Tests Lillee went above 25 just three times, that too only to 25.3. Oh what a bowler. I think it would be a great disservice to call Lillee over-rated and would only betray a narrow chauvinistic attitude. Let us not demean greatness. We demean ourselves.

Batsmen CTD Average Analysis Table

Batsman	Start	Finish				Tests		High	Low	High-Low
CTD Averages			Tests	Runs	Avge	> 50	%	Avge	Avge	/CarAvge

Tendulkar	1989	2012	188	15470	55.45	154	81.9	58.9	35.1	43.0
Ponting R.T	1995	2012	165	13346	52.75	102	61.8	60.0	36.6	44.2
Dravid R	1996	2012	164	13288	52.31	146	89.0	58.8	47.0	22.5
Kallis J.H	1995	2012	153	12561	57.62	85	55.6	58.2	22.7	61.7
Lara B.C	1990	2006	131	11953	52.89	100	76.3	62.6	46.1	31.2
Border A.R	1979	1994	156	11174	50.56	115	73.7	54.1	42.8	22.5
Waugh S.R	1985	2004	168	10927	51.06	55	32.7	51.9	20.8	60.8
Jayawardene	1997	2012	133	10540	50.43	48	36.1	54.7	39.0	31.2
Chanderpaul	1994	2012	143	10290	50.20	23	16.1	61.8	38.6	46.2
Gavaskar	1971	1987	125	10122	51.12	111	88.8	61.1	47.7	26.2
Sangakkara	2000	2012	111	9872	56.74	50	45.0	57.2	37.9	34.1
Gooch G.A	1975	1995	118	8900	42.58	0	0.0	44.8	24.9	46.6
J Miandad	1976	1993	124	8832	52.57	124	100.0	75.8	51.7	45.8
Inzamam	1992	2007	120	8830	49.61	21	17.5	51.8	31.1	41.8
Laxman	1996	2012	134	8781	45.97	0	0.0	47.8	24.1	51.6
Hayden M.L	1994	2009	103	8626	50.74	72	69.9	59.0	24.4	68.2
Richards	1974	1991	121	8540	50.24	108	89.3	64.1	30.4	67.2
Stewart A.J	1990	2003	133	8465	39.56	0	0.0	46.1	24.6	54.4
Gower D.I	1978	1992	117	8231	44.25	15	12.8	60.0	40.5	44.0
Sehwag V	2001	2012	96	8178	50.80	79	82.3	56.7	39.9	33.1
Smith G.C	2002	2012	100	8174	50.15	59	59.0	78.6	45.8	65.4
Boycott G	1964	1982	108	8114	47.73	15	13.9	52.1	36.7	32.4
Sobers	1954	1974	93	8032	57.78	76	81.7	63.9	29.5	59.5
Waugh M.E	1991	2002	128	8029	41.82	9	7.0	47.8	33.2	35.0

Bradman D.G	1928	1948	52	6996	99.94	51	98.1	112.3	89.6	22.8
Sutcliffe H	1924	1935	54	4555	60.73	54	100.0	82.6	60.7	36.0

This table is ordered by the career aggregate of runs to ensure that all top batsmen are covered. One other important information needs to be understood. The career-high average and career-low average values are computed after the first 10 Tests are played. This is to allow the batsmen to settle down after very poor starts (Gooch/Kallis/S Waugh) or come down to earth after terrific starts (Gavaskar/Harvey/Azharuddin). I had initially done this work after 5 Tests but Milind, who is currently doing the editing task, suggested a change to 10 Tests to reduce the differences between maximum and minimum. It has worked out very well. Thanks a lot, Milind.

In order to get a handle on the variations, I have also determined a simple Avge Ratio which is (HighAvge - LowAvge) / CareerAvge, expressed as a percentage. I know Std Deviation might be a better divisor but this is sufficient at this stage. A high ratio need not necessarily mean an inconsistent career, it may be a reflection of a great or atrocious start. However a low ratio does mean a very consistent career. Do not forget that 10 Tests are given for the player to settle down.

We already know about Miandad who has 100%. Tendulkar's >50 tally is a good 82%, exceeded by Dravid and Gavaskar with 89%. Ponting has an indifferent 62% and Steve Waugh, a poor 33%. Lara is thereabouts with 76%. Richards has a high 89% indicating that the later Tests were the odd ones out. At the other end, Laxman, Vengsarkar, Gooch, Cowdrey, Boon, Langer et al have never exceeded 50.

Miandad reached 76 and his lowest was 52. Tendulkar has never exceeded 59 and once fell to around 35. Lara's range is a respectable 63 and 46. Look at Harvey who went to 95, after the tenth Test. Also Hussey reached 86.3. Samaraweera reached Bradmanesque levels of 83.0. Graeme Smith's band is between 78 and 45. Mark Taylor's range is 70 and 42. Steve Waugh once went as low as 21. Kallis to 23 and now he is 57+. Finally look at Adams, not in this table, who is the only one with an Avge Ratio exceeding 110. He has a range of 87 and 51 which is wider than his career average.

Harvey's extraordinarily high Avge Ratio is easy to understand. A phenomenal start to the career meant that he came down to 50 only in the 70th Test. This is reflected in the 97% ratio. And the amazing thing is that Harvey has this high ratio because of a fabulous start, not a terrible one like many top batsmen. Kallis is the other way around: a very poor start means he has an Avge Ratio of 87. And Steve Waugh, similarly on 77%. Many top batsmen are around the 40% mark.

Border is the best amongst the top batsmen with a very low Avge Ratio of 22.5. Gavaskar has an equally low figure of 26% and Lara and Jayawardene clock in at 31%.

Bowlers CTD Average Analysis Table

Bowler	Start	Finish				Tests		Low	High	High-Low
CTD Averages			Tests	Wkts	Avge	< 25	%	Avge	Avge	/CarAvge

Muralitharan	1992	2010	133	800	22.73	68	51.1	21.3	33.9	55.3
Warne S.K	1992	2007	145	708	25.42	50	34.5	22.6	35.7	51.8
Kumble A	1990	2008	132	619	29.65	13	9.8	23.2	29.8	22.3
McGrath G.D	1993	2007	124	563	21.64	100	80.6	21.0	33.9	59.3
Walsh C.A	1984	2001	132	519	24.44	56	42.4	21.2	26.2	20.6
Kapil Dev N	1978	1994	131	434	29.65	0	0.0	26.1	39.1	43.7
Hadlee R.J	1973	1990	86	431	22.30	39	45.3	22.0	35.6	60.9
Pollock S.M	1995	2008	108	421	23.12	102	94.4	19.9	25.8	25.5
Wasim Akram	1985	2002	104	414	23.62	75	72.1	22.3	28.8	27.4
Harbhajan	1998	2011	98	406	32.22	0	0.0	25.6	32.2	20.7
Ambrose	1988	2000	98	405	20.99	85	86.7	20.5	27.4	32.6
Ntini M	1998	2009	101	390	28.83	0	0.0	25.7	37.8	42.2
Botham I.T	1977	1992	102	383	28.40	63	61.8	16.5	28.4	41.7
Marshall	1978	1991	81	376	20.95	55	67.9	20.3	32.9	60.2
Waqar Younis	1989	2003	87	373	23.56	82	94.3	18.4	23.6	22.1
Imran Khan	1971	1992	88	362	22.81	46	52.3	21.5	35.3	60.6
Vettori D.L	1997	2012	112	360	34.42	1	0.9	30.3	38.1	22.8
Vaas WPUJC	1994	2009	111	355	29.58	13	11.7	20.7	32.1	38.7
Lillee D.K	1971	1984	70	355	23.92	67	95.7	21.6	25.3	15.5
Donald A.A	1992	2002	72	330	22.25	61	84.7	21.5	26.5	22.7
Willis	1971	1984	90	325	25.20	31	34.4	23.5	33.2	38.8
Lee B	1999	2008	76	310	30.82	18	23.7	19.4	33.0	44.3
Gibbs L.R	1958	1976	79	309	29.09	38	48.1	20.6	29.1	29.3
Trueman F.S	1952	1965	67	307	21.58	67	100.0	20.7	23.0	10.5

Barnes S.F	1901	1914	27	189	16.43	27	100.0	16.4	22.1	34.3
Miller K.R	1946	1956	55	170	22.98	55	100.0	20.5	23.2	12.1
Johnston	1947	1955	40	160	23.91	40	100.0	16.7	23.9	30.0
Croft C.E.H	1977	1982	27	125	23.30	27	100.0	20.5	23.7	13.8

This table is ordered by the career aggregate of wickets to ensure that all top bowlers are covered. The career-high average and career-low average values are computed after the first 10 Tests are played. This is to allow the bowlers to settle down after very poor starts (Warne/Hadlee/Kapil) or come down to earth after terrific starts (Botham/Underwood/Lee).

McGrath has a very high 80% sub-25 average situations. However Waqar Younis and Shaun Pollock have better figures with 94%. But the best is Lillee with 95.7%. Donald is also quite high at 85%. Of course Trueman amongst the top bowlers stands alone at 100%. There are four other bowlers, with sub-200 wicket tally, who have 100%: Barnes, Miller, Johnston and Croft. Barnes stabilized to a figure around 20 after 20 Tests had been played but finished with an excellent average of 16+, mainly because he captured 67 wickets in his last Tests at an extraordinary sub-10 average.

Kapil has never been there. Similarly Ntini and Harbhajan Singh. And Flintoff, Bedi, Qadir et al. Vettori touched the mark once in the fourth Test. It can be understood why Kumble has touched this mark a mere 13 times.

Botham's average had come down to 16.5. Waqar Younis came down to 18+. And Lee and Underwood also had very low figures.

Look at the well-above 50% values of the Avge Ratios for Murali, Warne, McGrath, Hadlee, Marshall, Imran Khan. This indicates indifferent starts to their illustrious careers: And how they all turned the tide. And look at how little the careers of Kumble, Harbhajan, Waqar Younis, have oscillated, with ratios of around 22%. Finally look at Croft: albeit a brief career, but within a narrow band of 14%. Amongst top bowlers, only Flintoff, not in the table, has a 100+ Avge Ratio.

To download/view the Excel sheet containing the Batsmen CTD Avge analysis and Bowlers CTD Avge analysis tables, please Click/Right-Click HERE. The serious students of the game are going to have a link to this Excel file on their desktop and refer to it a few times a day. I have also given the CTD Averages for each of the 160 odd players at the end of each Test they played. Thus this is a huge data bank.

Again a personal request. Please stick to the article and don't start a "xyz vs abc" discussion which does not relate to the article.

Full post

Test Bowling: location summary, by innings and vs country

An analysis of Test bowling performances by location, opposition and innings number in the match

Anantha Narayanan

25-Feb-2013

This completes the sequence of four articles in which I analyzed ODI/Test Batting/Bowling performances at home or away, against different teams and in the first or second innings. While the graphs and tables provide immediate and easy viewing, the core of the articles is the exhaustive set of Excel tables which could be downloaded by readers and different types of analysis done by themselves. That is what has been done so far and hundreds of informed and insightful comments have come out so far. Normally I do analysis-centric articles which take on and expound a theme. Once in a while I do different types of articles in which I go deep in one area of the game and provide data tables around it. This is one such article.

This information is certainly available through StatsGuru of Cricinfo. However, what will not be available are the composite multidimensional tables which are provided here. You would have to put in multiple queries and saving the tables in an accessible format is another problem.

In order to avoid the usual questions and comments which relate to specific players, let me explain how these series of articles have been structured. I have covered the top/selected 10-15 players in a graph to visually present the variations. Then I present data tables, in the body of the articles, which normally cover the top 30 players or so. However the most important of the tables are the ones which have been uploaded and are available for downloading for permanent storage and perusal. Normally these cover the complete set of players, say 160 or so, who meet the cut-off criteria. So, before coming out with comments that "Willis or Bedi or Croft is not mentioned", please download the tables and check. Superficial reading of the articles is not enough.

The vs Country grouping is simple. I have 10 countries: Australia, Bangladesh, England, India, New Zealand, Pakistan, South Africa, Sri Lanka, West Indies & Zimbabwe. And the analysis is very extensive in that it is by country played against: at home, away and across career. These being Test matches, I have also analyzed the career averages by first and second innings.

1. The criterion is 100 Test wickets for the career analysis and other analyses. I know that Bond and Mailey will miss out. However I do not want to lower the target further since the vs Country numbers would be too low. I have also raised the bar for the Home/Away analysis so that we are able to look at the real performers.
2. Bowling Average is a complete measure and explains what all needs to be communicated regarding a bowler. In addition, in my graphs, I have taken the liberty of not including pre-WW1 bowlers other than Barnes. The reasons are obvious. Averages of below 20 were quite frequent and this would have distorted the presentation.
3. There are problems with the single Australia-ICC Test match. It could be said that the ICC players played against Australia away. Fine. But what about Australia ? Which country did they play against ? And I am not certainly going to allocate part of the match runs/wickets only. So this match has been completely excluded from the analysis. So do not come out with a complaint if you see Muralitharan with 795 wickets.
4. There is no neutral location. Too few matches (probably a maximum of 20) have been played in the neutral locations for me to classify these. These are treated as "Away" for both teams, probably a very fair assignment.

First the graphs. I would only offer limited comments since I expect the readers to come out with their own comments. I might anyhow miss some obvious comment. Should not really matter. The ordering is different for different modes of presentation since we can get different insights. In general, the graphs are ordered by the concerned Bowling Average values and the tables are ordered by the appropriate Wickets captured values.

Bowler analysis - Summary by location / innings / quality of batsmen dismissed

Summary of bowling averages and wickets distrbution
© Anantha Narayanan

Look at Hadlee, McGrath and Warne. Almost the same bowling averages at home and away. Laker, Muralitharan and Imran Khan have performed significantly better at home. Davidson has performed much better away, as do Garner and Ambrose, to a lesser extent.

Most bowler have performed better in the first innings. Marshall, Laker, Warne and Donald have performed much better in the second innings. Look at Imran Khan's almost similar first and second innings performances.

Nearly 50% of the wickets captured by McGrath and Donald are top-order wickets. Ambrose, Davidson, Miller are close behind. Warne has the highest share of late order wickets, followed by O'Reilly and Muralitharan.

Bowler analysis - All matches - by opposing country

Summary of bowling averages against each team
© Anantha Narayanan

This graph requires some explanation. These are ordered by the Bowling Average values. The player's performance against the 10 teams are plotted. Blue circles indicate Bowling Average values of below 25.0 and Red circles indicate Bowling Average values above 25.0. The number of wickets and bowling averages are displayed under each country.

Possibly the best performances across all countries are by Davidson, Trueman and McGrath. Only for this composite graph, I have included a new table indicating the top three bowlers against each country. I have done a selection of mine, keeping in view both number of wickets and bowling average.

Australia	Hadlee	130 @ 21.6	Ambrose	128 @ 21.2	Laker	79 @ 18.3
England	Ambrose	164 @ 18.8	Garner	92 @ 17.9	Marshall	127 @ 19.2
West Indies	Vaas	55 @ 16.6	McGrath	110 @ 19.4	Muralitharan	82 @ 19.6
India	Trueman	54 @ 14.8	Donald	57 @ 17.3	McGrath	51 @ 18.6
South Africa	Barnes	83 @ 9.9	Grimmett	77 @ 15.6	Muralitharan	104 @ 22.2
Pakistan	Croft	50 @ 19.6	Warne	90 @ 20.2	Marshall	50 @ 20.7
Sri Lanka	Imran Khan	46 @ 14.6	Wasim Akram	63 @ 21.3	Waqar Younis	56 @ 22.7
New Zealand	Wasim Akram	60 @ 17.0	Willis	60 @ 18.9	Bedi	57 @ 19.1
Bangladesh	Muralitharan	89 @ 13.4	Vettori	51 @ 16.1
Zimbabwe	Muralitharan	87 @ 16.9	Waqar Younis	62 @ 19.9

Some of these figures have to be taken with a lot of salt. For instance, Trueman's figures against India or Barnes' figures against South Africa. The Indian batting during the 1950s, especially away, was quite poor and any bowler worth his salt would have averaged below 20. The performances to appreciate and applaud are Hadlee and Ambrose against Australia, the Caribbean trio against England, McGrath against West Indies and Donald and McGrath against India. Mention has to be made of Vaas against West Indies. Surprisingly Warne has done phenomenally well against Pakistan and quite poorly against India, despite the fact that both teams have very good players of spin.

Bowler analysis - Home matches - by opposing country

Summary of bowling averages against each team in home Tests
© Anantha Narayanan

Surprisingly the only bowlers to have above-par performances against all countries are Trueman and Marshall. Laker has a blip against West Indies. As far as teams are concerned, Australia and Pakistan have travelled well. The triple-red-circles against India are with very few wickets. I have also shown the worst three bowlers at the lower end of the graph. Sobers, Flintoff and Vettori have done worst at home. Flintoff is a surprise. To average above 35 in helpful conditions at home: maybe some opinions about his bowling prowess should be changed. This, despite a great 2005 Ashes outing.

Bowler analysis - Away matches - by opposing country

Summary of bowling averages against each team in away Tests
© Anantha Narayanan

McGrath has been the best travelling bowler. Ambrose has been equally good, although his career has been dominated by tours against Australia and England. Kumble and Harbhajan have been the worst performers away from home. Ntini also has not set any away grounds alight.

Now for the tables. Most of these are self-explanatory.

Test bowler summary: by location, innings and quality of Bowlers dismissed

Bowler	Ctry	Career	Career	Home		Away		1 Inns		2 Inns		TopOrd	MidOrd	LateOrd
		Wkts	Avge	Wkts~Avge								Wkts	Wkts	Wkts

Muralitharan	Slk	800	22.73	500~20.15	62.5%	300~27.02	37.5%	458~23.95	57.2%	342~21.09	42.8%	280	260	260
Warne S.K	Aus	708	25.42	366~25.55	51.7%	342~25.27	48.3%	349~28.05	49.3%	359~22.86	50.7%	225	220	263
Kumble A	Ind	619	29.65	383~24.9	61.9%	236~37.36	38.1%	339~32.17	54.8%	280~26.61	45.2%	237	181	201
McGrath G.D	Aus	563	21.64	316~21.97	56.1%	247~21.23	43.9%	329~21.95	58.4%	234~21.22	41.6%	282	139	142
Walsh C.A	Win	519	24.44	252~23.15	48.6%	267~25.66	51.4%	279~28.48	53.8%	240~19.75	46.2%	228	128	163
Kapil Dev N	Ind	434	29.65	225~26.82	51.8%	209~32.69	48.2%	299~31.2	68.9%	135~26.21	31.1%	214	97	123
Hadlee R.J	Nzl	431	22.30	215~22.23	49.9%	216~22.37	50.1%	289~22.77	67.1%	142~21.35	32.9%	189	113	129
Pollock S.M	Saf	421	23.12	247~21.35	58.7%	174~25.63	41.3%	255~23.05	60.6%	166~23.22	39.4%	186	111	124
Wasim Akram	Pak	414	23.62	173~23.13	41.8%	241~23.97	58.2%	242~25.71	58.5%	172~20.69	41.5%	163	106	145
Harbhajan	Ind	406	32.22	267~28.36	65.8%	139~39.65	34.2%	225~37.55	55.4%	181~25.61	44.6%	142	116	144
Ambrose	Win	405	20.99	211~21.54	52.1%	194~20.39	47.9%	243~21.8	60.0%	162~19.77	40.0%	192	101	112
Ntini M	Saf	390	28.83	261~24.43	66.9%	129~37.71	33.1%	245~29.06	62.8%	145~28.43	37.2%	190	108	92
Botham I.T	Eng	383	28.40	226~27.99	59.0%	157~29	41.0%	262~28.24	68.4%	121~28.75	31.6%	155	110	118
Marshall	Win	376	20.95	189~19.66	50.3%	187~22.25	49.7%	200~22.5	53.2%	176~19.18	46.8%	167	110	99
Waqar Younis	Pak	373	23.56	180~20.64	48.3%	193~26.28	51.7%	225~25.26	60.3%	148~20.98	39.7%	163	101	109
Imran Khan	Pak	362	22.81	181~19.34	50.0%	181~26.29	50.0%	227~22.91	62.7%	135~22.64	37.3%	167	91	104
Vettori D.L	Nzl	359	34.16	179~34.7	49.9%	180~33.63	50.1%	225~32.67	62.7%	134~36.68	37.3%	131	106	121
Lillee D.K	Aus	355	23.92	234~23.79	65.9%	121~24.18	34.1%	208~22.82	58.6%	147~25.48	41.4%	166	92	97
Vaas WPUJC	Slk	355	29.58	187~26.33	52.7%	168~33.2	47.3%	223~30.92	62.8%	132~27.31	37.2%	185	89	81
Donald A.A	Saf	330	22.25	192~21.35	58.2%	138~23.51	41.8%	200~24.02	60.6%	130~19.54	39.4%	164	80	86
Willis	Eng	325	25.20	180~23.66	55.4%	145~27.11	44.6%	205~26.25	63.1%	120~23.4	36.9%	161	82	82
Lee B	Aus	310	30.82	191~29.11	61.6%	119~33.55	38.4%	174~29.84	56.1%	136~32.07	43.9%	147	80	83
Gibbs L.R	Win	309	29.09	151~26.77	48.9%	158~31.31	51.1%	160~33.49	51.8%	149~24.36	48.2%	98	100	111
Trueman F.S	Eng	307	21.58	231~20.07	75.2%	76~26.16	24.8%	196~21.34	63.8%	111~22.01	36.2%	139	71	97
Underwood	Eng	297	25.84	154~24.38	51.9%	143~27.41	48.1%	152~29.46	51.2%	145~22.04	48.8%	124	103	70
McDermott	Aus	291	28.63	204~26.37	70.1%	87~33.94	29.9%	196~27.68	67.4%	95~30.6	32.6%	135	85	71
Zaheer Khan	Ind	288	31.78	102~35.39	35.4%	186~29.81	64.6%	188~32.15	65.3%	100~31.09	34.7%	148	83	56
Kallis J.H	Saf	276	32.45	163~30.65	59.1%	113~35.05	40.9%	140~38.36	50.7%	136~26.37	49.3%	104	93	79
Steyn D.W	Saf	272	23.19	168~22.17	61.8%	104~24.84	38.2%	160~22.5	58.8%	112~24.17	41.2%	118	65	88

This table includes all matches, including the one-off ICC Test. The Home/Away and First/Second innings columns are self-explanatory. The last three columns contain the top order, middle order and late order wickets captured by the bowler. The batting position is important since, irrespective of the batting average, top order wickets are important for both teams. In the uploaded table I also have the average Batting average of all wickets captured by the bowler. The higher the average Batting average, the higher the average quality of batsmen dismissed.

McGrath's consistency across locations and innings and his 50% top order wickets tally are amazing achievements. Hadlee matches McGrath in all but top order wickets which is still a respectable 44%. Ambrose is in between with a top order figure of 47%. Ignoring the freak 60% figures for two bowlers, Motz and Pathan, at the end of the table, the highest top-order capture is for Vaas, with 52%. The lowest amongst the top bowlers is for Warne with 32%. MacGill is still lower at 28%.

Vaas is also the lowest in the late-order wickets tally, with 23%. Zaheer Khan clocks in with 19% in the sub-300 wickets band of bowlers. Warne is the highest with 37%.

I will let the readers come out with their comments on the following three tables. The selection criterion is 100 wickets and the ordering is by wickets captured. Let me also say categorically that, as far as I am concerned, a Test wicket is a wicket and does not go down in value because it is of a lesser team's batsman. Just as we have accepted the runs, we should accept wickets. I will always bring in the example of Tendulkar's 100 against Bangladesh in 2010. It is one of his best five efforts. But for him India would have lost. So let us not bring in the bogey of cheaper wickets. In that case we have to discount many more wickets since many teams, at different times in history, have been poor and of lesser quality. And let me conclude by saying that if Warne did not get more Bangladeshi/Zimbabwe wickets, it was because Australia did not play enough matches there, for their own own valid reasons.

I will only talk about the first data row, which is a weighted average of the bowling average of the 160+ bowlers against the specific country. This is determined by the formula: Sum(Bowler wickets x Bowling average) / Sum(Bowler wickets).

Test bowler summary: All matches vs other teams

Bowler	Team	Career	Aus	Bng	Eng	Ind	Nzl	Pak	Saf	Slk	Win	Zim
All matches		28.03	30.60	19.38	27.51	28.26	24.25	29.41	28.18	29.02	28.88	22.74

Muralitharan	Slk	795~23	54~36	89~13	112~20	105~33	82~22	80~25	104~22		82~20	87~17
Warne S.K	Aus	702~26		11~27	195~23	43~47	103~24	90~20	130~24	59~26	65~30	6~23
Kumble A	Ind	619~30	111~30	15~17	92~31		50~26	81~32	84~32	74~31	74~30	38~23
McGrath G.D	Aus	560~22		5~25	157~21	51~19	57~25	80~22	57~27	37~22	110~19	6~15
Walsh C.A	Win	519~24	135~29		145~25	65~20	43~22	63~23	51~20	8~35		9~15
Kapil Dev N	Ind	434~30	79~25		85~37		25~35	99~30	8~37	45~27	89~25	4~34
Hadlee R.J	Nzl	431~22	130~21		97~25	65~23		51~28		37~13	51~22
Pollock S.M	Saf	421~23	40~37	9~15	91~24	52~20	43~22	45~21		48~22	70~23	23~15
Wasim Akram	Pak	414~24	50~26		57~31	45~29	60~17		13~30	63~21	79~21	47~21
Harbhajan	Ind	406~32	90~29	6~48	43~39		43~33	25~52	60~28	52~39	56~23	31~25
Ambrose	Win	405~21	128~21		164~19	15~38	13~21	42~28	21~19	14~14		8~12
Ntini M	Saf	390~29	58~35	35~16	70~34	36~29	46~25	41~24		35~30	63~28	6~46
Botham I.T	Eng	383~28	148~28			59~26	64~23	40~32		11~28	61~35
Marshall	Win	376~21	87~23		127~19	76~22	36~22	50~21
Waqar Younis	Pak	373~24	30~34	18~10	50~27	8~49	70~20		24~29	56~23	55~23	62~20
Imran Khan	Pak	362~23	64~25		47~25	94~24	31~28			46~15	80~21
Vettori D.L	Nzl	358~34	65~36	51~16	45~37	40~48		20~48	21~73	51~24	33~26	32~27
Lillee D.K	Aus	355~24			167~21	21~22	38~19	71~30		3~36	55~28
Vaas WPUJC	Slk	355~30	38~32	19~26	49~31	30~45	42~24	47~37	27~34		55~17	48~28
Donald A.A	Saf	330~22	53~31		86~23	57~17	21~21	27~22		29~19	43~21	14~16
Willis	Eng	325~25	128~26			62~23	60~19	34~24		3~23	38~36
Gibbs L.R	Win	309~29	103~31		100~29	63~23	11~57	32~24
Lee B	Aus	308~31		8~47	62~41	53~32	44~21	5~47	50~35	16~18	64~23	6~37
Trueman F.S	Eng	307~22	79~25			53~15	40~19	22~20	27~23		86~23
Underwood	Eng	297~26	105~26			62~27	48~12	36~24		8~12	38~44
McDermott	Aus	291~29			84~26	34~29	48~30	18~35	21~29	27~27	59~29
Zaheer Khan	Ind	288~32	61~36	31~24	39~27		35~26	17~47	33~34	28~39	23~30	21~27
Kallis J.H	Saf	275~32	48~38	17~14	46~35	18~43	24~37	23~38		26~33	52~30	21~15
Steyn D.W	Saf	272~23	45~26	22~17	31~34	53~19	45~19	19~31		22~27	35~19
Anderson	Eng	267~30	41~39	9~25		45~30	27~24	32~18	48~38	18~36	36~28	11~20

The across-bowlers-countries bowling average is around 28. The all-inclusive average is around 30. But remember that these are the top 160 bowlers. Australia has been the toughest team to bowl to, with a bowling average of over 30, even for these top bowlers. Pakistan is the next toughest at 29.4 and surprisingly Sri Lanka follows with 29.0. West Indies follows next with 28.8 and only then comes India. It is no surprise that Bangladesh is at the other end with an average below 20. Zimbabwe fares much better with 22.7.

Test bowler summary: Home matches vs other teams

Bowler	Team	Career	Aus	Bng	Eng	Ind	Nzl	Pak	Saf	Slk	Win	Zim
Home matches		25.25	26.83	16.56	25.88	25.49	22.80	25.76	28.27	25.29	25.28	18.71

Muralitharan	Slk	493~20	47~26	60~10	64~21	65~25	52~22	30~26	69~20		45~17	61~12
Kumble A	Ind	350~25	62~24		56~24		39~22	57~28	39~32	44~22	29~27	24~19
Warne S.K	Aus	313~27			66~26	9~63	54~27	45~22	69~24	22~32	48~27
McGrath G.D	Aus	286~23		5~25	70~23	18~14	27~33	47~21	28~31	31~20	60~18
Harbhajan	Ind	258~28	81~24		29~34		22~42	25~38	42~26	27~31	20~17	12~20
Ntini M	Saf	249~24	40~31	23~13	27~32	18~30	39~21	32~18		26~22	39~26	5~18
Pollock S.M	Saf	235~21	8~47	2~32	56~23	39~17	17~20	27~21		26~20	50~21	10~12
Lillee D.K	Aus	231~24			71~22	21~22	16~15	68~27			55~25
Walsh C.A	Win	229~24	63~22		58~27	22~24	11~25	30~24	29~20	7~31		9~15
Trueman F.S	Eng	229~20	50~24			53~15	21~23	22~20	27~23		56~19
Botham I.T	Eng	226~28	79~27			29~27	37~21	38~31		8~31	35~32
Kapil Dev N	Ind	219~26	28~27		42~35		10~23	55~22		29~23	54~26	1~41
Ambrose	Win	203~21	50~23		76~16	15~38	8~20	27~29	8~10	11~15		8~12
Hadlee R.J	Nzl	201~23	53~25		27~24	34~24		41~24		10~14	36~20
McDermott	Aus	193~26			54~22	34~26	32~31	11~27	14~18	13~30	35~33
Lee B	Aus	184~29		6~32	33~36	45~27	26~24		23~43	16~18	29~19	6~37
Vaas WPUJC	Slk	180~26	21~25	13~27	40~21	19~47	6~30	8~62	16~25		39~12	18~33
Donald A.A	Saf	177~22	24~34		41~22	40~18	11~18	20~19		17~19	23~17	1~83
Willis	Eng	176~24	56~21			30~24	32~16	25~23			33~34
Anderson	Eng	173~27	12~45	9~25		35~30	19~19	23~14	30~37	7~29	27~24	11~20
Abdul Qadir	Pak	168~27	33~27		61~20	21~46	16~26			9~31	28~27
Bedser A.V	Eng	167~22	57~24			44~13	7~31	10~16	38~24		11~34
Imran Khan	Pak	163~19	19~17			67~22	14~30			31~13	32~15
Vettori D.L	Nzl	159~37	29~32	17~23	19~46	9~61		19~42	14~78	21~20	25~26	6~36
Steyn D.W	Saf	159~22	27~26	8~25	23~34	27~18	36~17	4~22		14~22	20~19
Marshall	Win	157~20	42~22		33~21	40~20	27~18	15~19
Waqar Younis	Pak	156~21	10~26		5~23	6~40	36~12		4~31	31~26	23~21	41~19
Kallis J.H	Saf	155~31	30~34	12~15	11~57	11~45	14~30	15~32		19~31	25~29	18~12
Wasim Akram	Pak	154~22	14~30		4~56	18~31	10~16		6~19	28~23	44~16	30~18

For the home matches of the bowlers, the across-bowlers-countries bowling average falls, as expected, to around 25. This time there are changes. South Africa has been the toughest team to bowl to, for the bowlers bowling at home, with a bowling average of just over 28, even for these top bowlers. Australia follows next with 26.8 and again surprisingly England follows with 25.8. It is again no surprise that Bangladesh is at the other end, travelling very poorly with an average around 16.5. Zimbabwe fares better with 18.7.

Test bowler summary: Away matches vs other teams

Bowler	Team	Career	Aus	Bng	Eng	Ind	Nzl	Pak	Saf	Slk	Win	Zim
Away matches		27.69	31.20	19.21	26.47	26.65	24.75	30.24	26.72	29.05	29.02	25.52
Warne S.K	Aus	389~25		11~27	129~22	34~43	49~21	45~19	61~24	37~21	17~40	6~23
Muralitharan	Slk	302~28	7~107	29~19	48~19	40~45	30~20	50~25	35~26		37~23	26~28
Walsh C.A	Win	290~25	72~34		87~24	43~19	32~21	33~22	22~19	1~60
McGrath G.D	Aus	274~21			87~19	33~21	30~18	33~22	29~24	6~36	50~21	6~15
Kumble A	Ind	269~36	49~38	15~17	36~41		11~40	24~42	45~32	30~45	45~31	14~29
Wasim Akram	Pak	260~25	36~24		53~29	27~28	50~17		7~39	35~20	35~27	17~26
Hadlee R.J	Nzl	230~22	77~18		70~25	31~22		10~45		27~12	15~27
Marshall	Win	219~22	45~23		94~19	36~25	9~32	35~21
Waqar Younis	Pak	217~26	20~38	18~10	45~27	2~76	34~27		20~28	25~19	32~25	21~22
Kapil Dev N	Ind	215~33	51~25		43~39		15~42	44~40	8~37	16~33	35~23	3~31
Ambrose	Win	202~21	78~20		88~21		5~23	15~25	13~24	3~9
Imran Khan	Pak	199~26	45~29		47~25	27~28	17~27			15~18	48~25
Vettori D.L	Nzl	199~31	36~38	34~12	26~30	31~45		1~178	7~64	30~26	8~27	26~25
Zaheer Khan	Ind	191~30	25~35	31~24	31~28		24~23	11~41	23~33	18~40	15~38	13~22
Pollock S.M	Saf	186~26	32~34	7~11	35~25	13~27	26~23	18~23		22~25	20~29	13~18
Gibbs L.R	Win	183~29	59~33		62~26	39~23	8~45	15~26
Vaas WPUJC	Slk	175~33	17~42	6~24	9~78	11~41	36~23	39~32	11~47		16~28	30~25
D Kaneria	Pak	164~34	34~42	25~11	14~59	31~40	16~31		15~26	15~27	14~40
Holding M.A	Win	163~24	63~24		63~21	30~22	7~48
Botham I.T	Eng	157~30	69~28			30~26	27~27	2~45		3~22	26~40
Donald A.A	Saf	153~23	29~28		45~24	17~16	10~25	7~32		12~19	20~25	13~11
Underwood	Eng	152~27	50~31			54~27	24~14	11~38		8~12	5~63

Now for the travelling bowlers. The across-bowlers-countries bowling average is around 28, almost the same as playing at home. Even for these top bowlers, Australia has been the toughest team to bowl to, away, with a bowling average of over 31. Pakistan is the next toughest at 30.2 and surprisingly Sri Lanka follows with 29.0. West Indies follows next with 29.0 and only then come South Africa and India. Bangladesh is at the other end with an average below 20. Zimbabwe fares much better, at home, with 25.5.

To download/view the Excel sheet containing the following tables, please CLICK/RIGHT-CLICK HERE. The serious students of the game are going to have a link to this Excel file on their desktop and refer to it a few times a day.

Bowlers location summary and innings summaries.
Bowlers analysis vs Team - for all matches
Bowlers analysis vs Team - for home matches
Bowlers analysis vs Team - for away matches

No specific conclusions. I decided against coming out with any selection of bowlers. It will be a red herring.

Finally a request. Often we go off on a tangent and pursue areas which are completely off-track. In my anxiety to avoid rejecting comments (I might have rejected fewer than 100 comments over 4 years), I allow lot of freedom to readers. With greater freedom comes greater responsibility. Bring in discussion points only if they have some relevance to the topic of the article. Bring in First Class records only to support something specific. Do not bring in Lara or Tendulkar or Richards into a discussion of bowlers' performances other than for discussing specific areas. Do not highlight failures of great players just for the sake of doing it. Do not start with an agenda and take it to the nth level, just for the heck of it. If everyone wants to have the last word, there would not be a last word at all. YouTube videos are wonderful. I myself have had the pleasure of watching many a video of which I was not aware of. But use these only to enlighten readers and offer additional insights. Then the experience is enjoyable.

Henceforth I will neither publish a comment nor answer any queries related to it if I feel it is way out and is not going to steer the discussions in a positive manner. Alternately, if appropriate, I will publish after editing, making sure that the spirit behind the comment is retained.

Full post

A tale of two halves

Analysis of Test batting and bowling performances in each half of a player's career

Anantha Narayanan

25-Feb-2013

I had scheduled the Test Bowlers vs Countries/Innings article at this point. Then I realized that I could squeeze in this totally different one so that there is a welcome change of scene.

The mid-point of a Test career. A perfect symmetrical point to look back and forward. In real life, no single Test player would have known that he was at the mid-point. Barring (and stretching a lot) a two-Test player who knew with certainty that he was never going to be selected again at the end of the concerned Test. However, looking back, with the aid of the massive database, the mid-point is obvious, even for the currently active players. In this article I am going to separate the player careers into two equal halves and compare these. I hope that readers do not bring in the usual across-players comparisons in this analysis since all comparisons are within the player. Title courtesy Milind !!!

What is the expectation? In the first half of a player career, he is younger, fitter, (possibly) faster in his actions and does not have to conserve himself. However he is inexperienced, learning the trade and susceptible to selectorial whims. In the second half he is settled, might even set his own destiny, master of the trade, carries a great reputation behind him and knows his adversaries well. However he is also aging, has a non-syncing body and mind and has to compete with younger players. Which half player is more expected to deliver. The younger, fitter but inexperienced one or the wily, wiser but older player. Very difficult to generalise since so many other factors come into play. Let us see.

First, a few analysis criteria.

1. The half is determined exactly as it is defined as. For batsman, the mid-point is based on innings and for bowlers, innspells. So, for a qualifying all-rounder, the batting mid-point may be different to the bowling mid-point. This effectively takes care of the Imran Khan / Vettori type situation.
2. The overall criteria is 3000 runs for batsmen and 100 wickets for bowlers. 162 batsmen and 160 bowlers qualify. There is a nice symmetry about these numbers. For current players, it is obvious that the last Test they played could very well be their last Test ever. If I do this analysis couple of years hence, the numbers for the current batsmen would undergo a change.
3. In addition there is a special analysis of batsmen (mostly bowlers) who have played 40 or more innings and scored below 1000 runs.
4. For batsmen, both Runs and Batting average figures are analysed independently. There is expected to be a strong correlation between these two. A batsman who scores 5000 runs at 50 with a first half compilation of 2000 runs is more likely to score these at an average around 40 since there is a settling of the number of innings played over a number of matches.
5. For bowlers, both Wickets and Bowling average figures are analysed independently. There is expected to be less correlation between these two. A bowler who captures 200 wickets at 25 with a first half compilation of 120 wickets could capture these at 22 or 27. 22 is more likely though.

I have some other interesting points for analysis based on the career and these would come in a later article. Surprisingly there are quite a few outliers, some of them very well established players, to make this article very interesting and illuminating.

First an overall summary.

Batting

It is amazing and eerie. These values apply to the creme de la creme of batsmen, the 162 selected ones, who have scored 45% of the total runs scored. The average of average % of runs scored in the first half of career stands at 50.09%, almost perfect middle point. An alternate measure, which is the average of runs scored in the first half divided by the average of career runs, stands exactly at 50.0%. This is unbelievable. These batsmen have scored exactly 50% of the runs at mid-point. All career related variables have been cancelled out between the two halves. The average of the batting averages for the first half stands slightly lower at 98.9% and the average of batting averages for the second half stands at 101.4%.

Bowling

For the 160 bowlers there is a slightly different picture. The two averages of averages for the first half work out to 51.4% and 51.2% leading to a value of 51.3%. The second half is thus 48.7%. So there is a clear change between the first and second halves: maybe only 5% but a clear difference and evidence that the bowlers tend to drop off as their career progresses. The bowling averages drop off significantly: 103.1% to 97.6%, more of this caused by the spinners.

Readers might ask how this varies between pace bowlers and spinners. For the 97 pace bowlers the first half figure works out to 50.9% and the second half figure is 49.1%. For the 63 spinners the first half figure works out to 51.8% and the second half figure is 48.2%. This indicates that the pace bowlers tend to drop off less, by about 3.5% in the second half while the spinners tend to drop off by more, about 7%. Somewhat surprising, this difference is. One would have expected the spinners to be the steadier of the two classifications.

The bowling average variation is more pronounced. The spinners go from 105% to 95%, a very significant drop indeed. Again, quite surprisingly, the pace bowlers drop off very little, 101.4% to 99.1%. What could be the reason, I wonder. Fine, let the readers explain this.

Now for the graphs. These have been designed specifically for this analysis. There are four graphs. Batting: Runs and Batting average and Bowling: Wickets and Bowling average. Each graph shows the first and second half career figures of 10 players. The four on the left are the batsmen who have the highest first half values, the middle two have the perfect splits at the mid-point and the four on the right are the batsmen who have the highest second half values. Thus the graphs would be of great interest.

The first graph relates to Batting: Runs scored in each half.

Test runs scored in each half of career
© Anantha Narayanan

Look at how much of a change there has been in the careers of Adams, Harvey and Morris, the batsmen in the left half. All of them scored well over 60% of the runs in the first half of their careers and below 40% in the second halves. That is a drop of more than 50%. If these three had even a decent half, they would have finished with well over 50 average. Other than these three, only Grant Flower amongst batsmen has a 60-40 split.

Trescothick and Laxman are amazing. Their first half tally of runs has been within a cameo of the second half tally. Hobbs, Clarke, Border, Sehwag are others who are very close to a 50-50 split.

Now for the other side. Vettori has a 33.5-66.5 split. It is true: he has scored nearly twice in his second half as in the first half (2980 vs 1506). Imran Khan is the only other batsman with a 40-60 split. Samaraweera and de Villiers are two current batsmen who have scored around 50% more in their still-active second halves of their careers.

The second graph relates to Batting: Batting average in each half.

Test batting average in each half of career
© Anantha Narayanan

As expected the same four batsmen occupy the left side of the table, albeit in a different sequence. In general their averages show a huge drop, in excess of 150%. Adams has averaged only half his first innings average during the second half. The other three, well below 65%. The interesting point is that Adams' continued selection, with splits of 57-27, might very well have been because of the downhill trend West Indies were on. However in case of Harvey (61-37) and Morris (59-33), the heavy first half numbers could very well have influenced their continued selection.

The middle two show a change. Laxman's tally of runs might be similar but his average has shown a marked improvement. Currently the middle spots are occupied by two great batsmen of different eras: Trumper and Sobers. They average within a decimal point of the career averages during either halves of their careers. Haynes, Kanhai, Sehwag and Martyn are the others who have almost similar first and second half career averages.

The right half has the same four batsmen. They have averaged nearly twice in the second half. Vettori leads with 39.21 against 20.92, an amazing transformation indeed.

Let me now show the table, with no special comments, for the batsmen who have crossed 8000 runs and one honorary entry. My (totally worthless) digital autograph will be sent to the readers who correctly guess this wild card !!!

Batsman	Cty	Career		First	Half			Second	Half
		Runs	Avge	Runs	%	Avge	%	Runs	%	Avge	%

Tendulkar	Ind	15470	55.45	8032	51.9%	56.96	102.7%	7438	48.1%	53.90	97.2%
Ponting R.T	Aus	13346	52.75	6864	51.4%	55.80	105.8%	6478	48.5%	49.83	94.5%
Dravid R	Ind	13288	52.31	7203	54.2%	57.17	109.3%	6085	45.8%	47.54	90.9%
Kallis J.H	Saf	12379	56.78	5840	47.2%	54.07	95.2%	6539	52.8%	59.45	104.7%
Lara B.C	Win	11953	52.89	5759	48.2%	50.96	96.4%	6194	51.8%	54.81	103.6%
Border A.R	Aus	11174	50.56	5611	50.2%	51.01	100.9%	5563	49.8%	50.12	99.1%
Waugh S.R	Aus	10927	51.06	5256	48.1%	50.54	99.0%	5671	51.9%	51.55	101.0%
Jayawardene	Slk	10440	51.18	4893	46.9%	48.45	94.7%	5550	53.2%	53.88	105.3%
Chanderpaul	Win	10290	50.44	4555	44.3%	42.57	84.4%	5735	55.7%	59.12	117.2%
Gavaskar	Ind	10122	51.12	5647	55.8%	55.91	109.4%	4475	44.2%	46.13	90.2%
Sangakkara	Slk	9382	54.87	4130	44.0%	47.47	86.5%	5252	56.0%	62.52	114.0%
Gooch G.A	Eng	8900	42.58	3801	42.7%	36.55	85.8%	5099	57.3%	48.56	114.0%
J Miandad	Pak	8832	52.57	4519	51.2%	55.79	106.1%	4313	48.8%	49.57	94.3%
Inzamam	Pak	8830	49.61	3848	43.6%	43.24	87.2%	4982	56.4%	55.98	112.8%
Laxman	Ind	8781	45.97	4401	50.1%	44.01	95.7%	4380	49.9%	48.13	104.7%
Hayden M.L	Aus	8626	50.74	4811	55.8%	56.60	111.5%	3815	44.2%	44.88	88.5%
Richards	Win	8540	50.24	4807	56.3%	54.62	108.7%	3733	43.7%	45.52	90.6%
Stewart A.J	Eng	8465	39.56	4543	53.7%	41.30	104.4%	3922	46.3%	37.71	95.3%
Gower D.I	Eng	8231	44.25	4270	51.9%	45.43	102.7%	3961	48.1%	43.05	97.3%
Sehwag V	Ind	8178	50.80	4103	50.2%	50.65	99.7%	4075	49.8%	50.94	100.3%
Boycott G	Eng	8114	47.73	4123	50.8%	49.67	104.1%	3991	49.2%	45.87	96.1%
Smith G.C	Saf	8043	49.65	3970	49.4%	47.83	96.3%	4073	50.6%	51.56	103.8%
Sobers	Win	8032	57.78	4155	51.7%	57.71	99.9%	3877	48.3%	57.87	100.1%
Waugh M.E	Aus	8029	41.82	4294	53.5%	42.51	101.7%	3735	46.5%	41.04	98.1%
Bradman D.G	Aus	6996	99.94	3772	53.9%	99.26	99.3%	3224	46.1%	100.75	100.8%

Now for the bowlers.

The third graph relates to Bowling: Wickets captured in each half.

Test wickets in each half of career
© Anantha Narayanan

Rhodes' career is unbelievable. 94-33 split in the two halves of his career. The others are no less: Noble 84-37, Valentine 99-40 and Tate 100-55. The other two all-rounders are understandable. They turned from bowling all-rounders to batting ones. But how does one explain Valentine and Tate. Pure bowlers suddenly have a 45% drop in wickets. Prasanna and Shastri also have had similar huge drops.

Srinath and Caddick had perfectly matching career halves. Their splits are 118-118 and 117-117. Three other players have achieved this perfect split. These two were selected because of their 200+ career wickets. But Kumble's split is possibly more impressive. He has achieved 309-310, just a wicket separating the two halves in over 130 tests. That is some symmetry and consistency.

Two modern and two olden day bowlers have improved beyond all recognition in their careers. Intikhab Alam has a mind-boggling 39-86, almost the reverse of Rhodes/Noble. Blythe and Trumble have two contrasting halves. Yardley moved from 44 to 82. Davidson's split was 69-117. Zaheer Khan and Flintoff are close to this.

The fourth graph relates to Bowling: Bowling average in each half.

Test bowling average in each half of career
© Anantha Narayanan

Now for the bowling averages. Let us see if there is a difference unlike the batsmen graph which was almost a replica.

As expected we have different sets of bowlers who fill up the average table showing that there is less correlation between number of wickets and bowling averages. Briggs, Turner and Peel, the pre-WW1 bowlers have all had wonderful starts and have dropped off. Averages of around 10 moving to 30, 12 to 22 and 13 to 22. Boje has had a great first half and an equally poor second half. An average of 31 became 58. Botham, probably concentrating on his batting, dropped from 22.6 to 36.9.

Two current bowlers, Swann and Umar Gul have almost identiical first and second half career bowling averages. At this mid-stage in Swann's career he has proved his amazing consistency by having two almost perfect halves to his career. Same with Umar Gul. In addition, Ambrose and Gillespie had almost the same averages of 21 and 26 around their half-point mark.

At the other end, we have Intikhab Alam who moved dramatically from 51.9 to 28.7. Alec Bedser improved significantly from 33.4 to 18.7. Barnes improved from 22 to 13, leading to his phenomenal career average of 16.4. Similar change there for Blythe. Laker has had poor first half and then a phenomenal second half with a change from 27.9 to 15.9 (no doubt aided by 19 for 90). Currently Anderson has had two totally different halves with 35.7 and 25.6.

Now the table, with no special comments, for the bowlers who crossed 300 wickets in their Test career. This time the wild card is for SF Barnes.

Bowler	Cty	Type	Career		First	Half			Second	Half
					Wkts	%	Avge	%	Wkts	%	Avge	%

Muralitharan	Slk	rob	800	22.73	391	48.9	23.92	95.0	409	51.1	21.58	105.3
Warne S.K	Aus	rlb	708	25.42	332	46.9	25.48	99.8	376	53.1	25.36	100.2
Kumble A	Ind	rlb	619	29.65	309	49.9	27.69	107.1	310	50.1	31.61	93.8
McGrath G.D	Aus	RFM	563	21.64	298	52.9	21.76	99.4	265	47.1	21.51	100.6
Walsh C.A	Win	RF	519	24.44	222	42.8	26.23	93.2	297	57.2	23.10	105.8
Kapil Dev N	Ind	RFM	434	29.65	263	60.6	28.84	102.8	171	39.4	30.89	96.0
Hadlee R.J	Nzl	RFM	431	22.30	187	43.4	25.44	87.7	244	56.6	19.89	112.1
Pollock S.M	Saf	RFM	421	23.12	232	55.1	20.50	112.8	189	44.9	26.33	87.8
Wasim Akram	Pak	LFM	414	23.62	219	52.9	23.28	101.5	195	47.1	24.01	98.4
Harbhajan	Ind	rob	406	32.22	206	50.7	28.61	112.6	200	49.3	35.94	89.6
Ambrose	Win	RF	405	20.99	218	53.8	20.87	100.6	187	46.2	21.12	99.4
Ntini M	Saf	RF	390	28.83	181	46.4	30.07	95.9	209	53.6	27.75	103.9
Botham I.T	Eng	RFM	383	28.40	228	59.5	22.62	125.6	155	40.5	36.90	77.0
Marshall	Win	RF	376	20.95	197	52.4	21.59	97.0	179	47.6	20.23	103.6
Waqar Younis	Pak	RFM	373	23.56	222	59.5	21.49	109.6	151	40.5	26.61	88.5
Imran Khan	Pak	RF	362	22.81	176	48.6	25.69	88.8	186	51.4	20.09	113.5
Vettori D.L	Nzl	lsp	359	34.16	178	49.6	35.12	97.3	181	50.4	33.23	102.8
Lillee D.K	Aus	RF	355	23.92	181	51.0	23.60	101.4	174	49.0	24.26	98.6
Vaas WPUJC	Slk	LFM	355	29.58	192	54.1	28.84	102.6	163	45.9	30.45	97.1
Donald A.A	Saf	RF	330	22.25	174	52.7	23.34	95.3	156	47.3	21.04	105.8
Willis	Eng	RF	325	25.20	166	51.1	23.78	106.0	159	48.9	26.69	94.4
Lee B	Aus	RF	310	30.82	145	46.8	31.84	96.8	165	53.2	29.92	103.0
Gibbs L.R	Win	rob	309	29.09	176	57.0	24.11	120.7	133	43.0	35.68	81.5
Trueman F.S	Eng	RF	307	21.58	138	45.0	20.70	104.3	169	55.0	22.30	96.8
Barnes S.F	Eng	RFM	189	16.43	71	37.6	22.07	74.4	118	62.4	13.04	126.0

Now for a collection of late order batsmen who qualify to be talked about in this article. This table includes my favourite late order batsmen, Gillespie and the one-and-only Chris Martin. Why Chris Martin? Because Chris is someone special and rare. His innings come and go in a flash and if you blink, you could miss an entire innings. His arrival creates an expectation like no other batsman's. If you miss a straight drive of Tendulkar or a cover drive of Sangakkara or a six of Gayle, no problems. Before Shastri finishes saying "this is going to the wire" for the 167th time or "went like a tracer bullet" for the 353rd time, there would be another such shot. But if you miss the Chris Martin delivery, you have missed it forever. I have always maintained that he is the only batsman I will pay to watch.

Batsman    Career       I Half       II Half
Verity     669 @ 20.91  469 @ 31.27  200 @ 11.76
(I half: 45, 40, 55, 42, 60 and 66. II - 29)

Laker 766 @ 14.06 465 @ 17.88 211 @ 9.59 (As his bowling picked, his batting fell off) ... ... Gillespie 1221 @ 18.78 432 @ 13.09 789 @ 24.66 (588 @ 18.38 before his last innings!!!)

Full post

Test batting: location summary, by innings and vs country

An analysis of players' Test careers by innings number, opposition and host country

Anantha Narayanan

25-Feb-2013

Jacques Kallis has a better average in the second innings than in the first innings © Getty Images

This is a continuation of the two ODI articles and analyses how Test batsmen and bowlers performed at home or away, against different teams and in the first or second innings. Normally I do analysis-centric articles which take on and expound a theme. Once in a while I do different types of articles in which I go deep in one area of the game and provide data tables around it. This is one such article. This has been a tough exercise on presentation and I must thank Milind for his invaluable suggestions.

In order to avoid the usual questions and comments which relate to specific players, let me explain how these series of articles would be structured. I would cover the top/selected 10-12 players in a graph to visually present the variations. Then I would present data tables, in the body of the articles, which would normally cover the top 30 players or so. However the most important of the tables are the ones which have been uploaded and are available for downloading for permanent storage and perusal. Normally these cover the complete set of players, say 150 or so, who meet the cut-off criteria. So, before coming out with comments that "Miandad or Graham Gooch or Amarnath is not mentioned", please download the tables and check. Superficial reading of the articles is not enough.

1. The criteria is 3000 Test runs for the career analysis and 2000 runs for the other analyses. I know that Pollock and Headley will miss out. However I do not want to lower the target further since the vs Country numbers would be too low.
2. There is no problem with using the Batting Average since this is an analysis of Test matches. Not outs do not play that significant a part as happens in the ODI game.
3. There are problems with the single Australia-ICC Test match. It could be said that the ICC players played against Australia away. Fine. But what about Australia. Which country did they play against? And I am not certainly going to allocate part of the match runs/wickets only. So this match has been completely excluded from the analysis. So do not come out with a complaint if you see Muralitharan, in the next article, with 795 wickets and Hayden, in this analysis, short by over 100 runs.
4. There is no neutral location. Too few matches (probably a maximum of 20) have been played in the neutral locations for me to classify these. These are treated as "Away" for both, probably a very fair assignment.

There are some similarities between this and the previous article on Bradman. However that article had the individual innings as the basis while this analysis has, as basis, the runs scored in different locations, against other teams and different team innings. The objectives are quite different. There are different insights to be drawn. In these articles the unassailable fact is the superiority of Bradman, in figures. So all attempts have been made to highlight facts related to other batsmen. I request readers to try and maintain this. After all there are other Test batsmen than Bradman and Tendulkar.

First the graphs. I would only offer limited comments since I expect the readers to come out with their own comments. I might anyhow miss some obvious comment. Should not really matter. The ordering is different for different modes of presentation since we can get different insights. In general, the graphs are ordered by the concerned Batting Average values and the tables are ordered by the appropriate Runs scored values.

Batsmen analysis - Summary by location / innings

Summary of career performance
© Anantha Narayanan

This graph contains batsmen with the top 10 averages and Tendulkar and Lara. Kallis is the only modern batsman in the top-10. The visual presentations are quite clear and are also explained on the graphs. Bradman is Bradman. Let us stop there. Barrington's away batting average is significantly higher than his home figure. As is the case with Hammond. Walcott has been much better at home than away. Hutton is almost the same everywhere.

Understandably most batsmen have performed better in the first innings than the second innings. Only three batsmen, Bradman, Sutcliffe and Kallis have performed better in the second innings than the first. This should put Kallis in slightly different light.

Batsmen analysis - All matches - by opposing country

Summary of performance against each team
© Anantha Narayanan

This graph requires some explanation. These are ordered by the Batting Average values. The player's performance against the 10 team groups are plotted. Blue ovals indicate Batting Average values of over 50.0 and Red ovals indicate Batting Average values below 50.0. The number of innings and runs scored are displayed under each country. Both Tendulkar and Lara have a mixed bag of performances and have been sub-par against three teams each. Both have been just below par against New Zealand and South Africa.

Only Bradman and Hobbs have performed above par across all countries. Tendulkar has been below par against Pakistan and South Africa while Lara has not been so successful against India and New Zealand. Looking down the graph, West Indies has been the toughest team to bat against and India the easiest to bat against.

Batsmen analysis - Home matches - by opposing country

Summary of performance in home Tests
© Anantha Narayanan

Other than Bradman, Weekes and Walcott have been outstanding at home against all opposition. Look at how well Australian bowlers have performed against all countries, away.

Batsmen analysis - Away matches - by opposing country

Summary of performance in away Tests
© Anantha Narayanan

Barring West Indies, Barrington has been above par while visiting the other countries. Same as with Hammond. Hobbs has also done well while on road. Surprisingly England has been a good country to visit and not so surprisingly New Zealand the toughest.

Now for the tables. Most of these are self-explanatory.

Test batsmen summary: by location, innings and average bowling quality

				Career		Home		Away		1st Ins		2nd Ins		Avge	31.79	Adj Avge
Batsman	Team	Inns	Nos	Runs	Avge	Runs	Avge	Runs	Avge	Runs	Avge	Runs	Avge	BowQty	/ABQ

Tendulkar	Ind	311	32	15470	55.45	6765	56.38	8705	54.75	10924	62.07	4546	44.14	34.46	0.92	51.15
Ponting R.T	Aus	282	29	13346	52.75	7446	59.10	5796	46.37	9364	58.53	3982	42.82	34.82	0.91	48.16
Dravid R	Ind	286	32	13288	52.31	5598	51.36	7667	53.62	9105	59.12	4183	41.83	34.15	0.93	48.71
Kallis J.H	Saf	257	39	12379	56.78	6738	58.59	5558	54.49	7905	55.28	4474	59.65	35.34	0.90	51.08
Lara B.C	Win	232	6	11953	52.89	6217	58.65	5695	48.26	8249	63.95	3704	38.19	32.02	0.99	52.52
Border A.R	Aus	265	44	11174	50.56	5743	45.94	5431	56.57	6803	48.25	4371	54.64	32.79	0.97	49.02
Waugh S.R	Aus	260	46	10927	51.06	5710	47.58	5217	55.50	8558	60.70	2369	32.45	34.19	0.93	47.48
Jayawardene	Slk	217	13	10440	51.18	6646	63.90	3797	37.97	7699	60.62	2741	35.60	36.40	0.87	44.69
Chanderpaul	Win	243	39	10290	50.44	5444	59.17	4846	43.27	6746	56.22	3544	42.19	33.98	0.94	47.19
Gavaskar	Ind	214	16	10122	51.12	5067	50.17	5055	52.11	6159	50.90	3963	51.47	34.17	0.93	47.56
Sangakkara	Slk	183	12	9382	54.87	5186	59.61	4196	49.95	5781	56.13	3601	52.96	36.39	0.87	47.93
Gooch G.A	Eng	215	6	8900	42.58	5917	46.23	2983	36.83	5002	42.39	3898	42.84	30.54	1.04	44.33
J Miandad	Pak	189	21	8832	52.57	4481	61.38	4351	45.80	6504	56.56	2328	43.92	34.59	0.92	48.31
Inzamam	Pak	200	22	8830	49.61	3604	52.23	5225	48.83	5636	51.24	3194	46.97	34.29	0.93	45.99
Laxman	Ind	225	34	8781	45.97	3767	51.60	5014	42.49	5310	44.25	3471	48.89	33.54	0.95	43.57
Hayden M.L	Aus	184	14	8626	50.74	5023	57.08	3415	42.69	5153	50.03	3473	51.84	34.34	0.93	46.97
Richards	Win	182	12	8540	50.24	3136	49.78	5404	50.50	6045	50.80	2495	48.92	32.66	0.97	48.89
Stewart A.J	Eng	235	21	8465	39.56	4652	40.81	3813	38.13	5003	39.71	3462	39.34	30.45	1.04	41.29
Gower D.I	Eng	204	18	8231	44.25	4454	42.83	3777	46.06	5311	46.59	2920	40.56	32.22	0.99	43.66
Sehwag V	Ind	167	6	8178	50.80	4248	58.19	3847	44.73	6170	64.95	2008	30.42	34.49	0.92	46.82
Boycott G	Eng	193	23	8114	47.73	4356	48.40	3758	46.98	4795	45.67	3319	51.06	33.57	0.95	45.20
Smith G.C	Saf	174	12	8043	49.65	3572	44.65	4459	55.74	4872	50.23	3171	48.78	36.47	0.87	43.27
Sobers	Win	160	21	8032	57.78	4075	66.80	3957	50.73	5109	59.41	2923	55.15	32.07	0.99	57.27
Waugh M.E	Aus	209	17	8029	41.82	4019	43.22	4010	40.51	5568	44.90	2461	36.19	33.12	0.96	40.14
Atherton	Eng	212	7	7728	37.70	4716	38.98	3012	35.86	4458	39.45	3270	35.54	30.12	1.06	39.79
Langer J.L	Aus	182	12	7696	45.27	4406	49.51	3268	41.37	5176	50.75	2520	37.06	34.02	0.93	42.31
Cowdrey M.C	Eng	188	15	7624	44.07	3537	43.13	4087	44.91	5250	47.30	2374	38.29	33.23	0.96	42.17
Greenidge	Win	185	16	7558	44.72	3209	48.62	4349	42.22	4635	43.32	2923	47.15	32.68	0.97	43.50
Mohd Yousuf	Pak	156	12	7530	52.29	2965	63.09	4565	47.06	5043	60.04	2487	41.45	35.15	0.90	47.30
Taylor M.A	Aus	186	13	7525	43.50	3993	43.40	3532	43.60	4384	43.41	3141	43.62	34.36	0.93	40.25
Lloyd C.H	Win	175	14	7515	46.68	2881	46.47	4634	46.81	5191	49.91	2324	40.77	32.23	0.99	46.04
Haynes D.L	Win	202	25	7487	42.30	3868	56.06	3619	33.51	4457	38.76	3030	48.87	33.53	0.95	40.10
Boon D.C	Aus	190	20	7422	43.66	4541	46.34	2881	40.01	4491	43.60	2931	43.75	34.20	0.93	40.59
Kirsten G	Saf	176	15	7289	45.27	3384	42.30	3905	48.21	4620	47.14	2669	42.37	33.86	0.94	42.50
Hammond W.R	Eng	140	16	7249	58.46	3004	50.07	4245	66.33	5070	64.18	2179	48.42	43.86	0.72	42.37
Ganguly S.C	Ind	188	17	7212	42.18	3180	42.97	4032	41.57	4769	43.75	2443	39.40	34.05	0.93	39.37
Fleming S.P	Nzl	189	10	7172	40.07	2947	33.87	4225	45.92	4861	46.30	2311	31.23	32.76	0.97	38.88
Chappell	Aus	151	19	7110	53.86	4515	54.40	2595	52.96	4791	58.43	2319	46.38	32.29	0.98	53.04
Bradman D.G	Aus	80	10	6996	99.94	4322	98.23	2674	102.85	4697	97.85	2299	104.50	35.95	0.88	88.38

The Home/Away and First/Second innings columns are self-explanatory. The last three columns are interesting. I have first posted the Average Bowling Quality, which is the Career-to-date bowling average faced by the batsman weighted by the runs scored. To counter the single bowler anomalies, the reciprocal method is used. An excellent bowling attack off which a 100 is scored will get a higher weight than the same attack off which 10 runs are scored. Thus this is a true depiction of the quality of bowling faced by the batsmen through their career and how they handled the attacks.

This work is an off-shoot of a comment for the previous article. Basically I have adjusted the batsman average by a factor which is 31.79 / ABQ. What is 31.79. That is the single bowling average value across 135+ years and 2000+ Tests. Bradman's ABQ being a below-par 35.95, his average gets reduced from 99.94 to 88.38. Gooch, having faced an above-par bowling attack of 30.54, has his average increased from 42.58 to 44.32. This seems to be an excellent adjustment tool.

Test summary: All matches vs other teams

Batsman	Team	Runs	Avge	Ins	Aus		Bng		Eng		Ind		Nzl		Pak		Saf		Slk		Win		Zim
All matches					Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins

Tendulkar	Ind	15470	55.45	311	57.3	67	137	9	56.3	47			49.4	36	42.3	27	42.5	45	60.5	36	55.2	30	76.5	14
Dravid R	Ind	13265	52.64	284	39.7	60	70	10	60.9	37			63.8	28	53.7	26	33.8	40	48.6	32	63.8	38	97.9	13
Ponting R.T	Aus	13242	52.76	280			65	5	44.2	58	54.4	51	53.6	26	66.8	26	52.5	43	46.4	23	53.4	44	96.7	4
Kallis J.H	Saf	12296	56.66	255	38.1	47	79.2	7	42.7	46	72	28	64.1	26	66.9	26			38.9	25	73.6	43	170	7
Lara B.C	Win	11912	53.18	230	52.1	56	86.5	2	62.1	51	34.6	29	41.4	17	53.3	22	49	35	86.5	14			55.5	4
Border A.R	Aus	11174	50.56	265					56.3	82	52.2	35	51.7	32	59.5	36	33.1	10	54.3	11	39.5	59
Waugh S.R	Aus	10927	51.06	260					58.2	73	41.9	31	38.5	34	34.6	30	49.9	25	87.6	11	49.8	51	145	3
Jayawardene	Slk	10443	51.19	217	34.9	23	66.4	14	59.9	37	67.5	28	51.6	19	32	40	59.4	28			44	18	60	10
Chanderpaul	Win	10290	50.44	243	50	38	54.6	8	52.4	54	65.7	40	42.9	20	42.9	26	50.6	36	42	12			28.8	9
Gavaskar	Ind	10122	51.12	214	51.7	31			38.2	67			43.4	16	56.5	41			66.7	11	65.5	48
Sangakkara	Slk	9382	54.87	183	42.7	17	73	14	35	36	57.1	24	59.2	14	79.6	25	48.6	28			54	19	89.3	6
Gooch G.A	Eng	8900	42.58	215	33.3	79					55.6	33	52.2	24	42.7	16	23.2	6	62.7	6	44.8	51
J Miandad	Pak	8832	52.57	189	47.3	40			51.1	32	67.5	39	80	29					41.6	16	29.8	28	28.6	5
Inzamam	Pak	8829	50.16	198	34.1	25	80.8	8	54.6	32	52.1	17	66.2	19			32.3	23	60	31	53.5	24	42.9	19
Laxman	Ind	8781	45.97	225	49.7	54	39	4	30.6	28			58.4	17	43.1	25	37.5	31	47.4	22	57.2	36	40	8
Richards	Win	8540	50.24	182	44.4	54			62.4	50	50.7	41	43	10	42	27
Stewart A.J	Eng	8465	39.56	235	30.7	65					40.6	15	45.9	26	52.3	22	39.2	39	41.2	16	36.9	43	69	9
Hayden M.L	Aus	8438	50.23	182			33.6	5	45.7	35	59	35	36.6	18	46.8	10	43.7	36	51.1	13	51.5	27	250	3
Gower D.I	Eng	8231	44.25	204	44.8	77					44.9	37	50	22	49.4	27			93	3	32.8	38
Boycott G	Eng	8114	47.73	193	47.5	71					57.1	22	38.2	25	84.4	10	37.3	12			45.9	53
Sehwag V	Ind	8095	50.91	165	43.9	40	35.2	6	27	22			44.4	18	91.1	14	50.2	26	72.9	18	52.2	17	58.7	4
Sobers	Win	8032	57.78	160	43.1	38			60.6	61	83.5	30	23.8	18	89.5	13
Smith G.C	Saf	8031	50.19	172	38.6	27	82.6	9	57.4	34	34.9	23	44	20	44.7	20			35	12	69.3	25	81	2
Waugh M.E	Aus	8029	41.82	209					50.1	51	33.2	24	42.6	20	42.4	22	42	29	24.6	14	41.3	48	90	1
Atherton	Eng	7728	37.70	212	29.7	66					57.4	13	68	17	41.4	19	43.8	32	18	8	31.7	50	37	7
Langer J.L	Aus	7674	45.68	180			36	2	50.2	38	40.3	26	62.9	23	57	20	42.7	20	35.9	14	37.9	33	20	4
Cowdrey M.C	Eng	7624	44.07	188	34.3	75					72.6	11	59.6	24	45.2	15	39.3	27			51.5	36
Greenidge	Win	7558	44.72	185	40.4	52			50.4	48	47.9	39	55.1	19	31.9	27
Mohd Yousuf	Pak	7530	52.29	156	29.6	21	252	6	62.5	24	49.9	27	53.4	15			29.8	13	29	26	101	14	68.4	10
Taylor M.A	Aus	7525	43.50	186					42.3	61	42.2	18	47.6	16	79.2	20	41.4	19	43.6	15	28.1	37
Lloyd C.H	Win	7515	46.68	175	50.2	48			45.1	51	58.6	44	16.7	14	37.9	18
Haynes D.L	Win	7487	42.30	202	42.1	59			47.8	59	34.1	32	49.6	20	37.1	29	40.5	2	20	1
Boon D.C	Aus	7422	43.66	190					45.7	57	70.8	20	47.5	27	23.9	20	43.3	11	32.9	15	39.9	40
Kirsten G	Saf	7289	45.27	176	34.4	34	155	2	48.7	35	40	19	50.1	23	55.9	18			42.6	16	34.5	24	82.5	5
Hammond W.R	Eng	7249	58.46	140	51.9	58					79.3	9	113	11			62.5	42			35.5	20
Ganguly S.C	Ind	7212	42.18	188	35.1	44	61.8	6	57.8	19			46.9	15	47.5	20	33.8	31	46.3	24	32.1	16	44.2	13
Fleming S.P	Nzl	7172	40.07	189	25.2	27	66.2	6	35.1	37	32.6	20			47.5	16	41.2	27	58.3	23	46.9	16	37.6	17
Chappell	Aus	7110	53.86	151					45.9	65	73.6	5	56.6	22	63.2	27			66	1	56	31
Bradman D.G	Aus	6996	99.94	80					89.8	63	179	6					202	5			74.5	6

I have resolved not to mention the dreaded B word once in this paragraph. Coming down to earth, the averages which stand out, after ensuring that sufficient innings are played are: Sutcliffe 46 @ 66.9 and Barrington 39 @ 64.0 against Australia. Richards 50 @ 62.4 and Lara 51 @ 62.1 against England. Zaheer Abbas 25 @ 87.0 and Sobers 30 @ 83.5 against India. Javed Miandad 29 @ 80.0 against New Zealand. Sangakkara 25 @ 79.6 and Taylor 20 @ 79.2 against Pakistan. Harvey 23 @ 89.2 against South Africa. Tendulkar 36 @ 60.5 against Sri Lanka. Kallis 43 @ 73.6 and Gavaskar 48 @ 65.5 against West Indies. I am certain I have missed out some gems.

Test summary: Home matches vs other teams

Batsman	Team	Runs	Avge	Ins	Aus		Bng		Eng		Ind		Nzl		Pak		Saf		Slk		Win		Zim
Home matches					Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins

Ponting R.T	Aus	7446	59.10	147			34.5	2	44.3	28	86	26	51	18	69.9	16	58.2	22	50	9	45.4	23	130	3
Tendulkar	Ind	6765	56.38	135	62.7	29			60.6	17			49.3	18	44.2	14	36.2	17	52.5	17	61.7	16	113	7
Kallis J.H	Saf	6738	58.59	134	33.6	26	127	4	53.9	26	91.7	13	68.8	13	47.2	13			41.1	15	94.3	21	58.7	3
Jayawardene	Slk	6646	63.90	112	35.3	15	79.4	7	89	18	70.2	18	66.7	12	30.7	15	105	12			45.4	11	55.7	4
Lara B.C	Win	6217	58.65	111	66	23	86.5	2	78	24	35	23	49.7	6	60.8	9	51.4	17	69.8	7
Gooch G.A	Eng	5917	46.23	131	33.5	46					66.7	17	58.1	19	45.8	10	23.2	6	80.8	4	47.6	29
Border A.R	Aus	5743	45.94	145					47.3	39	53.4	19	52.8	18	57.7	20	29.2	5	63.2	4	33.9	40
Waugh S.R	Aus	5710	47.58	140					47.5	41	37.9	16	42	17	25.2	13	49.5	11	130	8	39.1	30	69.5	2
Dravid R	Ind	5598	51.36	120	35.7	30			47.8	14			63.8	14	42.9	17	39.2	18	76.9	11	58.4	10	126	6
Chanderpaul	Win	5444	59.17	114	80.4	17	108	2	40	27	70.3	29	46.8	6	65.9	11	63.1	15	43.3	4			24.3	3
Sangakkara	Slk	5186	59.61	95	30.5	11	118	7	39.2	18	74.3	14	52.8	7	68.8	10	65.8	12			68	12	63.8	4
Gavaskar	Ind	5067	50.17	108	52.5	12			36	39			43.2	6	54.4	22			104	5	61.1	24
Hayden M.L	Aus	5023	57.08	96			30.5	2	56.8	17	71.8	13	41.9	11	32	6	53.9	18	54.4	7	47.7	19	250	3
Atherton	Eng	4716	38.98	124	29.8	38					64.1	11	58.7	13	31.2	13	46.7	16			29.3	30	75	3
Stewart A.J	Eng	4652	40.81	126	30	33					52.9	9	35	17	63.9	16	41.6	23	58.8	8	26.9	15	60.5	5
Boon D.C	Aus	4541	46.34	108					42.9	29	73.2	15	58.3	16	18.3	9	31.2	5	36.9	9	46.3	25
Chappell	Aus	4515	54.40	96					50	37	73.6	5	36.2	9	60	22					58.8	23
J Miandad	Pak	4481	61.38	86	69.9	12			70	8	91.4	18	82.6	15					51.9	12	26.8	16	28.6	5
Gower D.I	Eng	4454	42.83	113	45.2	32					52.1	20	57.8	18	36.8	22			55	1	22.4	20
Langer J.L	Aus	4406	49.51	94			36	2	48.4	27	50.6	13	87.9	8	72.1	10	54.9	10	52.5	4	25.8	17	12	3
Boycott G	Eng	4356	48.40	100	50	34					64.3	12	46.1	17	87.3	5	18.8	4			41	28
Bradman D.G	Aus	4322	98.23	50					78.5	33	179	6					202	5			74.5	6
Sehwag V	Ind	4248	58.19	76	40.2	20			26.4	12			71.9	9	90.7	6	84	11	78.1	7	53.1	10	74	1
Jayasuriya	Slk	4114	43.77	102	31.1	14	75.6	5	30.6	14	94.8	10	31.5	8	42.6	14	45	15			27.9	11	43.3	11
Sobers	Win	4075	66.80	75	38.9	18			73.4	24	72.9	17	36.1	8	137	8
Waugh M.E	Aus	4019	43.22	99					50.8	23	22	11	45.9	12	45	9	40.3	14	63.8	4	42	26

I will let the readers come out with real gems from this table.

Test summary: Away matches vs other teams

Batsman	Team	Runs	Avge	Ins	Aus		Bng		Eng		Ind		Nzl		Pak		Saf		Slk		Win		Zim
Away matches					Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins	Avge	Ins

Tendulkar	Ind	8705	54.75	176	53.2	38	137	9	54.3	30			49.5	18	40.2	13	46.4	28	67.9	19	47.7	14	40	7
Dravid R	Ind	7667	53.62	164	44	30	70	10	68.8	23			63.8	14	78.6	9	29.7	22	33.1	21	65.7	28	79.2	7
Ponting R.T	Aus	5796	46.37	133			95.5	3	44.1	30	26.5	25	59.7	8	62.1	10	46.8	21	44.2	14	61.1	21	31	1
Lara B.C	Win	5695	48.26	119	43.3	33			48.8	27	33	6	36.9	11	48.2	13	46.7	18	101	7			55.5	4
Kallis J.H	Saf	5558	54.49	121	43.8	21	31.5	3	29.3	20	58.5	15	59	13	90.5	13			35.3	10	55.4	22	503	4
Border A.R	Aus	5431	56.57	120					65.1	43	51.1	16	50.2	14	61.9	16	38	5	48.3	7	53.1	19
Richards	Win	5404	50.50	115	47.6	39			64.3	34	45.4	24	19.2	4	42.8	14
Inzamam	Pak	5225	48.83	120	35.2	14	89	3	42.5	22	54.9	10	59.6	15			31.8	15	80.9	13	48.9	16	49	12
Waugh S.R	Aus	5217	55.50	120					74.2	32	47.4	15	35.3	17	42.1	17	50.2	14	17.3	3	68.5	21
Gavaskar	Ind	5055	52.11	106	51.1	19			41.1	28			43.6	10	58.9	19			37.2	6	70.2	24
Laxman	Ind	5014	42.49	134	44.1	29	39	4	34.5	19			40.2	9	37.4	9	40.4	18	48.2	13	47.8	27	41.5	6
Chanderpaul	Win	4846	43.27	129	30.2	21	41.2	6	66.6	27	54.6	11	41.2	14	30.6	15	42.1	21	41.3	8			31	6
Lloyd C.H	Win	4634	46.81	108	49	36			42.1	30	75.5	22	15.3	11	33.8	9
Mohd You	Pak	4565	47.06	105	31.9	18	378	3	54.3	15	33.7	17	55.2	14			26.1	10	32.5	12	78.4	9	58.1	7
Younis Khan	Pak	4500	50.00	100	31.8	12	128	5	47.4	14	76.8	12	65.3	8			40.4	14	42.6	17	40	13	50.5	5
Smith G.C	Saf	4459	55.74	86	43.7	12	67	5	72.2	17	35.9	12	57.2	12	45.6	11			44.8	4	73	13
J Miandad	Pak	4351	45.80	103	38.1	28			46.6	24	49.9	21	77.3	14					15.8	4	33.8	12
Greenidge	Win	4349	42.22	110	31	32			56.1	30	45.3	25	56.2	12	17.3	11
Hammond W.R	Eng	4245	66.33	72	61.9	35							321	3			62.9	26			25	8
Fleming S.P	Nzl	4225	45.92	100	29.3	15	116	2	37.9	19	35.7	11			50	6	46.3	20	105	10	41.9	8	39	9
Sangakkara	Slk	4196	49.95	88	65.2	6	40.6	7	30.6	18	36.5	10	66.8	7	86.5	15	35.8	16			34	7	140	2
Cowdrey M.C	Eng	4087	44.91	100	36	48					103	4	55.9	16	33.2	4	33.1	10			60.3	18
Ganguly S.C	Ind	4032	41.57	104	34.8	20	61.8	6	65.4	15			27.7	8	49.3	3	36.1	16	36.8	17	40	12	30.6	7
Waugh M.E	Aus	4010	40.51	110					49.5	28	43.5	13	35.8	8	40.6	13	43.6	15	9	10	40.5	22	90	1

Let us set aside Hammond's average of 321.0 against New Zealand and Mohd Yousuf's 378.0 against Bangladesh (albeit in 3 innings each). The stand-out averages are: Hammond 35 @ 61.9 and Tendulkar 38 @ 53.2 against Australia. Steve Waugh 32 @ 74.2 and Dravid 23 @ 68.8 against England. Lloyd 22 @ 75.5 and Sobers 13 @ 99.9 against India. Kallis 13 @ 90.5 and Sangakkara 15 @ 86.5 against Pakistan. Inzamam 13 @ 80.9 and Fleming 10 @ 104.7 against Sri Lanka. Finally Gavaskar 24 @ 70.2 and Steve Waugh 21 @ 68.5 against West Indies. Again this is probably not a final list.

But for me the most inexplicable and impossible-to-understand performance is Sobers' 10 innings in New Zealand at 15.1. His scores during 3 tours are 27, 25, 27, 1, 1, 11, 0, 20, 39 and 0. What really happened ???

To download/view the Excel sheet containing the following tables, please click/right-click here. The serious students of the game are going to have a link to this Excel file on their desktop and refer to it a few times a day.

Batsman location summary and innings summaries.
Batsmen run analysis vs Team - for all matches
Batsmen run analysis vs Team - for home matches
Batsmen run analysis vs Team - for away matches

No specific conclusions. I thought for long and decided against coming out with any selection of batsmen. It will be a red herring.

Full post

What made Bradman click?

A statistical analysis dissecting the extraordinary Test batting career of Don Bradman

Anantha Narayanan

25-Feb-2013

A very loaded question. This cannot be answered by subjective inferences.

He scored heavily: Others also did.
He started well: Not at all, he was not a great starter.
His hundreds were big: So what, many other also had big 100s.
He had a great defence: Quite a few other batsmen probably had better defence.
His temperament was great: Maybe, but Hobbs and Gavaskar et al also had excellent temperament.
He scored fast: Yes, possibly, but quite a few others scored faster.

So where did he get his near-100 average, around 65% higher than the next best. I will try to answer this question in objective terms.

First the criteria for selection of the list of batsmen. I do not want to lower the level to 2000 Test runs. This is a career of around 25 Tests and is too short for a study like this. I know that I will lose Pollock and Headley when I go above this point, but they played too few Tests to be of any real help in this analysis. At the end of 20 Tests, Hussey averaged 80 and Trott, 65. So this is too low a level. After a lot of deliberation I have fixed 4000 Test runs, approximately a Test career of 50 Tests, as the cut-off. This gives me 112 batsmen, a very decent selection.

First let me look at areas in which Bradman was decidedly human and failed more than many other batsmen. I will demonstrate this through a special graph as well as a table. The graph is more a player positioning chart than a routine graph. There are 3 related charts in each graph. In general the batsmen shown in relative positions are Bradman, top three and bottom two batsmen. The mean is also shown. There is no need to anything other than the normal Arithmetic Mean.

Graph of zeroes and single-digit dismissals
© Anantha Narayanan

Bradman had seven zeroes out of 80 innings, which works to 8.8%. He was worse than most batsmen in the selected group. Out of 112 batsmen, he was 97th. A very average performer indeed. The best time to get Bradman was before he scored: and this happened once in 11 innings.

Why zero? A 1 or 3 or 8 is also a failure. So I expand the definition of a real failure to a single digit dismissal. Very few will argue with this. There may be rare instances of single digit dismissals which are good innings but let us ignore those. Bradman had 14 such instances, more than once in six attempts. His average was a very poor 2.2. So low that he is ranked LAST in the list of 112 batsmen. Yes, last, I repeat. This average does not make much of a difference in the overall context. However that means the string of Bradman's low scores was really low. Ah! we have located another Achilles heel. Get that shorty before he reached 10. You probably have won the match.

But the numbers do not tell the complete story. The batsmen in our selected set have played between 80 and 311 innings. So what we need is a % of the total innings played. This is the correct method of measuring success or failure. Bradman has failed in 17.5% of his career innings. But now we begin to see some daylight. Bradman is slowly moving up. There are others better than him, but he is placed in the 6th position. Pretty good. This also gives us a clue to what we are looking for. The specific averages seem comparable but the frequency of failures or successes may provide the clue. Anyhow lot more work needs to be done. The related table is given below.

			Zeros			Sub-10
Batsman	Cty	Inns	Num	%	Avge	Num	%

Hobbs J.B	Eng	102	4	3.9%	3.2	13	12.7%
Sutcliffe H	Eng	84	2	2.4%	4.4	12	14.3%
Hammond W.R	Eng	140	4	2.9%	3.7	24	17.1%
Hutton L	Eng	138	5	3.6%	3.6	24	17.4%
Kanhai R.B	Win	137	7	5.1%	3.4	24	17.5%
Bradman D.G	Aus	80	7	8.8%	2.2	14	17.5%
Dexter E.R	Eng	102	6	5.9%	2.7	19	18.6%
Sangakkara K.C	Slk	183	7	3.8%	3.5	35	19.1%
Sobers G.St.A	Win	160	12	7.5%	3.2	31	19.4%
Graveney T.W	Eng	123	8	6.5%	2.8	24	19.5%
Hayden M.L	Aus	184	14	7.6%	3.1	36	19.6%
Barrington K.F	Eng	131	5	3.8%	3.6	26	19.8%
Lloyd C.H	Win	175	4	2.3%	4.4	35	20.0%
Javed Miandad	Pak	189	6	3.2%	3.9	38	20.1%
Katich S.M	Aus	99	4	4.0%	3.0	20	20.2%
Wright J.G	Nzl	148	7	4.7%	3.2	30	20.3%
Compton D.C.S	Eng	131	10	7.6%	2.6	27	20.6%
Smith G.C	Saf	174	10	5.7%	3.4	36	20.7%
Kallis J.H	Saf	257	14	5.4%	3.6	54	21.0%
Gower D.I	Eng	204	7	3.4%	4.6	43	21.1%

I am going to move forward a little to my next group of graph/table. Without in any way conferring any additional credit for crossing these landmarks, I will use 100 and 150 merely as convenient cut-off points.

Graph of 100-plus and 150-plus scores
© Anantha Narayanan

Let us first look at the 100-plus scores. The additional cut-off is that there should have been ten such scores. The average of these scores for Bradman is an impressive 186.0. Quite impressive indeed, until you look at the others following close behind. Zaheer Abbas is quite close behind with an average of nearly 180. And then Sehwag and Lara follow closely and are within 10-12 runs of Bradman's average. So Bradman is leading here, but nowhere with a dominating lead.

Now we will take the innings in which the batsmen crossed 150. This time the cut-off is 5 such scores. The average of these scores for Bradman is an impressive 225.8. Very impressive, until we see that Chris Gayle (why isn't he in whites, walking in with Barath/Powell ?), who averages even higher. He clocks in at 231.8. Then Jayasuriya is closely behind at 225.0. So Bradman is not even the leader in this sub-analysis.

Now let us look at this from another angle. Let us look at the frequency of such occurrences. Again, there is clear daylight. Bradman has crossed the 100 mark on 36.8% of the occasions, nearly double that of the next batsman. He has scored 150+ in an astonishing 22.5% of the batting forays. Next placed is Weekes, with 8.6%. Now we are beginning to get a handle on what made Bradman click. His frequency of crossing high landmarks was well over double of the others. Once he reached there, he and the others performed at par.

				100s			150s
Batsman	Cty	Inns	Avge	Num	%	Avge	Num	%

Bradman D.G	Aus	80	186.0	29	36.2%	225.9	18	22.5%
Zaheer Abbas	Pak	124	179.8	12	9.7%	207.8	8	6.5%
Sehwag V	Ind	167	176.3	22	13.2%	209.5	14	8.4%
Fleming S.P	Nzl	189	176.1	9	4.8%	220.8	5	2.6%
Lara B.C	Win	232	173.2	34	14.7%	214.0	19	8.2%
Jayasuriya S.T	Slk	188	168.3	14	7.4%	225.0	6	3.2%
Hammond W.R	Eng	140	167.5	22	15.7%	222.7	10	7.1%
Gayle C.H	Win	159	166.8	13	8.2%	231.8	6	3.8%
Simpson R.B	Aus	111	164.6	10	9.0%	203.2	6	5.4%
Sangakkara K.C	Slk	183	163.3	28	15.3%	204.5	14	7.7%
Atapattu M.S	Slk	156	161.5	16	10.3%	206.5	8	5.1%
Jayawardene D.P	Slk	217	159.4	31	14.3%	210.4	14	6.5%
Gibbs H.H	Saf	154	159.0	14	9.1%	192.9	7	4.5%
Dexter E.R	Eng	102	157.3	9	8.8%	180.6	5	4.9%
Hutton L	Eng	138	156.1	19	13.8%	197.1	10	7.2%
Javed Miandad	Pak	189	155.8	23	12.2%	206.1	10	5.3%
Younis Khan	Pak	133	155.5	20	15.0%	214.1	7	5.3%
Gooch G.A	Eng	215	152.1	20	9.3%	194.6	8	3.7%
de Villiers A.B	Saf	125	151.5	13	10.4%	195.0	6	4.8%
Sobers G.St.A	Win	160	150.7	26	16.2%	189.8	11	6.9%

For the third set of table/graph, which is going to be the defining one, I am going to work on 50 as a cut-off point. In fact I am going to take 50 runs as a point at which the batsman can be said to have done well. 50 is anyhow the minimum average we expect in top batsmen. And since we are doing a comparison exercise, the figure of 50 will remain the same for all batsmen, Bradman to Healy.

And let me say this. If anyone comes out with a comment that there have been great 40s and awful 60s, the comment will not see the light of the day.

Graph of sub-50 and 50-plus scores
© Anantha Narayanan

First let us take the sub-50 scores. Bradman averages 18.4, which is well below a few other batsmen, 32 to be exact. So even in this relatively acceptable failure category, Bradman is ordinary, as ordinary as many other batsmen.

Now we come to the crunch point. What about the 50-plus scores.

I think we have got it. Bradman's average for 50-plus innings is a huge 149.9, He is nearly 30% ahead of the next best. Translate this bland number. Every time he reached 50, 42 times in all, while he raised his bat, he was mentally taking guard to add 100 runs to his score, on an average. If this single statement does not prove Bradman's exalted status as a batsman, nothing else will.

But lo and behold, he has also done this a remarkable 52.5% of the times he batted in. This is 10% more than the next best batsmen.

This is the only analysis point wherein Bradman has averaged well above the next best and has had a frequency much higher than the next best. In all other criteria, either his average is comparable or the frequency is comparable. Not in this. And let me do a back-of-stamp calculation. When I multiply these two differential values for selected batsmen, I get 50-60% which is almost the same as his overall superiority.

				Sub-50			50 +
Batsman	Cty	Inns	Avge	Num	%	Avge	Num	%

Bradman D.G	Aus	80	18.4	38	47.5%	149.9	42	52.5%
Hammond W.R	Eng	140	20.9	94	67.1%	114.9	46	32.9%
Lara B.C	Win	232	17.7	150	64.7%	113.4	82	35.3%
Sehwag V	Ind	167	18.2	113	67.7%	113.3	54	32.3%
Atapattu M.S	Slk	156	14.4	123	78.8%	113.1	33	21.2%
Zaheer Abbas	Pak	124	16.0	92	74.2%	112.2	32	25.8%
Sangakkara K.C	Slk	183	18.8	117	63.9%	108.8	66	36.1%
Clarke M.J	Aus	138	17.1	97	70.3%	108.2	41	29.7%
Jayawardene D.P	Slk	217	18.6	145	66.8%	107.6	72	33.2%
Younis Khan	Pak	133	17.7	88	66.2%	107.6	45	33.8%
Crowe M.D	Nzl	131	18.0	96	73.3%	106.2	35	26.7%
Sobers G.St.A	Win	160	20.2	104	65.0%	105.8	56	35.0%
Pietersen K.P	Eng	145	19.4	100	69.0%	105.8	45	31.0%
de Silva P.A	Slk	159	16.6	117	73.6%	105.3	42	26.4%
EdeC Weekes	Win	81	18.6	47	58.0%	105.3	34	42.0%
Hayden M.L	Aus	184	19.4	125	67.9%	105.2	59	32.1%
Tendulkar S.R	Ind	311	17.5	195	62.7%	103.9	116	37.3%
Gibbs H.H	Saf	154	17.9	114	74.0%	103.2	40	26.0%
Smith G.C	Saf	174	19.4	118	67.8%	102.8	56	32.2%
Slater M.J	Aus	131	18.0	96	73.3%	102.4	35	26.7%

Finally a table/graph looking at this from a different angle. This looks at the average number of balls he faced per innings and his average runs per innings, leading to the average scoring rate.

Graph of runs per innings, balls per innings and scoring rate
© Anantha Narayanan

Bradman averages 149 balls per innings. This is 12% ahead of the next best one. His career strike rate is 58.3, which, if not the best, is in the top-15 out of the 112 selected batsmen. The way-out BpI value coupled with the quite high Strike rate leads to the impressive value of 87.5 runs per innings.

Batsman	Cty	Runs/Inns	Balls/Inns	St Rate

Bradman D.G	Aus	87.5	149.2	58.6
EdeC Weekes	Win	55.0	115.3	47.7
Sutcliffe H	Eng	54.2	134.1	40.4
Hobbs J.B	Eng	53.0	110.1	48.2
Barrington K.F	Eng	52.0	123.2	42.2
Hammond W.R	Eng	51.8	115.8	44.7
Lara B.C	Win	51.5	85.1	60.5
Sangakkara K.C	Slk	51.3	94.7	54.2
Hutton L	Eng	50.5	127.1	39.7
Sobers G.St.A	Win	50.2	103.0	48.7
Tendulkar S.R	Ind	49.7	91.9	54.1
Sehwag V	Ind	49.0	59.7	82.0
Mohammad Yousuf	Pak	48.3	92.1	52.4
Kallis J.H	Saf	48.2	105.6	45.6
Younis Khan	Pak	48.1	90.3	53.3
Jayawardene D.P	Slk	48.1	92.9	51.8
Gavaskar S.M	Ind	47.3	105.4	44.9
Ponting R.T	Aus	47.3	80.5	58.8
Chappell G.S	Aus	47.1	92.2	51.1
Richards I.V.A	Win	46.9	72.5	64.8

Now for a final collection of related figures. I am sure readers might want to know about the quality of bowling faced by Bradman. First let me say this. Only a cricketing ignoramus would say that the bowling faced by Bradman was not good. Amongst the bowlers Bradman faced were Larwood, Verity, Bowes, Voce, Robins, Alec Bedser, Griffith, Constantine, Laker and Mankad: most of these bowlers had bowling averages below 30.0. Only the South African bowlers were average. Overall the average quality of bowling (AQB) faced by Bradman works out to 35.9. This is based on the CTD bowling figures and the reciprocal method of working. The range of AQB is 28.5 to 46.6 and the mean is 34.4. So one could say that Bradman faced, on the whole, slightly below-par bowling. Just to give a reference point, the AQB values for Jayawardene, Sangakkara and Graeme Smith are above 36.0.

Let me do a final summary.

1. When Bradman failed, he failed in a big way. He failed often, more than the others. Until he reached 50, he was quite human.
2. When he posted big scores, he was very good but only comparable to the other equally good players in these aspects of the game.
3. It is at the level of 50 that he completely overshadowed others. He reached this level over 50% of the time and posted an average of nearly 150 when he crossed 50. These two factors together account for the 60% increase he had in the key measures.
4. From a different angle, he posted a way-above figure on the Balls per innings and boasted of a very high career strike rate. These two factors together also account for the 60% increase he had in the key measures.

To download/view the Excel sheet containing the single comprehensive table, please click/right-click here.

To download/view the Excel sheet containing all the lower level tables, please click/right-click here.

To download/view the Excel sheet containing Bradman's career details, please click/right-click here.

To download/view the Special table containing the information requested for by Arjun (Averege of innings factors based on innings highest score at 1.00) and Gerry (Average Bowling Quality faced for scores of 50 and above), please click/right-click here. This also contains an additional data field which is Batting Average * 30.0 / Career ABQ.

It is certain that Bradman possessed non-quantifiable skills which enabled him to reach the pinnacle, leaving everyone far behind. Enough has been written about that. Here I have only tried to look at the mundane figures. Somewhere earlier in the article I had mentioned shorty in a flippant manner. Short Bradman might have been in stature, but he stands tall, very tall indeed. And hopefully now we know why Bradman clicked.

Easier analysed than done !!!

Since my tennis elbow is troubling me a lot I will be responding to many remarks with pre-formed boiler-plate responses generated through a key macro program. However let me assure the readers that I always read the responses in full.

Without bringing in anything else into the picture, it is necessary to briefly recapitulate the wonderful performance of Anand, winning his fifth World Chess title, fourth in succession and the toughest to date. Gelfand proved to be a tough nut to crack and Anand, to his credit, has said so right from he beginning. He is adding a few very significant bold lines to his biography and strengthens his claim to being called India's best sportsman ever. The only appropriate words needed during this epic occasion are expressions of recognition, approbation and appreciation.

Full post

ODI bowling: location summary, country details and key matches

Analysis of ODI bowling performances by location, country details and key matches

Anantha Narayanan

25-Feb-2013

This is a follow-up to the previous article which was an analysis on ODI Batsmen (click here for the article) by teams faced. Normally I do analysis-centric articles which take on and expound a theme. Once in a while I do different types of articles in which I go deep in one area of the game and provide data tables around it. This is one such article.

In order to avoid the usual questions and comments which relate to specific players, let me emphasize how these series of articles would be structured. I would cover the top/selected 12-15 players in a graph to visually present the variations. Then I would present data tables, in the body of the articles, which would normally cover the top 25 players or so. However the most important of the tables are the ones which have been uploaded and are available for downloading for permanent storage and perusal. Normally these cover the complete collection of players, say 150, who meet the cut-off criteria. So, before coming out with comments that "Zaheer Khan or Botham or Walsh are not mentioned", please download the tables and check. Superficial reading of the articles is not enough.

Over the current month or or two, I will be doing the following four tables. These may all not follow in sequence. I may intersperse other pending analysis in between.

1. ODI Batting analysis - summary by location and details by country played against and key tournament matches. Completed.
2. ODI Bowling analysis - summary by location and details by country played against and key tournament matches. Current article.
3. Test Batting analysis - summary by location and details by country played against.
4. Test Bowling analysis - summary by location and details by country played against.

The vs Country grouping is as explained below. I have 10 countries/groups: Australia, England, India, New Zealand, Pakistan, South Africa, Sri Lanka, West Indies, Bangladesh and Zimbabwe combined and finally "all other teams". A fair grouping and nothing of relevance would be left out. And the analysis is very extensive in that it is by country played against: at home, away, in neutral locations and across career.

I have defined the key tournament matches by the following criteria. I have deliberately excluded the tri-series finals from this group. At last count there were well over 100 such tournaments and inclusion of these tournaments would dilute the whole concept. Readers might differ. However it should be noted that inclusion of the Sharjah tournaments would also mean inclusion of all CB/VB Series and all inconsequential tri-series ever played. For that matter I have set the criteria as tournaments with 6 teams and above. World Cup Super-Sixes and Super-Eights rank with the Quarter finals.

10 World Cup Finals
20 World Cup Semi-finals
8 World Cup Quarter finals
45 World Cup Super-Sixes and Super-Eights
7 ICC/Champions' Trophy Finals
12 ICC/Champions' Trophy Semi finals
3 Finals of the following three 6+ team tournaments
- Benson & Hedges World Championship of Cricket, 1984/85 (7 teams)
- MRF World Series (Nehru Cup), 1989/90 (6 teams)
- Australasia Cup, 1989/90 (6 teams)

Total number of key tournament matches: 105 There were a number of comments in the batting article to consider the inclusion of some or all of the tri-series Finals also. I have generally discarded the suggestions because of the preponderance of average tri-series. Anyhow I am not going to be consistent across both areas of the game.

Normally bowling analysis is easier because of the unique nature of the single most important bowling measure, the bowling average. The batting average is an inconsistent and incomplete measure, with its two inherent drawbacks, viz., the confusing treatment of not-outs and the presence of another very important factor, the scoring rate. Bowling average, on the other hand, is a perfect composite of two important measures, viz., the bowling strike rate (BpW) and bowling accuracy (RpB). The single measure is so perfect that it is rare that we have to do any sub-analysis on the two constituent measures.

Bowler analysis - Summary by location

Summary of wickets for top bowlers
© Anantha Narayanan

This graph contains the top 11 wicket-takers, who have captured over 300 wickets and 7 other outstanding ODI bowlers. I have altered the presentation slightly. The Blue rectangles, anchored on the left, indicate BowAvg values below 28.0 and Red rectangles, anchored on the right, indicate BowAvg values above 28.0. The size of the rectangle gives an indication of the BowAvg value, the bigger the better or worse, depending on whether these are blue or red. The numbers adjacent to the rectangles indicate the number of wickets captured in that classification.

Most of the bowlers have performed above-par, home, away and in neutral locations. As expected Afridi and Jayasuriya have been below-par everywhere. Kumble has just about beaten par in neutral locations. But two real surprises: Wasim Akram has been below-par at home (72 wickets at 31.12) and less surprisingly, Warne, below-par, away (84 wickets at 30.40).

Bowler analysis - All matches - by opposing country

Summary of wickets against teams
© Anantha Narayanan

This graph is similar to the batting graphs. These are ordered by the BowAvg values. The top 15 bowlers are shown. The player's performance against the 10 team groups are plotted. Blue ovals indicate BowAvg values of below 28.0 and Red ovals indicate BowAvg values above 28.0. The size of ovals indicates how far off the par the performances are. The number of wickets captured is displayed, colour-coded, under each country. In general, the cut-off values are 100 wickets. For the lower level Home/Away/Neutral vs-team analysis, the cut-off is lowered to 75 wickets.

Garner's overall average has been stupendous, backed by an excellent strike rate and the best-ever RpO values. He has played very well against all countries barring India: 6 wickets at 32.5. The all-blue performers are Holding, Hadlee, Donald and McGrath. Muralitharan has two below-par countries and has a huge tally against the weaker countries. Wasim Akram has one weak spot, against England. Two relatively unknown bowlers, Pringle and Bracken are in this elite lot.

A vertical perusal of the table indicates that South Africa is the toughest country to bowl against and New Zealand, relatively the friendliest one.

Bowler analysis - Home matches - by opposing country

Summary of wickets against teams in home matches
© Anantha Narayanan

McGrath was king at home. An all-blue graph. No other top bowler has had this distinction. Pollock has also been quite good, barring 11 against New Zealand at 38.5. Many bowlers seem to have found it tough to bowl against India, even playing at home. Surprisingly Pakistan is the other way around.

Bowler analysis - Away matches - by opposing country

Holding has been good overall against all countries, playing away. But Donald and Johnson have the best record, playing away. As expected, the away BowAvg values graph has a smattering of red ovals scattered across.

Bowler analysis - Neutral matches - by opposing country

Lee has been an outstanding neutral match record with almost all-blue record. Again Donald has done very well also. And McGrath has been equally good. All other leading bowlers have their weak spots. Look at the huge number of wickets captured by Wasim Akram in neutral countries, that too at very low average.

Now for the tables. Most of these are self-explanatory.

	Career			Key	Mats			Home			Neutral			Away
Batsman	Wkts	Avge	ODIs	Wkts	Avge	RpO	Wkts	Avge	RpO	Wkts	Avge	RpO	Wkts	Avge	RpO

Muralitharan	534	23.08	32	36	21.03	4.31	154	24.62	3.86	220	20.81	3.82	160	24.71	4.14
Wasim Akram	502	23.53	12	14	32.93	4.48	72	31.12	4.36	262	21.21	3.76	168	23.89	3.87
Waqar Younis	416	23.85	3	6	22.83	5.07	67	23.15	4.67	218	22.49	4.75	131	26.47	4.61
Vaas WPUJC	400	27.54	19	23	26.61	4.27	81	27.53	3.89	168	27.08	4.18	151	28.06	4.39
Pollock	393	24.51	15	15	33.47	3.86	193	20.24	3.51	97	27.99	3.83	103	29.23	3.78
McGrath	381	22.02	37	48	17.23	3.93	160	20.13	3.77	113	20.22	3.81	108	26.69	4.07
Lee	377	23.18	12	26	19.00	4.89	169	23.34	4.69	79	18.29	4.55	129	25.97	4.93
S Afridi	344	33.38	8	5	32.60	4.23	62	36.21	4.87	161	33.42	4.55	121	31.89	4.55
Kumble	337	30.90	10	12	39.00	4.81	126	28.94	4.45	144	26.22	4.14	67	44.64	4.37
Jayasuriya	323	36.74	20	18	34.17	4.90	119	28.31	4.45	111	41.03	4.84	93	42.41	5.05
Srinath	315	28.08	12	17	30.71	5.07	103	30.50	4.58	124	27.78	4.54	88	25.68	4.14
Warne	293	25.73	9	18	20.56	4.44	134	24.40	4.17	75	22.88	4.23	84	30.40	4.38
Agarkar	288	27.85	5	2	81.50	5.69	109	27.39	5.29	82	31.71	4.94	97	25.11	4.97
Saqlain M	288	21.79	6	6	38.50	4.75	54	20.61	4.52	174	21.63	4.35	60	23.32	3.99
Vettori	282	31.50	14	16	35.06	4.23	113	31.08	4.06	76	30.86	4.12	93	32.54	4.23
Zaheer Khan	278	29.03	13	21	27.38	5.17	94	30.21	5.20	78	26.35	4.57	106	29.96	4.92
Donald	272	21.79	7	11	28.18	4.56	88	26.35	4.24	94	18.78	4.10	90	20.47	4.10
Kallis	270	31.70	16	15	31.93	4.63	113	32.40	4.93	78	27.44	4.39	79	34.90	5.09
Abdul Razzaq	269	31.83	9	7	34.86	4.07	40	44.23	5.02	126	31.27	4.63	103	27.71	4.60
Ntini	266	24.67	11	7	40.86	5.02	122	24.00	4.42	55	23.56	4.15	89	26.26	4.93
Harbhajan	259	33.40	11	15	26.07	3.99	104	35.48	4.42	70	30.67	4.16	85	33.09	4.28
Kapil Dev	253	27.45	4	9	13.00	2.85	100	27.15	4.01	85	24.49	3.48	68	31.59	3.64
ShoaibAkhtar	247	24.98	6	10	23.80	5.06	46	28.24	5.08	113	23.97	4.70	88	24.57	4.68
Streak	239	29.83	6	5	52.20	5.12	67	26.72	4.47	85	31.47	4.42	87	30.62	4.66
Gough	235	26.44	3	4	35.50	5.26	108	27.27	4.41	34	24.44	4.36	93	26.20	4.40
Walsh	227	30.47	3	2	69.50	4.88	46	40.02	4.35	99	26.04	3.53	82	30.46	3.85
Ambrose	225	24.13	3	4	24.00	3.20	65	25.25	3.58	86	22.64	3.35	74	24.88	3.54
Anderson	208	30.80	9	3	90.67	4.96	91	27.80	4.99	44	28.45	4.73	73	35.95	5.21
Harris	203	37.50	11	4	95.75	4.85	94	29.73	4.07	52	48.38	4.29	57	40.39	4.55
McDermott	203	24.72	2	6	15.83	4.75	125	24.32	3.89	18	24.33	3.84	60	25.67	4.42

This table is ordered by career wickets captured. The top 30 are shown.

Muralitharan is a comfortable second with 36 wickets. The surprise is the low tally of the two great Pakistan bowlers. Lee has been very productive in these matches. It is obvious that this would be the domain of the modern bowlers because of the number of matches in WCs classified so. The formats have changed drastically.

All matches	Career		AUS		ENG		IND		NZL		PAK		SAF		SLK		WIN		B/Z		OTH
Bowlers	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge

Muralitharan	534	23.08	53	31.15	28	24.00	74	31.77	74	17.93	96	25.25	49	23.35			34	28.12	90	15.56	36	11.08
Wasim Akram	502	23.53	67	27.43	32	35.44	60	25.17	64	18.53			35	23.94	92	20.97	89	25.57	42	20.93	21	10.57
Waqar Younis	416	23.85	29	40.76	30	20.10	37	24.49	79	15.85			58	24.91	84	24.36	60	26.98	26	26.35	13	14.08
Vaas WPUJC	400	27.54	47	35.70	32	35.03	70	31.63	49	22.22	61	32.98	40	28.00			26	18.69	61	16.90	14	18.93
Pollock	393	24.51	57	29.35	40	21.77	48	24.48	48	27.31	49	25.14			51	23.90	44	24.23	33	23.09	23	14.04
McGrath	381	22.02			53	22.96	34	26.76	59	19.78	57	19.11	58	23.79	36	25.08	36	25.72	24	20.96	24	12.25
Lee	377	23.18			64	22.61	55	21.00	52	20.98	38	23.18	40	23.65	38	32.61	49	21.63	20	30.30	21	15.00
S Afridi	344	33.38	43	31.05	34	27.53	38	58.21	20	48.20			26	39.15	57	35.12	31	35.16	65	24.75	30	10.60
Kumble	337	30.90	31	40.29	25	46.00			39	27.87	54	24.26	46	32.00	34	51.41	41	23.73	40	25.30	27	15.22
Jayasuriya	323	36.74	29	45.72	32	35.69	44	49.14	38	29.89	71	36.46	33	36.03			29	28.69	40	30.23	7	40.29
Srinath	315	28.08	33	36.73	35	23.71			51	20.41	54	30.69	28	50.25	34	28.03	31	27.74	36	17.19	13	20.54
Warne	293	25.73			22	33.14	15	56.27	49	19.24	37	23.76	60	28.63	29	25.69	50	20.90	23	21.52	8	17.75
Agarkar	288	27.85	36	28.42	21	39.71			20	29.20	32	36.16	18	50.28	49	20.61	32	24.50	61	22.61	19	18.16
Saqlain M	288	21.79	27	23.33	24	19.88	57	24.39	33	21.70			16	36.94	47	24.87	30	18.00	48	13.69	6	17.67
Vettori	282	31.50	53	40.57	24	29.38	27	38.22			31	28.81	25	45.08	25	40.24	31	22.19	49	20.71	17	15.71
Zaheer Khan	278	29.03	23	46.39	25	31.40			30	27.73	28	40.86	22	27.91	62	30.56	13	28.46	44	20.11	31	15.45
Donald	272	21.79	45	25.98	31	19.42	46	21.15	40	21.07	27	24.00			32	20.31	21	27.10	17	19.88	13	10.31
Kallis	270	31.70	34	47.97	25	33.44	30	36.77	37	29.62	40	28.57			31	29.74	42	23.02	18	25.61	13	30.69
Abdul Razzaq	269	31.83	25	28.84	19	37.95	35	39.86	39	34.15			21	47.24	50	29.88	31	27.32	35	19.09	14	28.07
Ntini	266	24.67	39	29.77	20	27.85	25	28.68	37	23.22	49	20.24			27	27.78	27	25.59	33	19.91	9	19.67
Harbhajan	259	33.40	32	46.44	36	25.33			17	40.76	14	57.21	25	30.88	61	26.95	33	34.24	25	32.84	16	24.44
Kapil Dev	253	27.45	45	27.69	28	28.07			33	27.61	42	26.50	12	31.25	38	26.08	43	28.88	12	23.42
ShoaibAkhtar	247	24.98	32	34.78	34	24.18	41	26.80	35	23.46			27	23.59	29	25.38	10	23.00	20	22.05	19	14.26
Streak	239	29.83	18	41.61	33	20.18	39	38.00	19	41.74	35	20.66	17	32.88	28	31.96	24	27.75	16	18.00	10	30.80
Gough	235	26.44	31	31.10			24	31.88	21	27.33	21	34.52	51	21.25	23	25.87	15	37.07	43	18.95	6	22.50
Walsh	227	30.47	30	45.73	25	45.48	44	24.16	21	26.71	65	27.48	4	47.25	26	25.88			6	13.83	6	8.83
Ambrose	225	24.13	61	21.80	14	40.86	32	22.47	13	24.15	69	21.35	15	34.07	12	32.17			5	19.00	4	7.25
Anderson	208	30.80	29	41.45			32	34.75	18	39.72	29	21.90	24	20.71	24	26.96	28	29.39	12	34.83	12	29.75
Harris	203	37.50	31	37.52	20	33.70	24	37.08			26	43.35	20	52.25	30	38.87	22	30.00	21	38.52	9	8.78
McDermott	203	24.72			21	19.43	30	27.47	26	27.58	23	28.74	17	28.12	17	34.12	63	19.43	5	22.80	1	12.00

Since this is a table of top performers, low bowling averages will not be discussed. I will restrict myself to the surprising high averages by the top bowlers. Muralitharan has found the Indian batsmen tough to bowl against. Wasim Akram's waterloo has been against the English batsmen. Waqar Younis has found the batsmen of his settled land intimidating. The Sri Lankan batsmen handled Lee very well. Warne had problems against English and Indian batsmen. No country's batsmen have mastered McGrath and Donald.

Home matches	Career		AUS		ENG		IND		NZL		PAK		SAF		SLK		WIN		B/Z		OTH
Bowlers	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge

Pollock	193	20.24	31	25.26	29	19.34	23	17.91	11	38.45	19	20.47			26	17.77	27	16.59	18	18.33	9	10.89
Lee	169	23.34			33	19.67	33	19.52	20	23.85	19	24.95	16	21.56	21	39.00	13	13.62	7	36.00	7	15.43
McGrath	160	20.13			30	19.97	7	10.71	20	21.55	30	20.30	26	17.62	23	23.13	12	26.83	9	17.33	3	13.00
Muralitharan	154	24.62	18	23.33	10	13.80	31	40.06	13	20.00	27	25.56	9	16.00			9	30.22	25	19.52	12	11.50
Warne	134	24.40			19	28.21	1	57.00	24	18.00	18	21.78	18	38.83	19	24.74	26	19.62	9	19.33
Kumble	126	28.94	13	30.46	10	51.40			16	28.62	10	26.90	19	24.74	11	48.00	27	21.56	13	26.15	7	12.71
McDermott	125	24.32			6	17.00	17	25.18	19	23.53	14	28.86	8	24.50	14	31.57	45	21.40	2	29.00
Ntini	122	24.00	26	29.00	11	25.36	9	30.00	11	22.73	18	23.72			17	23.35	7	38.86	17	11.82	6	13.00
Jayasuriya	119	28.31	13	31.08	13	16.31	28	36.00	13	20.23	17	27.82	9	35.89			11	21.45	15	27.73
Kallis	113	32.40	21	45.57	9	44.44	11	36.64	7	48.29	17	23.06			16	27.94	18	22.33	11	20.18	3	33.33
Vettori	113	31.08	26	33.27	11	28.82	9	35.78			9	46.22	7	51.00	9	52.67	21	19.67	21	16.57
Agarkar	109	27.39	17	27.00	12	31.17			5	35.80	5	38.80	6	56.67	20	20.00	15	31.13	24	21.04	5	13.40
Gough	108	27.27	22	27.50			12	31.00	2	51.50	15	29.27	15	28.13	10	26.60	11	32.45	17	20.41	4	8.50
Abdur Razzak	107	23.82	9	30.56	7	23.57	4	90.75	9	33.56	10	19.80	4	37.00	2	71.00			41	12.15	21	16.86
Harbhajan	104	35.48	18	54.94	31	22.10			1	41.00	9	54.78	8	23.75	16	37.19	11	38.64	10	21.20
Srinath	103	30.50	12	35.50	21	23.14			11	31.27	4	41.25	9	58.78	16	23.56	19	31.84	11	15.55
S Waugh	101	31.16			15	28.60	13	35.00	17	35.65	13	29.31	5	44.40	10	27.00	26	29.08	2	14.00
Kapil Dev	100	27.15	12	37.83	16	27.50			3	47.00	12	28.08	6	25.00	24	21.67	19	25.74	8	23.00
Cairns	94	31.01	23	36.57	20	21.15	7	36.14			12	41.42	10	29.70	11	26.45	7	19.57	4	44.00
Zaheer Khan	94	30.21	19	44.32	11	22.91			7	21.43	14	40.21	1	43.00	16	29.25	7	17.14	13	27.08	6	8.33

Look at the struggles Pollock had at home against the New Zealand batsmen, Lee against the Sri Lankan batsmen, Muralitharan against the Indian batsmen, Warne against South African batsmen and so on. The bowlers who have done very well at home are McGrath and Pollock.

Neutral matches	Career		AUS		ENG		IND		NZL		PAK		SAF		SLK		WIN		B/Z		OTH
Bowlers	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge

Wasim Akram	262	21.21	21	22.57	13	32.69	46	23.48	20	18.80			10	25.80	66	18.73	59	23.36	16	15.94	11	6.82
Muralitharan	220	20.81	2	75.50	10	32.70	28	20.82	34	12.56	53	27.28	26	19.00			17	25.53	34	14.74	16	13.44
Waqar Younis	218	22.49	13	31.31	4	26.75	25	28.04	29	12.72			21	25.24	67	22.84	43	23.37	13	17.00	3	11.00
Saqlain M	174	21.63	7	31.00	10	13.40	47	24.70	21	17.67			4	67.50	41	21.90	25	19.40	16	9.31	3	26.33
Vaas WPUJC	168	27.08	5	42.40	11	44.00	30	27.70	16	28.44	40	33.50	15	24.40			18	12.44	25	20.32	8	16.12
S Afridi	161	33.42	15	32.20	8	41.75	25	45.92	12	46.17			11	41.55	35	34.03	19	37.42	11	29.27	25	7.20
Kumble	144	26.22	14	40.57	5	34.00			9	23.78	44	23.66	13	27.08	11	37.91	10	26.70	22	21.64	16	16.88
Abdul Razzaq	126	31.27	6	54.17			21	25.81	20	28.40			15	35.47	27	31.37	16	27.50	13	28.54	8	35.88
Srinath	124	27.78	9	40.56	8	27.62			2	34.50	50	29.84	6	52.00	15	17.00	12	21.25	12	24.67	10	18.00
McGrath	113	20.22			6	23.17	13	31.85	20	14.45	16	16.50	14	27.93	6	25.33	11	17.45	9	21.89	18	13.72
ShoaibAkhtar	113	23.97	11	44.27	8	17.75	18	30.33	12	25.58			12	27.50	18	16.56	8	24.88	9	19.67	17	13.12
Jayasuriya	111	41.03	3	77.00	6	71.00	7	69.00	16	32.81	36	41.11	13	27.08			9	43.00	15	31.73	6	32.33
Walsh	99	26.04	3	47.67	9	42.33	14	21.86	3	19.33	39	23.23	1	99.99	18	30.06			6	13.83	6	8.83
Pollock	97	27.99	6	30.00	5	12.00	12	39.00	24	20.50	14	29.21			17	27.12	4	55.75	4	51.75	11	19.55
Prasad	96	29.91	13	28.85	6	10.50			9	21.89	35	30.60	6	47.50	8	33.75	3	64.33	14	26.57	2	22.50
Donald	94	18.78	12	20.17	4	34.25	9	20.67	24	16.92	13	16.23			15	20.20	6	21.67	4	16.50	7	12.00
Ambrose	86	22.64	5	23.80			13	24.15	1	19.00	45	20.87	5	27.60	10	30.20			3	9.33	4	7.25
Aaqib Javed	86	33.15			2	17.00	36	27.44	6	34.17			2	61.00	24	32.67	13	44.92	3	35.67
Kapil Dev	85	24.49	12	17.83	4	22.25			21	20.62	20	22.55	1	36.00	8	32.38	17	32.00	2	28.00
Streak	85	31.47	4	60.25	6	27.00	14	42.57	2	84.00	17	19.29	3	46.00	17	29.82	15	19.87			7	33.86

Barring a few matches against England, Wasim Akram was the master of neutral pitches. Almost similar situation with Muralitharan.

Away matches	Career		AUS		ENG		IND		NZL		PAK		SAF		SLK		WIN		B/Z		OTH
Bowlers	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge	Wkts	Avge

Wasim Akram	168	23.89	38	26.58	13	34.77	12	32.33	37	15.46			18	21.89	13	22.62	13	32.85	16	24.06	8	11.50
Muralitharan	160	24.71	33	32.73	8	25.88	15	35.07	27	23.70	16	18.00	14	36.14			8	31.25	31	13.26	8	5.75
Vaas WPUJC	151	28.06	32	34.91	9	45.22	14	45.71	29	17.90	14	23.57	20	31.10			8	24.88	21	15.33	4	20.25
Waqar Younis	131	26.47	13	49.08	24	16.04	2	33.50	29	21.34			31	21.81	10	29.60	8	46.12	9	36.22	5	18.20
Lee	129	25.97			29	23.07	10	36.70	19	22.58			24	25.04	11	30.27	26	26.08	6	33.17	4	18.50
S Afridi	121	31.89	28	27.32	14	30.57	8	80.00	4	56.75			9	32.44	17	23.94	12	27.08	26	25.69	3	35.67
McGrath	108	26.69			17	28.18	14	30.07	19	23.53	11	19.64	18	29.50	7	31.29	13	31.69	6	25.00	3	2.67
Zaheer Khan	106	29.96	2	46.00	12	38.67			14	31.57	10	49.40	15	27.00	29	28.10	2	38.50	19	17.84	3	16.00
Pollock	103	29.23	20	35.50	6	41.67	13	22.69	13	30.46	16	27.12			8	37.00	13	30.38	11	20.45	3	3.33
Abdul Razzaq	103	27.71	19	20.84	13	33.31	7	50.86	11	40.36			5	68.60	16	25.75	15	18.53	11	7.82	6	17.67
Agarkar	97	25.11	10	28.90	9	51.11			4	21.50	6	38.17	7	47.43	11	18.36	15	16.40	30	15.70	5	24.20
Johnson	97	23.57			10	26.40	24	26.00	15	27.60			18	20.17	11	18.27	8	20.75	10	22.60	1	28.00
Jayasuriya	93	42.41	13	53.15	13	38.77	9	74.56	9	38.67	18	35.33	11	46.73			9	23.22	10	31.70	1	54.00
Vettori	93	32.54	21	44.90	6	31.67	11	38.73			5	32.40	11	30.36	11	28.36	4	41.50	24	20.54
Gough	93	26.20	9	39.89			8	37.75	19	21.47	2	96.00	33	17.30	2	55.00	4	49.75	16	18.50
Donald	90	20.47	23	23.35	15	14.47	18	20.11	9	22.56	1	94.00			5	15.20	11	25.55	2	24.50	6	3.83
Ntini	89	26.26	12	28.25	9	30.89	3	49.00	11	26.18	24	16.79			6	44.00	17	20.24	6	41.83	1	23.00
Srinath	88	25.68	12	35.08	6	20.50			38	16.53			13	43.54	3	99.99			13	11.69	3	16.33
ShoaibAkhtar	88	24.57	18	26.00	19	26.11	14	20.64	12	27.92			3	28.33	9	22.11	2	15.50	9	23.44	2	24.00
Streak	87	30.62	14	33.50	16	13.88	13	48.92	11	35.91	12	19.08	7	37.43	7	36.29	1	95.00	6	17.00
Harbhajan	85	33.09	5	38.20	5	29.00			6	34.00			10	48.00	33	23.52	13	41.08	13	37.15
Warne	84	30.40			3	64.33	7	52.86	14	21.07	1	99.99	29	27.90	7	26.86	17	26.94	4	20.50	2	25.50
Walsh	82	30.46	16	50.00	6	47.50	23	21.30	11	28.09	18	25.94	3	27.33	5	13.00
Kallis	79	34.90	11	51.00	10	32.60	10	32.00	12	36.25	8	41.75			7	37.29	17	24.00	1	64.00	3	16.00
Mills	78	25.18	15	30.60	8	28.00	5	63.00					17	20.76	9	25.33			24	16.04
Holding	78	19.53	49	20.51	15	16.20	3	31.33	1	23.00	10	15.80
Marshall	75	28.64	38	28.66	15	24.60	5	31.80			17	29.18

Donald has performed best on away pitches. And Holding. The wicket tally for both, however, was below 100.

Bowler location summary and key tournament match performances.
Bowler wickets analysis vs Team - for all matches
Bowler wickets analysis vs Team - for home matches
Bowler wickets analysis vs Team - for away matches
Bowler wickets analysis vs Team - for neutral matches

I am not going to do too much of work on the conclusions which can be drawn. This is not that type of article. Just a minimalistic set of statements to complete the all-time best ODI team, according to me.

There is a need to mention four bowlers who stand out for various reasons.

The first (amongst equals) is McGrath. An overall bowling average of 22, composed of averages below 27.0 against all countries and below 20 against couple of countries. Impeccable control over line and length, all delivered at good pace. Add to it the 48 wickets in key matches, it is difficult to think of anyone else as the first selection.

The next one is Garner. Not many wickets, by today's standards, but understandable. But almost all top quality wickets, a very low career Bowling average, a wicket every 36 balls at an RpO of just over 3.0. How can anyone not include him. How successful today's batsmen would have been against Garner, with his height, bounce he normally extracted and swing and cut he could generate at will. Possibly not 18+ but comparable to McGrath's figures.

The third one is Wasim Akram. His overall numbers speak for themselves. Above-par performances in all locations outside Pakistan and the ability to generate wicket-taking deliveries at will. Almost certainly the best left-arm fast bowler to have played ODI cricket. His batting would be a bonus.

Finally for the spinner. Muralitharan's huge number of wickets against the weaker teams and Warne's relative lack of success in away locations means that my selection is going to be Saqlain Mushtaq. An excellent career bowling average and very consistent sub-24.0 average in any location means he is going to be effective everywhere.

Four jewels in the crown, that is all one can say.

So these four bowlers, McGrath, Garner, Wasim Akram and Saqlain Mushtaq complete the team line-up. It is difficult to leave out Hadlee, Waqar Younis, Donald, Lee, Bond, Holding, Muralitharan and Warne. But that is the way selections go.

At a later date I might come with a combined article, doing the Batting computations using the ODI Index (RpAI (excluding single-digit not outs) x S/R). This will ensure that strike rate will be given its due importance. Also will, at least partly, address the vexed question of averages vs runs per innings. I might then look at including at least 4/5 Team tournament Finals.

Full post

ODI batting: location summary, country details and key matches

An analysis of ODI batting performances by opposition, host country and major tournaments

Anantha Narayanan

25-Feb-2013

Normally I do analysis-centric articles which take on and expound a theme. Once in a while I do different types of articles in which I go deep in one area of the game and provide data tables around it. This is one such article. However I can assure the readers that once the reader understands what is being covered, he will download the tables and keep the same for permanent reference. This has been a tough exercise on presentation and I must thank Milind for his invaluable suggestions.

In order to avoid the usual questions and comments which relate to specific players, let me emphasize how these series of articles would be structured. I would cover the top/selected 10-12 players in a graph to visually present the variations. Then I would present data tables, in the body of the articles, which would normally cover the top 25 players or so. However the most important of the tables are the ones which have been uploaded and are available for downloading for permanent storage and perusal. Normally these cover the complete collection of players, say 150, who meet the cut-off criteria. So, before coming out with comments that "Miandad or Nick Knight or Amarnath are not mentioned", please download the tables and check. Superficial reading of the articles is not enough.

Over the next month or or two, I will be doing the following four tables. These may all not follow in sequence. I may intersperse other pending analysis in between.

1. ODI Batting analysis - summary by location and details by country played against and key tournament matches.
2. ODI Bowling analysis - summary by location and details by country played against and key tournament matches.
3. Test Batting analysis - summary by location and details by country played against.
4. Test Bowling analysis - summary by location and details by country played against.

The vs Country grouping is as explained below. I have 10 countries/groups: Australia, England, India, New Zealand, Pakistan, South Africa, Sri Lanka, West Indies, Bangladesh & Zimbabwe combined and finally "all other teams". A fair grouping and nothing of relevance would be left out. And the analysis is very extensive in that it is by country played against: at home, away, in neutral locations and across career.

I have defined the key tournament matches by the following criteria. I have deliberately excluded the tri-series Finals from this group. At last count there were well over 100 such tournaments and inclusion of these tournaments would dilute the whole concept. Readers might differ. However it should be noted that inclusion of the Sharjah tournaments would also mean inclusion of all CB/VB Series and all inconsequential tri-series ever played. For that matter I have set the criteria as tournaments with 6 teams and above. World Cup Super-Sixes and Super-Eights rank with the Quarter finals.

10 World Cup Finals
20 World Cup Semi-finals
8 World Cup Quarter finals
45 World Cup Super-Sixes and Super-Eights
7 ICC/Champions' Trophy Finals
12 ICC/Champions' Trophy Semi finals
3 Finals of the following three 6+ team tournaments
- Benson & Hedges World Championship of Cricket, 1984/85 (7 teams)
- MRF World Series (Nehru Cup), 1989/90 (6 teams)
- Australasia Cup, 1989/90 (6 teams)

Total number of key tournament matches: 105

First the graphs. I would only offer limited comments since I expect the readers to come out with their own comments. I might miss some obvious comment. Should not really matter. The ordering is different for different modes of presentation since we can get different insights. In general, the graphs are ordered by the concerned RpI values and the tables are ordered by the appropriate run values. For ODIs I am a great fan of RpI (Runs per innings) than Batting average. Granted this measure might be slightly unfair to those middle order players who end unbeaten in over 20% of the innings and have a fat Batting average. However I think that is small price to pay for having a measure which measures the real contributions. So, no Batting average in this article.

Batsmen analysis - Summary by location

This graph contains the top 10 run-scorers and five other outstanding ODI batsmen. The Green rectangles indicate RpI values over 35.0 and Red rectangles indicate RpI values below 35.0. The size of the rectangle gives an indication of the RpI value. The numbers within the rectangles indicate the number of runs scored in that classification. Tendulkar has comfortable 40+ RpI values at home and in neutral venues and falls marginally below 35.0 in away matches. Ponting, Kallis and Ganguly average above 35.0 in all three areas. Jayasuriya falls below 35.0 in all areas. Haynes is the outstanding Home performer, with a mind-blowing RpI value of 52.51. Tendulkar's RpI value at neutral venues is the highest at 43.73. Richards has a magnificent away RpI value of 47.82. It is amazing that Richards has only played 26 matches at home and not even crossed 1000 runs.

Batsmen analysis - All matches - by opposing country

This graph requires some explanation. These are ordered by the RpI values. The player's performance against the 10 team groups are plotted. Blue ovals indicate RpI values of over 35.0 and Red ovals indicate RpI values below 35.0. The number of innings is displayed under each country. Amla crosses 35.0 against all countries. However the number of matches is quite low. Now look at Tendulkar's graph. He has crossed 35.0 against all countries. That is some level of consistency especially considering that he has played over 30 matches against all countries, barring the non-Test teams. Kohli also has similar figures, barring against the non-Test countries.

A vertical perusal of the table indicates that Australia is the toughest country to bat against and Sri Lanka, relatively the friendliest one.

Batsmen analysis - Home matches - by opposing country

Haynes leads the table, with a RpI value of 52.51. Tendulkar's home figures are all above-par, barring, surprisingly, against Bangladesh/Zimbabwe. Real surprise is that no batsman has a 100% record of 35+ average against all countries. Possibly Tendulkar has the best overall home record.

Batsmen analysis - Away matches - by opposing country

As expected, the away RpI values graph has a smattering of red ovals scattered across. Richards has crossed 35 everywhere, but with fewer matches. Chanderpaul's graph looks very good. It can be seen that an almost completely different set of batsmen are featured here. It is not easy to average RpI of 40+ in away matches across a career. Only Richards and Hayden have achieved that. Richards' 47.83 is well above his career RpI. Tendulkar has gone below 35.0 only because of one blot in his career: an RpI of 25 in the away matches against South Africa.

Batsmen analysis - Neutral matches - by opposing country

In neutral locations, Tendulkar is king, with an outstanding RpI value of 43.73. Only against South Africa does he slip to 34.9. Otherwise all RpI values are above 35. No one else has achieved anywhere near this level of consistency.

Now for the tables. Most of these are self-explanatory.

		Career			KeyMats			Home			Neutral			Away
Batsman	Inns	Runs	RpI	Inns	Runs	RpI	Inns	Runs	RpI	Inns	Runs	RpI	Inns	Runs	RpI

Tendulkar	452	18426	40.76	18	656	36.44	160	6976	43.60	146	6385	43.73	146	5065	34.69
Ponting	365	13704	37.54	26	1163	44.73	150	5406	36.04	86	3208	37.30	129	5089	39.45
Jayasuriya	433	13430	31.01	20	656	32.80	124	3880	31.29	162	5463	33.72	147	4087	27.80
Inzamam	350	11739	33.54	10	256	25.60	64	2674	41.78	159	5133	32.28	127	3932	30.96
Kallis	307	11498	37.45	16	794	49.62	131	5102	38.95	75	2689	35.85	101	3708	36.71
Ganguly	300	11363	37.87	11	684	62.18	75	3110	41.47	127	4785	37.68	98	3468	35.39
Dravid	318	10889	34.24	12	392	32.67	91	3406	37.43	114	3439	30.17	113	4044	35.79
Jayawardene	351	10596	30.18	16	610	38.12	107	3229	30.18	113	3810	33.72	131	3557	27.15
Sangakkara	306	10472	34.22	17	557	32.76	96	3025	31.51	87	3021	34.72	123	4426	35.98
Lara	289	10405	36.00	14	471	33.64	85	3225	37.94	111	3969	35.76	93	3212	34.54
Mohd Yousuf	273	9720	35.60	4	98	24.50	66	2768	41.94	105	3497	33.30	102	3456	33.88
Gilchrist	279	9619	34.47	22	764	34.73	110	3960	36.00	62	2017	32.53	107	3642	34.04
Azharuddin	308	9378	30.44	8	209	26.12	102	3163	31.01	121	3411	28.19	85	2804	32.99
de Silva	296	9284	31.36	12	529	44.08	66	2407	36.47	133	3950	29.70	97	2927	30.18
Saeed Anwar	244	8824	36.16	9	544	60.44	40	1597	39.92	135	5353	39.65	69	1873	27.14
Chanderpaul	251	8778	34.97	15	596	39.73	84	2926	34.83	85	2632	30.96	82	3222	39.29
Haynes	237	8648	36.48	5	234	46.80	49	2573	52.51	87	3042	34.97	101	3033	30.03
Atapattu	259	8529	32.93	8	247	30.88	71	2559	36.04	96	2915	30.36	92	3055	33.21
M Waugh	236	8500	36.01	8	351	43.88	113	3827	33.87	43	1614	37.53	80	3059	38.24
Gibbs	240	8094	33.72	12	668	55.67	102	3556	34.86	58	1974	34.03	80	2564	32.05
Sehwag	239	8090	33.84	11	335	30.45	82	2905	35.43	71	2270	31.97	86	2915	33.90
Gayle	223	8087	36.26	11	370	33.64	90	3072	34.13	56	2073	37.02	77	2943	38.22
Yuvraj Singh	252	8051	31.94	10	302	30.20	83	2927	35.27	68	1747	25.69	101	3377	33.44
J Miandad	218	7381	33.85	9	307	34.11	60	1976	32.93	82	2832	34.54	76	2573	33.86
Bevan	196	6912	35.26	10	325	32.50	80	2849	35.61	45	1577	35.04	71	2488	35.04
Younis Khan	232	6814	29.37	2	28	14.00	52	1796	34.54	93	2295	24.68	87	2727	31.34
Kirsten	185	6798	36.74	6	155	25.83	63	2068	32.83	59	2384	40.41	63	2346	37.24
Flower	208	6786	32.62	6	142	23.67	57	1887	33.11	76	2544	33.47	75	2354	31.39
Dhoni	184	6773	36.80	3	123	41.00	67	2659	39.69	38	1232	32.42	79	2882	36.48
Richards	167	6721	40.24	7	324	46.29	26	805	30.96	59	1995	33.81	82	3921	47.82

Since I have already talked about the Home/Away/Neutral performances in the graph section, I would only talk about the key tournament matches here. The bar for selection for the key tournament matches has been set quite high, and that is the way it should be. It can be safely concluded that these runs have all been scored in real tough situations. Ponting is the runaway leader in this classification, having scored 1163 runs at a very high RpI value of 44.73. This is a very impressive record and should not be swept under the carpet. Imagine, Ponting has scored nearly 10% of his career runs in tough tournament situations. No wonder that Australia won five major World tournaments during the past 20 years. Kallis follows next with an impressive 794 runs at 49.62. Although this has not resulted in many tournament successes. Gilchrist follows next, with 764 runs, albeit at a low RpI value of 34.73. Ganguly is next with an impressive tally of 684 runs at a more impressive 62.18. In fifth place is Gibbs, with 668 runs at a huge 55.67.

Batsman	Inns	Runs	RpI	vs AUS	vs ENG	vs IND	vs NZL	vs PAK	vs SAF	vs SLK	vs WIN	vs B/Z	vs OTH
All matches				RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)

Tendulkar	452	18426	40.76	44.0 (70)	39.3 (37)		42.7 (41)	37.7 (67)	35.1 (57)	38.9 (80)	40.3 (39)	42.6 (44)	62.2 (17)
Ponting	365	13704	37.54		42.1 (38)	36.7 (59)	39.4 (50)	31.6 (35)	39.1 (48)	36.6 (45)	32.8 (45)	42.3 (31)	39.4 (14)
Jayasuriya	433	13430	31.01	20.6 (47)	35.1 (34)	34.1 (85)	33.8 (45)	31.9 (79)	24.2 (44)		30.7 (30)	34.9 (54)	30.8 (15)
Inzamam	350	11739	33.54	29.1 (34)	33.4 (25)	37.5 (64)	30.5 (42)		26.9 (36)	39.1 (58)	32.0 (45)	39.2 (36)	14.4 (10)
Kallis	307	11498	37.45	33.2 (50)	27.7 (38)	47.3 (32)	33.0 (44)	32.9 (37)		43.3 (33)	41.6 (40)	47.8 (18)	43.1 (15)
Ganguly	300	11363	37.87	23.5 (33)	37.5 (26)		34.8 (31)	33.0 (50)	45.3 (29)	38.4 (40)	42.3 (27)	39.7 (46)	59.3 (18)
Dravid	318	10889	34.24	25.0 (39)	34.9 (29)		33.3 (31)	34.5 (55)	36.4 (36)	39.6 (42)	35.5 (38)	31.8 (34)	40.8 (14)
Jayawardene	351	10596	30.18	30.8 (48)	38.7 (31)	31.0 (71)	28.9 (32)	28.4 (55)	21.1 (35)		34.3 (18)	25.2 (45)	46.4 (16)
Sangakkara	306	10472	34.22	38.8 (44)	27.6 (27)	36.0 (62)	30.6 (30)	32.9 (39)	36.5 (34)		24.9 (15)	36.1 (39)	35.6 (16)
Lara	289	10405	36	37.2 (50)	29.4 (27)	28.6 (40)	41.1 (26)	36.9 (48)	32.9 (37)	44.9 (25)		38.4 (30)	46.5 ( 6)
Mohd Yousuf	273	9720	35.6	35.0 (29)	32.2 (24)	34.0 (42)	33.6 (27)		32.8 (34)	30.6 (42)	32.4 (24)	50.7 (38)	37.8 (13)
Gilchrist	279	9619	34.47		31.1 (35)	36.0 (45)	30.4 (41)	31.7 (24)	26.8 (42)	50.4 (30)	30.6 (24)	40.6 (25)	39.5 (13)
Azharuddin	308	9378	30.44	24.1 (41)	39.6 (23)		28.7 (39)	28.1 (59)	33.6 (33)	38.2 (48)	23.8 (42)	32.0 (21)	45.0 ( 2)
de Silva	296	9284	31.36	40.3 (36)	30.5 (15)	32.5 (55)	23.0 (36)	31.8 (73)	22.5 (27)		17.8 (27)	49.1 (22)	54.8 ( 5)
Saeed Anwar	244	8824	36.16	22.8 (30)	44.4 (11)	41.7 (48)	39.4 (32)		16.6 (24)	42.2 (52)	33.4 (16)	47.0 (21)	27.3 (10)
Chanderpaul	251	8778	34.97	35.3 (25)	39.5 (23)	32.2 (41)	26.8 (26)	32.9 (33)	40.0 (39)	34.0 (20)		35.0 (32)	44.1 (12)
Haynes	237	8648	36.48	35.3 (64)	33.9 (35)	37.7 (36)	48.3 (12)	36.8 (65)	28.2 ( 8)	34.5 (14)		55.0 ( 3)
Atapattu	259	8529	32.93	26.4 (27)	27.4 (19)	30.2 (52)	31.3 (29)	37.6 (47)	34.2 (34)		26.0 (12)	39.9 (34)	43.8 ( 5)
M Waugh	236	8500	36.01		41.7 (20)	37.5 (26)	34.9 (39)	24.9 (27)	27.1 (41)	39.4 (23)	38.0 (45)	56.7 (13)	98.5 ( 2)
Gibbs	240	8094	33.72	30.1 (44)	28.9 (22)	39.4 (27)	35.9 (29)	27.0 (22)		23.0 (27)	37.0 (29)	46.0 (27)	38.3 (13)
Sehwag	239	8090	33.84	21.7 (29)	37.3 (27)		50.3 (23)	35.7 (29)	27.6 (18)	32.3 (48)	31.0 (27)	38.6 (19)	33.8 (19)
Gayle	223	8087	36.26	28.3 (28)	39.4 (24)	36.8 (32)	33.3 (19)	31.1 (27)	32.4 (27)	24.7 (15)		44.7 (38)	58.2 (13)
Yuvraj Singh	252	8051	31.94	29.2 (33)	42.4 (28)		18.7 (27)	37.9 (33)	29.0 (21)	28.4 (49)	39.1 (21)	34.0 (25)	31.5 (15)
Fleming	269	8037	29.87	27.0 (46)	32.2 (19)	28.9 (38)		32.1 (34)	34.6 (37)	18.1 (33)	34.0 (27)	35.5 (26)	30.6 ( 9)
S Waugh	288	7569	26.28		23.8 (28)	24.8 (45)	21.3 (51)	25.1 (40)	35.9 (44)	21.6 (20)	23.2 (48)	43.6 (10)	65.5 ( 2)
Ranatunga	255	7456	29.23	26.9 (31)	26.5 (17)	28.5 (51)	25.4 (33)	29.5 (63)	27.6 (16)		34.4 (22)	35.2 (19)	50.0 ( 3)
J Miandad	218	7381	33.85	30.9 (33)	36.7 (27)	34.6 (34)	35.1 (20)		48.3 ( 3)	36.8 (31)	30.2 (64)	46.3 ( 6)

This table is ordered by career runs scored. The top 30 are shown. A smattering of RpI values above 45 are there. The ones where enough runs have been scored are Tendulkar vs Australia (44.0 but 70 innings), Gooch vs Australia (45.0), Richards vs England (47.1), Jones vs England (47.8), Kirsten vs India (53.0), Hayden vs India (51.8), Salman Butt vs India (47.2), Shoaib Malik vs India (47.1), Greenidge vs India (51.3), Sehwag vs New Zealand (50.3), Jones vs New Zealand (48.2), Kirsten vs Pakistan (43.9), Ganguly vs South Africa (45.3), Gilchrist vs Sri Lanka (50.4), Dhoni vs Sri Lanka (46.5), Kallis vs West Indies (42.3). Note the absence of high averages against Pakistan, West Indies and South Africa, strong bowling sides.

Batsman	Inns	Runs	RpI	vs AUS	vs ENG	vs IND	vs NZL	vs PAK	vs SAF	vs SLK	vs WIN	vs B/Z	vs OTH
Home matches				RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)

Tendulkar	160	6976	43.6	52.0 (30)	39.4 (16)		51.3 (16)	36.0 (15)	45.2 (22)	39.9 (27)	39.8 (17)	30.3 (12)	62.0 ( 5)
Ponting	150	5406	36.04		39.1 (14)	28.3 (18)	46.2 (19)	24.4 (20)	38.6 (22)	31.8 (25)	40.7 (19)	40.7 (10)	52.3 ( 3)
Kallis	131	5102	38.95	36.8 (26)	28.5 (19)	46.5 (11)	30.1 (15)	40.5 (11)		42.3 (17)	44.4 (18)	46.1 (10)	54.5 ( 4)
Jones	104	4069	39.12		56.1 (14)	37.8 (10)	48.7 (15)	42.8 (17)	39.8 ( 6)	57.1 (10)	18.8 (31)	54.0 ( 1)
Border	163	4068	24.96		23.0 (24)	33.1 (18)	20.1 (31)	25.4 (23)	16.0 ( 7)	38.4 (11)	24.1 (48)	22.0 ( 1)
Gilchrist	110	3960	36		28.1 (19)	30.2 (16)	31.6 (13)	26.8 (12)	27.9 (16)	58.6 (17)	28.6 ( 7)	55.3 ( 7)	60.0 ( 3)
Jayasuriya	124	3880	31.29	16.8 (14)	23.3 ( 9)	40.5 (39)	29.0 ( 9)	29.0 (16)	18.1 (11)		31.4 ( 7)	38.1 (16)	33.7 ( 3)
M Waugh	113	3827	33.87		36.5 (13)	28.2 ( 6)	21.9 (17)	19.0 (15)	32.5 (19)	42.5 (12)	45.3 (26)	44.4 ( 5)
Gibbs	102	3556	34.86	29.8 (25)	35.2 (12)	35.3 ( 7)	66.8 ( 6)	17.7 ( 7)		31.9 (12)	29.2 (15)	49.8 (11)	35.3 ( 7)
Astle	84	3448	41.05	34.9 (16)	45.4 (10)	24.6 ( 5)		31.0 ( 9)	42.7 ( 6)	32.2 (14)	50.8 (13)	60.5 (11)
Dravid	91	3406	37.43	28.3 (16)	37.2 ( 6)		38.4 ( 9)	41.7 (11)	31.4 (14)	52.5 (11)	50.9 (11)	24.2 (11)	41.0 ( 2)
Smith	86	3383	39.34	41.6 (17)	47.2 (11)	26.2 ( 9)	28.6 (10)	37.3 ( 7)		44.0 (10)	36.8 (10)	45.6 (11)	63.0 ( 1)
Jayawardene	107	3229	30.18	31.1 (14)	27.9 ( 7)	31.5 (32)	19.7 ( 9)	29.2 (14)	22.0 ( 8)		43.0 ( 7)	29.7 (13)	46.7 ( 3)
Lara	85	3225	37.94	41.6 (15)	37.9 (14)	23.8 (11)	44.2 (10)	45.2 ( 6)	33.8 (16)	55.8 ( 6)		31.4 ( 7)
S Waugh	136	3165	23.27		22.9 (15)	21.0 (14)	16.4 (22)	24.7 (24)	29.4 (19)	20.8 (12)	22.1 (27)	56.7 ( 3)
Azharuddin	102	3163	31.01	27.4 (14)	40.3 (10)		37.2 (13)	17.7 ( 7)	31.8 (13)	30.9 (18)	27.8 (19)	37.6 ( 7)	9.0 ( 1)
Boon	97	3132	32.29		26.2 ( 9)	46.1 (14)	29.6 (20)	31.9 (10)	26.4 ( 8)	36.2 (10)	26.7 (23)	51.3 ( 3)
Ganguly	75	3110	41.47	27.8 (13)	47.2 ( 5)		56.6 ( 7)	21.8 (13)	52.9 ( 7)	54.5 ( 8)	53.2 ( 8)	46.2 (12)	22.5 ( 2)
Gayle	90	3072	34.13	35.2 (13)	30.5 (11)	40.9 (12)	47.6 ( 5)	32.1 ( 9)	28.3 (17)	31.3 ( 6)		35.9 (16)	18.0 ( 1)
Sangakkara	96	3025	31.51	43.8 (12)	22.6 ( 7)	29.1 (27)	28.7 ( 6)	25.0 (15)	53.6 ( 7)		38.5 ( 6)	18.8 (12)	44.2 ( 4)
Fleming	101	2975	29.46	26.8 (20)	27.7 (10)	28.2 (12)		35.1 (14)	33.4 (10)	24.3 (14)	31.3 (10)	31.4 (11)
Clarke	86	2941	34.2		32.4 (12)	27.2 (13)	34.1 (12)	55.0 ( 9)	14.8 ( 6)	48.6 (17)	32.4 ( 7)	18.1 ( 7)	8.7 ( 3)

Tendulkar's RpI value is 52.0 against Australia at home. The other notable RpI values at home are Jones vs England (58.5), Kallis vs India (46.2), Tendulkar vs New Zealand (51.3), Jones vs Pakistan (42.8), Tendulkar vs South Africa (45.2), Gilchrist vs Sri Lanka (58.6), Mark Waugh vs West Indies (45.3) et al.

Batsman	Inns	Runs	RpI	vs AUS	vs ENG	vs IND	vs NZL	vs PAK	vs SAF	vs SLK	vs WIN	vs B/Z	vs OTH
Away matches				RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)

Ponting	129	5089	39.45		41.8 (20)	43.6 (25)	38.3 (18)	65.0 ( 3)	42.1 (18)	39.2 (15)	24.4 (18)	41.9 (11)	33.0 ( 1)
Tendulkar	146	5065	34.69	29.6 (25)	37.6 (17)		36.2 (18)	36.9 (13)	25.1 (22)	36.5 (28)	36.3 ( 6)	47.7 (14)	30.7 ( 3)
Sangakkara	123	4426	35.98	38.1 (28)	34.6 (14)	40.3 (17)	30.4 (14)	57.4 ( 7)	28.4 (18)		16.6 ( 7)	43.7 (15)	26.3 ( 3)
Jayasuriya	147	4087	27.8	23.0 (27)	41.4 (13)	22.5 (25)	31.4 (20)	30.8 (19)	20.7 (18)		42.3 ( 6)	22.6 (16)	55.0 ( 3)
Dravid	113	4044	35.79	29.5 (13)	32.4 (20)		35.4 (12)	45.7 (11)	46.5 (11)	33.4 (22)	29.7 (11)	40.4 (10)	36.0 ( 3)
Inzamam	127	3932	30.96	27.2 (22)	28.8 (13)	45.9 (12)	24.0 (23)		26.4 (17)	27.8 (13)	43.1 (13)	37.6 (11)	23.7 ( 3)
Richards	82	3921	47.82	50.1 (38)	57.4 (15)	41.4 (12)	66.5 ( 2)	35.0 (15)
Kallis	101	3708	36.71	28.5 (21)	25.9 (14)	52.7 (11)	32.9 (14)	29.5 (11)		41.9 ( 8)	46.1 (17)	59.3 ( 3)	42.5 ( 2)
Gilchrist	107	3642	34.04		38.0 (13)	37.8 (19)	34.9 (18)	63.3 ( 3)	29.7 (20)	23.9 ( 9)	30.0 (14)	35.4 ( 9)	31.5 ( 2)
Jayawardene	131	3557	27.15	31.5 (30)	46.9 (14)	24.5 (17)	14.8 (13)	23.6 (10)	22.6 (16)		27.9 ( 7)	22.6 (20)	25.8 ( 4)
Ganguly	98	3468	35.39	24.6 ( 9)	35.3 (16)		22.2 (12)	35.0 ( 8)	43.4 ( 9)	39.0 (20)	43.0 ( 6)	36.4 (15)	53.3 ( 3)
Mohd Yousuf	102	3456	33.88	24.8 (18)	29.8 (15)	36.9 (14)	29.3 (10)		35.7 (12)	19.2 (12)	22.9 ( 9)	73.2 (11)	83.0 ( 1)
Yuvraj Singh	101	3377	33.44	29.4 (11)	35.6 (14)		21.3 (11)	47.5 (12)	16.7 ( 9)	34.9 (22)	40.2 ( 9)	36.2 (12)	38.0 ( 1)
Chanderpaul	82	3222	39.29	45.5 ( 8)	45.9 ( 8)	36.2 (16)	29.4 (12)	53.7 ( 3)	42.5 (17)	21.8 ( 4)		41.8 (11)	43.0 ( 3)
Lara	93	3212	34.54	35.3 (27)	28.8 ( 9)	31.9 (14)	40.3 ( 9)	22.9 ( 9)	30.3 (13)	53.4 ( 5)		46.1 ( 7)
Dilshan	95	3089	32.52	23.9 (21)	5.3 ( 9)	45.9 (16)	26.6 ( 7)	38.9 (10)	40.7 ( 7)		24.2 ( 6)	38.2 (16)	63.0 ( 3)
M Waugh	80	3059	38.24		51.4 ( 7)	44.9 ( 8)	46.2 (18)	33.7 ( 6)	23.4 (14)	35.6 ( 9)	31.1 (16)	80.0 ( 2)
Atapattu	92	3055	33.21	34.3 (15)	34.4 ( 7)	21.8 (17)	34.0 (13)	48.2 (11)	39.4 (15)		23.8 ( 4)	27.6 ( 9)	23.0 ( 1)
Haynes	101	3033	30.03	30.7 (51)	17.4 ( 7)	26.0 (13)	32.6 ( 5)	33.5 (20)	33.3 ( 3)	37.0 ( 2)
Gayle	77	2943	38.22	13.7 (10)	42.5 (11)	46.0 (12)	31.1 (10)	14.2 ( 4)	32.3 ( 6)	20.2 ( 6)		57.2 (13)	72.2 ( 5)
de Silva	97	2927	30.18	30.2 (24)	20.8 ( 4)	32.0 (20)	23.0 (13)	28.1 (15)	22.5 (13)		16.0 ( 2)	83.8 ( 4)	48.0 ( 2)
Sehwag	86	2915	33.9	17.1 (12)	26.1 ( 7)		49.8 (12)	44.9 ( 9)	15.0 ( 7)	31.9 (19)	36.2 ( 8)	43.7 (12)

The stand-out performance is that of Richards who has an RpI value of 50.1 vs Australia in away matches. He also has 57.4 against England. Kallis averages over 50 per innings vs India. Dravid, 46.5 vs South Africa. Azharuddin has been the best visitor to Sri Lanka with an RpI of 45.3. The one who relished the West Indian attack most was Kallis with 46.1.

Batsman	Inns	Runs	RpI	vs AUS	vs ENG	vs IND	vs NZL	vs PAK	vs SAF	vs SLK	vs WIN	vs B/Z	vs OTH
Neutral matches				RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)	RpI (I)

Tendulkar	146	6385	43.73	51.7 (15)	46.2 ( 4)		39.6 ( 7)	38.6 (39)	34.9 (13)	40.5 (25)	42.4 (16)	46.7 (18)	72.9 ( 9)
Jayasuriya	162	5463	33.72	18.3 ( 6)	37.2 (12)	36.0 (21)	39.4 (16)	33.3 (44)	33.0 (15)		26.4 (17)	41.5 (22)	21.8 ( 9)
Saeed Anwar	135	5353	39.65	27.5 (10)	33.3 ( 3)	39.9 (39)	45.0 (14)		21.2 ( 9)	48.4 (38)	30.8 (12)	51.7 ( 6)	20.5 ( 4)
Inzamam	159	5133	32.28	25.9 ( 9)	21.2 ( 5)	32.3 (40)	51.5 (10)		22.1 (11)	40.3 (36)	26.1 (29)	36.9 (13)	9.2 ( 6)
Ganguly	127	4785	37.68	17.4 (11)	34.8 ( 5)		34.8 (12)	37.5 (29)	42.5 (13)	26.4 (12)	35.2 (13)	38.2 (19)	66.4 (13)
Lara	111	3969	35.76	35.0 ( 8)	1.2 ( 4)	28.9 (15)	37.6 ( 7)	39.2 (33)	35.6 ( 8)	37.2 (14)		38.0 (16)	46.5 ( 6)
de Silva	133	3950	29.7	65.0 ( 5)	29.6 ( 9)	26.7 (15)	20.5 (13)	35.2 (46)	22.1 (11)		16.4 (22)	41.5 (11)	10.0 ( 1)
Jayawardene	113	3810	33.72	24.0 ( 4)	34.9 (10)	35.2 (22)	55.6 (10)	29.6 (31)	18.3 (11)		30.2 ( 4)	24.7 (12)	55.4 ( 9)
S Afridi	160	3539	22.12	11.4 (14)	19.5 ( 8)	19.6 (37)	32.5 (15)		26.3 (13)	24.3 (34)	17.3 (19)	33.8 (12)	13.9 ( 8)
Mohd Yousuf	105	3497	33.3	47.2 ( 8)	0.0 ( 1)	40.1 (16)	28.6 (11)		15.9 (12)	35.2 (23)	38.9 (14)	26.3 ( 9)	34.6 (11)
Dravid	114	3439	30.17	13.8 (10)	47.0 ( 3)		26.1 (10)	28.4 (33)	32.5 (11)	39.0 ( 9)	28.8 (16)	31.7 (13)	42.3 ( 9)
Saleem Malik	122	3411	27.96	33.2 ( 4)	22.9 ( 8)	30.8 (39)	30.1 ( 9)		22.0 ( 5)	31.2 (26)	24.6 (24)	19.0 ( 6)	0.0 ( 1)
Azharuddin	121	3411	28.19	26.8 (13)	29.2 ( 5)		22.9 (13)	31.2 (47)	29.6 ( 8)	38.6 (12)	19.5 (15)	20.2 ( 8)
Ranatunga	116	3315	28.58	29.3 ( 3)	26.2 ( 9)	26.0 (13)	24.3 (15)	27.8 (40)	22.8 ( 8)		35.5 (17)	34.2 (10)	50.0 ( 1)
Ijaz Ahmed	122	3285	26.93	9.0 ( 5)	30.3 ( 7)	26.1 (39)	26.1 (10)		36.0 ( 7)	30.0 (28)	28.7 (22)	6.2 ( 4)
Ponting	86	3208	37.3		54.0 ( 4)	35.2 (16)	31.1 (13)	35.3 (12)	34.0 ( 8)	52.8 ( 5)	32.8 ( 8)	44.2 (10)	36.1 (10)
Haynes	87	3042	34.97	17.0 ( 3)	29.8 (13)	37.6 (15)	6.5 ( 2)	37.7 (37)	29.0 ( 2)	34.1 (12)		55.0 ( 3)
Sangakkara	87	3021	34.72	28.2 ( 4)	17.0 ( 6)	42.2 (18)	32.0 (10)	29.8 (17)	39.4 ( 9)		13.0 ( 2)	43.8 (12)	34.9 ( 9)
Atapattu	96	2915	30.36	15.0 ( 2)	14.3 ( 9)	31.3 (12)	24.9 ( 9)	34.7 (29)	25.2 (13)		4.0 ( 4)	47.3 (15)	31.7 ( 3)
J Miandad	82	2832	34.54	42.0 ( 4)	41.1 ( 9)	39.9 (13)	42.7 ( 3)			31.9 (14)	30.1 (36)	39.3 ( 3)
Fleming	96	2747	28.61	24.5 (13)	31.3 ( 6)	31.9 (12)		24.7 (15)	36.1 (15)	11.2 (13)	27.8 ( 6)	45.8 ( 8)	33.5 ( 8)
Kallis	75	2689	35.85	35.3 ( 3)	30.0 ( 5)	42.3 (10)	35.9 (15)	29.8 (15)		46.9 ( 8)	16.6 ( 5)	44.4 ( 5)	38.2 ( 9)

Tendulkar has an RpI value of 51.7 vs Australia in neutral locations. Saeed Anwar likes the New Zealand attack with an RpI value of 45.0. Inzamam has 51.5. Saeed Anwar also has an RpI value of 48.4 vs Sri Lanka.

Batsman location summary and key tournament match performances.
Batsmen run analysis vs Team - for all matches
Batsmen run analysis vs Team - for home matches
Batsmen run analysis vs Team - for away matches
Batsmen run analysis vs Team - for neutral matches

I am not going to do too much of work on the conclusions which can be drawn. This is not that type of article. Just a minimalistic set of statements.

There is a need to mention three players. I can already hear the reader or two saying "So! what's new: we know that already".

The first (amongst equals) is Tendulkar. A career which is almost the definition of consistency, not in any narrow numbers-based sense, but based on a broad definition. An RpI value exceeding 35 in almost all classifications, exceeding 40 in many classifications, no real failures (barring one: away against South Africa), tons of runs, all at an excellent strike rate. What more can one want.

The next one is Richards. Not many runs, by today's standards, but understandable. But almost all top quality runs, a very high career RpI, an away RpI which exceeds the already high career RpI, most of the runs scored away from home and all at a wonderful strike rate. An RpI value of 47+ in the key tournament matches adds to his aura.

The third one is Ponting. His overall numbers speak for themselves. Above-par performances in all locations and above-average against all countries. However what clinched this special inclusion is his tally of runs and RpI in the key tournament matches. These have contributed significantly to the team cause in winning three World Cups and two ICC Champions Trophies.

Three jewels in the crown, that is all one can say.

I can hear a few readers asking me "why not complete the batting line-up?". A valid request. So I will give you my three additional players to go with these three jewels to complete the top-6 batting line-up. Gilchrist (to open with Tendulkar), Lara (at 4/5) and Pietersen (Bevan/Hussey ???). This leaves the seventh spot for a truly great all-rounder (Shakib Al Hassan, anyone? Averages of 35+ and 28+, playing for Bangladesh).

Full post

Test Bowling: a peer analysis of spells

A statistical study analysing Test bowlers' performances relative to those of their peers

Anantha Narayanan

25-Feb-2013

When I was perusing the scorecard of the South Africa - New Zealand match which finished in a draw, I was admiring Morne Morkel's bowling performance: 6 for 23. Mentally I compared that with de Lange's spell of 0 for 77 and computed in my mind that it was "19 times better". Then it struck me that this was when compared to a single bowler. What happens if we compared to all the other bowlers. The number came to around 44. I suddenly remembered that I had done this analysis for batsmen more than a year back, based on a Unnikrishnan suggestion but had not done it for the bowlers. And I was curious to know where Morne Morkel's performance stood, over 2000 Tests.

To view the Batting Innings Peer Index article please click here.

The greatness of this analysis is that it is the purest of peer analyses possible. All conditions remain the same. Against the same set of batsmen, in almost the same conditions, identical match situation, ball conditions (somewhat) similar, weather similar, same set of umpires and so on.

Once the spark comes, the system takes over. Soon I realized that this was totally different to the Batting analysis. The differences are outlined below.

1. There is no limit to the batsman runs nor the team runs. However the total wickets cannot exceed 10. Hence there is a cap on the combined number of wickets.

2. In completed innings, the highest share of a batsman is Bannerman's 67.3. Two bowlers have captured 100% of the team wickets, 14 bowlers 90% of the team wickets, 72 bowlers 80% of the team wickets and 246 bowlers 70% of the team wickets. There is a totally different dynamics in operation here.

3. It is certain that if a batsman scored x runs, the other-batsmen would have scored y runs, whatever be the situation, if basic precautions are taken. However there are many instances in which a bowler captures x wickets and the other bowlers capture no wicket. Morkel's is the perfect example. So this has to be taken into account.

4. There are two sub-analyses possible in the bowler analysis, unlike the batting analysis. I could do a peer comparison within the innings of the bowling accuracy and bowling strike rate. These are likely to produce totally different sets of performances.

How do I take care of all the above situations. First the terminology. The ratios are called Spell Peer Factor - 1/2/3 (SPF-1/2/3).

a. As far as I am concerned there is no 0 wkt situation. If the other-bowlers have not captured a single wicket, I take that notionally as 1 so that a division is possible. I am anyhow a very practical analyst. If a batsman started his career with an unbeaten 75, as far as I am concerned, his career figures should read 1-1-75-75.00 and not as "infinity" as some misguided purists would suggest. I have been irritated by the oft-repeated phrase "no average". This method would work very well in all situations, including the two 10-wicket performances and the trigger for this analysis, Morkel's spell.

b. For the Spell Peer Factor analysis of Bowling average and strike rate, I will only consider spells of 4-wickets or more. A 4-wicket capture is a very significant bowling spell and will add weight to the results. I had initially considered 3-wickets but decided to raise the bar. I have given a list of a few significant 3-wicket performances at the end.

c. For the Spell Peer Factor analysis of Bowling accuracy, I will only consider spells of 120-balls or more. This makes eminent sense. Otherwise a bowler with a single maiden over will throw everything out of gear. Let the accurate bowlers earn their spots over a decent 150-minute spell.

d. For the Career Peer Factor determination, I would exclude spells which are wicket-less and lower than 30 balls. This will ensure inclusion of bowling spells like these: Benaud 3.4-3-0-3, Kumble 2-1-2-2, Lawson 1-0-2-1 et al. To those who say 10 overs, I can only say, 30 balls present a fair chance of a wicket. Anyhow do not waste too much time. An example: Muralitharan has only two such short fruitless spells. So the impact is minimal.

e. Since this is a peer analysis of a bowler's performance against the combined performance of his team-mates, I have decided that the rest of the wickets will include all dismissals. What matters is that the rest of the team effected these dismissals. That is all. If I had excluded run outs etc., then the ratios would be higher across the board.

The formula for determining the SPF values is quite simple and outlined below.

Bowling average for other bowlers for innings
SPF-1  = ---------------------------------------------
Bowling average for bowler for spell

where

Full post

Consistency in Test batsmen: a new look

A statistical analysis of consistency among Test batsmen

Anantha Narayanan

25-Feb-2013

This is based on an idea given by Prashanth. After giving the idea and participating in a discussion or two, he disappeared off the radar. However I thank him for providing the spark.

This follows the article on "Consistency in Test bowlers: a new look" (click here). The relevant points are explained below.

1. I had used 5 Tests as the basis for bowling. However there are many Tests in which a batsman does not get a chance to bat, because of heavy top-order batting, innings wins, big wicket wins et al. Hence I have taken 10-Innings slices as the basis for batsmen analysis. This is a reasonable number and normally covers 2-3 months of Test cricket. This is normally 5-6 Tests.
2. 10 innings means that batsman can go through a Test or two of limited opportunities to bat or non-batting because of emphatic wins etc. There will be enough opportunities within the 10-innings slice to catch up.
3. There is enough time to get over short duration loss of form.
4. To measure consistency, only runs scored will be used. The fundamental cricket dictum that batsmen should score runs and bowlers should take wickets is followed. Averages are important mainly over a career and for comparisons across players.
5. Why not average? Let us take couple of examples to understand why not. Sehwag and Younis Khan have career averages just over 50 and RpT values of around 85. In a 10-innings period, match context being comparable, Younis scores 330 at 55 and Sehwag scores 450 at 45. Who has performed closer to his career figures and for that matter, better. Certainly Sehwag, despite the lower slice average.
6. Let us not forget that we remember numbers like 974 (Bradman), 774 (Gavaskar) and 688 (Lara) rather than the averages.
7. The career slices should be non-overlapping and equal, other than the last one. Gooch's 333 should be part of one career slice only. Hence the concept of rolling number of innings is not valid.
8. 10 innings might seem arbitrary but represents a long enough career slice. It represents a long 5/6 Test series.
9. The keyword is consistency with reference to the player's own career performance levels.
10. We are not looking about high and low values but only relative to the concerned player's career figures. Over a 10-innings stretch Graeme Smith is expected to score 408 runs and Habibul Bashar is expected to score 300 runs. This will be the basis. If Smith scored 350 runs, it is a below-average performance and if Bashar scored 350 runs, it is an above-average performance.
11. Adjustment is made for the last career slice if the same is fewer than 10 innings.
12. The criteria for selection is 3000 or more Test runs. 162 batsmen qualify. It is unfortunate that a few top batsmen like Graeme Pollock and George Headley do not make the cut.
14.The Standard Deviation (SD) of the slice ratios is used to determine consistency.
15.There were suggestions that I should use more Tests/innings as the basis. I have resisted that idea mainly because I want to be hard on the players. If English batsmen had a great five- Test stint in summer and a poor five-Test sojourn in winter, I want these to be treated as two out-of-the-normal occurrences and do not want to get the 10 Tests together, get a nice, middle-level performance which papers over cracks. Same with all teams. Let us also agree. If a batsman scores 180 runs in 10 innings, it is a major cause for concern and should not be covered up by 600 runs in 10 innings before or after this barren period..

The following 5 groups are formed for purposes of determining consistency. For each career-slice of 10-innings, a ratio is formed between that concerned slice's runs and the career-average runs for 10 tests. This ratio is called SPF (Slice Performance Factor). Suppose the batsman has scored 284 runs and his 10-innings and his career-RpI value is 40, the SPF value is 0.71. If he scored 501 runs, the SPF is 1.25.

A. SPF  below 0.67:  Well below average - Falls into the inconsistent bracket.
B. SPF 0.67 - 0.90:  Below average
C. SPF 0.90 - 1.10:  Around average
D. SPF 1.10 - 1.33:  Above average
E. SPF  above 1.33:  Well above average - Falls into the inconsistent bracket.

Groups B, C and D are considered to be well within the average levels. Standard Deviation is also used to determine the consistency.

First some data tables. The complete table is available for download. The tables and graphs are presented with least comments. Let me allow the erudite readers to come out with their own comments.

Batsman	Team	Innings	Runs	Avge	RpI	Mean	StdDev	Mid3%	Grps	Grp A	Grp B	Grp C	Grp D	Grp E
									%	<67	67-90	90-110	110-133	>133
Tendulkar S.R	Ind	311	15470	55.45	49.7	0.99	0.325	68.8	32	4	10	7	5	6
Dravid R	Ind	286	13288	52.31	46.5	0.99	0.332	72.4	29	4	9	5	7	4
Ponting R.T	Aus	276	13196	53.43	47.8	1.01	0.412	60.7	28	5	5	8	4	6
Kallis J.H	Saf	257	12379	56.78	48.2	1.00	0.348	69.2	26	4	5	6	7	4
Lara B.C	Win	232	11953	52.89	51.5	0.99	0.278	75.0	24	3	8	3	7	3
Border A.R	Aus	265	11174	50.56	42.2	0.99	0.241	81.5	27	2	6	10	6	3
Waugh S.R	Aus	260	10927	51.06	42.0	1.00	0.333	61.5	26	4	7	5	4	6
Jayawardene D.P	Slk	217	10443	51.19	48.1	1.00	0.352	59.1	22	5	4	4	5	4
Gavaskar S.M	Ind	214	10122	51.12	47.3	1.00	0.350	68.2	22	4	6	4	5	3
Chanderpaul S	Win	234	9709	49.28	41.5	1.01	0.304	66.7	24	4	5	5	6	4
Sangakkara K.C	Slk	183	9382	54.87	51.3	0.99	0.425	57.9	19	4	4	5	2	4
Gooch G.A	Eng	215	8900	42.58	41.4	0.99	0.363	72.7	22	4	5	6	5	2
Javed Miandad	Pak	189	8832	52.57	46.7	1.00	0.384	57.9	19	3	6	4	1	5
Inzamam-ul-Haq	Pak	200	8830	49.61	44.1	1.00	0.376	60.0	20	5	3	3	6	3
Laxman V.V.S	Ind	225	8781	45.97	39.0	0.99	0.307	69.6	23	3	7	6	3	4
Hayden M.L	Aus	184	8626	50.74	46.9	0.99	0.385	47.4	19	6	1	6	2	4
Richards I.V.A	Win	182	8540	50.24	46.9	0.99	0.406	73.7	19	2	7	6	1	3
Stewart A.J	Eng	235	8465	39.56	36.0	1.00	0.368	66.7	24	4	6	6	4	4
Gower D.I	Eng	204	8231	44.25	40.3	0.99	0.297	85.7	21	2	7	6	5	1
Sehwag V	Ind	167	8178	50.80	49.0	0.99	0.428	52.9	17	4	5	3	1	4
Boycott G	Eng	193	8114	47.73	42.0	0.99	0.332	70.0	20	3	4	6	4	3
Smith G.C	Saf	174	8043	49.65	46.2	0.99	0.350	66.7	18	2	8	2	2	4
Sobers G.St.A	Win	160	8032	57.78	50.2	1.00	0.307	68.8	16	3	3	4	4	2
Waugh M.E	Aus	209	8029	41.82	38.4	1.00	0.283	76.2	21	2	6	6	4	3
Fleming S.P	Nzl	189	7172	40.07	37.9	1.00	0.247	84.2	19	1	8	4	4	2
Chappell G.S	Aus	151	7110	53.86	47.1	1.00	0.255	81.2	16	2	4	2	7	1
Bradman D.G	Aus	80	6996	99.94	87.5	1.00	0.272	75.0	8	1	2	1	3	1
Flower A	Zim	112	4794	51.55	42.8	0.98	0.436	66.7	12	2	4	4	0	2

To clarify the table contents. RpI mean Runs per innings. Mean is the mean of the SPF values and is close to 1.0 for all batsmen. StdDev is the Standard Deviation for all the SPF values. Mid3% is the % of the Groups B, C and D over the total number of Career Slices, which is the next column: Grps. Grp A to Grp E are self-explanatory. The complete file is available for downloading. The link is provided at the end. The first one is the core table of batsmen who have scored over 8000 runs in their Test career. In addition, Don Bradman (no need to explain), Greg Chappell (a modern great), Stephen Fleming (New Zealand) and Andy Flower (Zimbabwe) are included.

Contrary to what all of us may have perceived, Lara is remarkably consistent on this 10-innings basis. His SD of 0.278 is second only to Border amongst the top-20 batsmen. Just to confirm that this is not a fluke, look at his Mid3% which is quite high at 75.2. Again, bettered only by Border and Gower.

Consistency is determined in two ways. The first is statistical. The Standard Deviation (SD) is determined for all the ratios. Low SD values indicate consistent players and high SD values indicate inconsistent players. The usual method of using the Coefficient of Variation is not required since the means for almost all players is around 1.00. Shown below are the SD tables with the low-20 SDs indicating very consistent batsmen.

Batsman	Team	Innings	Runs	Avge	RpI	Mean	StdDev	Mid3%	Grps	Grp A	Grp B	Grp C	Grp D	Grp E
									%	<67	67-90	90-110	110-133	>133
Greig A.W	Eng	93	3599	40.44	38.7	0.99	0.171	100.0	10	0	2	5	3	0
Redpath I.R	Aus	120	4737	43.46	39.5	1.00	0.195	91.7	12	0	4	4	3	1
Ranatunga A	Slk	155	5105	35.70	32.9	1.01	0.202	93.8	16	0	7	4	4	1
Hassett A.L	Aus	69	3073	46.56	44.5	0.99	0.204	85.7	7	1	1	2	3	0
Fredericks R.C	Win	109	4334	42.49	39.8	1.00	0.205	100.0	11	0	3	4	4	0
Pietersen K.P	Eng	143	6654	49.29	46.5	1.01	0.210	86.7	15	1	3	7	3	1
Knott A.P.E	Eng	149	4389	32.75	29.5	1.00	0.228	86.7	15	1	4	5	4	1
Saeed Anwar	Pak	91	4052	45.53	44.5	1.02	0.230	100.0	10	0	4	1	5	0
Smith R.A	Eng	112	4236	43.67	37.8	1.00	0.236	83.3	12	1	4	4	2	1
Hutton L	Eng	138	6971	56.67	50.5	0.99	0.237	85.7	14	1	5	2	5	1
Wright J.G	Nzl	148	5334	37.83	36.0	1.00	0.238	80.0	15	1	6	2	4	2
Border A.R	Aus	265	11174	50.56	42.2	0.99	0.241	81.5	27	2	6	10	6	3
Ijaz Ahmed	Pak	92	3315	37.67	36.0	0.98	0.246	90.0	10	0	4	2	3	1
Fleming S.P	Nzl	189	7172	40.07	37.9	1.00	0.247	84.2	19	1	8	4	4	2
Mushtaq Mohammad	Pak	100	3643	39.17	36.4	1.00	0.248	70.0	10	1	3	2	2	2
Hunte C.C	Win	78	3245	45.07	41.6	1.00	0.248	87.5	8	0	3	3	1	1
Collingwood P.D	Eng	115	4260	40.57	37.0	0.98	0.249	91.7	12	1	3	4	4	0
Strauss A.J	Eng	167	6604	41.02	39.5	1.00	0.250	82.4	17	0	9	2	3	3
Sutcliffe H	Eng	84	4555	60.73	54.2	0.98	0.252	77.8	9	1	2	4	1	1
Chappell G.S	Aus	151	7110	53.86	47.1	1.00	0.255	81.2	16	2	4	2	7	1

Tony Greig is the surprise leader in this table, with a low SD value of 0.171. The most notable modern batsman in this table is Pietersen with an excellent SD value 0.210. Other than Pietersen there is no current batsman in this list. Like Lara. he has certainly surprised us. Maybe there is a lot of substance behind that exaggerated swagger. He talked about the many hours of practice put in while talking of his Colombo classic. Maybe that is paying off. It is also possible that unlike what one associates with him, he does not have extensive bad patches nor purple patches. I also wish he stops making silly statements.

The alternate method is common-sense-based rather than on a statistical measure. The two extreme group numbers, A and E, are considered significant departures from the career levels. The middle three group numbers are added and divided by the total number of slices to get the Mid3%. This reflects the consistency of the players. Shown below are the SD tables with the high-10 Mid3% values.

Batsman	Team	Innings	Runs	Avge	RpI	Mean	StdDev	Mid3%	Grps	Grp A	Grp B	Grp C	Grp D	Grp E
									%	<67	67-90	90-110	110-133	>133
Fredericks R.C	Win	109	4334	42.49	39.8	1.00	0.205	100.0	11	0	3	4	4	0
Saeed Anwar	Pak	91	4052	45.53	44.5	1.02	0.230	100.0	10	0	4	1	5	0
Greig A.W	Eng	93	3599	40.44	38.7	0.99	0.171	100.0	10	0	2	5	3	0
Ranatunga A	Slk	155	5105	35.70	32.9	1.01	0.202	93.8	16	0	7	4	4	1
Redpath I.R	Aus	120	4737	43.46	39.5	1.00	0.195	91.7	12	0	4	4	3	1
Collingwood P.D	Eng	115	4260	40.57	37.0	0.98	0.249	91.7	12	1	3	4	4	0
Ijaz Ahmed	Pak	92	3315	37.67	36.0	0.98	0.246	90.0	10	0	4	2	3	1
Hunte C.C	Win	78	3245	45.07	41.6	1.00	0.248	87.5	8	0	3	3	1	1
Pietersen K.P	Eng	143	6654	49.29	46.5	1.01	0.210	86.7	15	1	3	7	3	1
Knott A.P.E	Eng	149	4389	32.75	29.5	1.00	0.228	86.7	15	1	4	5	4	1
Gower D.I	Eng	204	8231	44.25	40.3	0.99	0.297	85.7	21	2	7	6	5	1
Cook A.N	Eng	135	6184	48.69	45.8	1.00	0.291	85.7	14	1	5	5	2	1
Hutton L	Eng	138	6971	56.67	50.5	0.99	0.237	85.7	14	1	5	2	5	1
Slater M.J	Aus	131	5312	42.84	40.5	0.98	0.263	85.7	14	1	4	4	4	1
Hassett A.L	Aus	69	3073	46.56	44.5	0.99	0.204	85.7	7	1	1	2	3	0

These are the batsmen with high middle three group % values indicating a high degree of consistency. In the bowler tables, there were six bowlers with 100% of their groups in the middle-3 groups. It seems like batting is slightly more difficult since there are only three batsmen. These all belong to the 70s/80s/90s. Roy Fredericks, the attacking West Indian batsman leads the three-some, followed by Saeed Anwar and Tony Greig. Collingwood is there as also Pietersen and Cook. Possible reason for England's pre-eminence.

Now for some special graphs.

Top run-scoring batsmen

The top-9 batsmen, who have crossed 10000 Test runs, are featured. It can be clearly seen that most of these batsmen do not exhibit a high level of consistency. The only exceptions seem to be Allan Border and for the first two-thirds of his career, Jayawardene.

Most consistent: Based on low SD values

As already discussed this table is led by Tony Greig. A fairly low SD of 0.171 indicates a very consistent career. This is borne out by his placement in the next graph also. However it should be noted that the lowest SD value for bowlers is a much lower 0.124. Pietersen finds a place in both the consistency graphs.

Most consistent: Based on high Middle-3-group % values

Unlike bowlers where there were six with 100% in the middle categories, amongst batsmen, there are only three: namely Fredericks, Saeed Anwar and Greig.

Least consistent: Based on high SD values

Full post

10 11 12 13 14

Showing 111 - 120 of 270

Race to IPL 2025 playoffs: GT close in on top-two finish; SRH all but out

01-May-2025 • Updated 37 mins ago•S Rajesh

Dravid: Don't want to confuse Suryavanshi with 'too many things', he should 'have some fun'

30-Apr-2025•Shashank Kishore

MI sign legspinner Raghu Sharma as replacement for injured Vignesh Puthur

01-May-2025•ESPNcricinfo staff

Hat-trickster Chahal is still making batters look silly

01-May-2025•Deivarayan Muthu

Finger injury rules Glenn Maxwell out of remainder of IPL 2025

01-May-2025•ESPNcricinfo staff

Writers

See all

Anantha Narayanan

Batting

Bowling

Batsmen CTD Avge analysis

Bowlers CTD Avge analysis

Batsmen CTD Average Analysis Table

Bowlers CTD Average Analysis Table

Bowler analysis - Summary by location / innings / quality of batsmen dismissed

Bowler analysis - All matches - by opposing country

Bowler analysis - Home matches - by opposing country

Bowler analysis - Away matches - by opposing country

Test bowler summary: by location, innings and quality of Bowlers dismissed

Test bowler summary: All matches vs other teams

Test bowler summary: Home matches vs other teams

Test bowler summary: Away matches vs other teams

Batting

Bowling

Batsmen analysis - Summary by location / innings

Batsmen analysis - All matches - by opposing country

Batsmen analysis - Home matches - by opposing country

Batsmen analysis - Away matches - by opposing country

Test batsmen summary: by location, innings and average bowling quality

Test summary: All matches vs other teams

Test summary: Home matches vs other teams

Test summary: Away matches vs other teams

Easier analysed than done !!!

Bowler analysis - Summary by location

Bowler analysis - All matches - by opposing country

Bowler analysis - Home matches - by opposing country

Bowler analysis - Away matches - by opposing country

Bowler analysis - Neutral matches - by opposing country

Batsmen analysis - Summary by location

Batsmen analysis - All matches - by opposing country

Batsmen analysis - Home matches - by opposing country

Batsmen analysis - Away matches - by opposing country

Batsmen analysis - Neutral matches - by opposing country

Top run-scoring batsmen

Most consistent: Based on low SD values

Most consistent: Based on high Middle-3-group % values

Least consistent: Based on high SD values

Race to IPL 2025 playoffs: GT close in on top-two finish; SRH all but out

Dravid: Don't want to confuse Suryavanshi with 'too many things', he should 'have some fun'

MI sign legspinner Raghu Sharma as replacement for injured Vignesh Puthur

Hat-trickster Chahal is still making batters look silly

Finger injury rules Glenn Maxwell out of remainder of IPL 2025

Matt Roller

S Rajesh

Alan Gardner

Alagappan Muthu

Mohammad Isam