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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
Bowling average Innings score - Extras - Runs conceded by bowler for other bowlers = ------------------------------------------------ for innings Innings wkts - Bowler wkts (If 0, taken as 1)
For SPF-2, use RpO (Runs per Over) instead of Bowling Average and For SPF-3, use BpW (Balls per wkt) instead of Bowling Average
Let me anticipate some comments. It really does not matter if the batsmen dismissed by the bowler are lower in the order and the other dismissals are top order. The bowler might have got the new ball and the other might not have. On the other hand the bowler might have got an old ball with reverse swing and the others might not have. The bowler might have bowled more to Richards. And so on. Let us forget all these factors. What we are looking at is a simple peer comparison within an innings, that is all. Instead of finding faults, let us draw some insights. I will be using this measure in my bowling spells rating work.
Readers have a knack of converting every analysis into a best bowler/batsman type of article. Please do not send comments such as Steyn is better than Morkel, Wasim is better than Sarfraz, Ambrose/Marshall are better than Lawson and so on. Possibly true, but not relevant here. Please read and understand the article which can be defined as "peer comparison within the same innings".
Now for the tables. There are four tables. Three cover the individual Spell Peer Factor values, one each for the Bowling average, Runs per over and Bowling strike rate. The last table highlights the Career Peer Factor values. As always, the top 25 or so entries are shown here and the full table, now standardized in the form of Excel sheets is available for downloading.
|851||1979||Ind||Eng||0||427.0||427.0||Kapil Dev N||5||146||29.20||14.62|
Where does the trigger spell of Morne Morkel stand. Lo and behold! It is at the top. I had a sneaking suspicion that 44 was not a number which could be beaten. Imagine a bowler performing nearly 50 times better than the rest of his team-mates including Philander (avge ~ 14) and Steyn (avge ~ 23). Then, as expected, comes Lohmann, not surprising since he specialized in spells of 8/9 for nothing. Afterwards comes a spell of recent vintage. Lawson picking up the last six Bangladeshi wickets for just 3 runs and an SPF-1 value of 36.50. However the other bowlers also fared well, capturing 4 for 73. Then comes the very well-known Michael Clarke special, for an SPF-1 value of 32.0, 6 for 9 to move India from 181 for 4 to 205 all out. This match was incidentally played on a muddy lane around Wankhede Stadium. The top-5 are rounded off with a non-freakish spell by Muralitharan: the first nine Zimbabwe wickets for 51 against the 10th wicket by his colleagues for 161, an SPF-1 value of 28.41. Laker's 10-wicket spell finds a place in the top-10 with an SPF-1 value of 25.66. Hadlee's master class against Australia is there with an SPF-1 value of 19.21. Kumble's 10-wicket haul has a SPF-1 value of 14.05.
This table is based on the RpO value of the spell and the RpO value of the other bowlers. The spell has to be a minimum of 120 balls to be considered. At the top, by a few kilometres, is Nadkarni's famous spell. 32 overs of rather innocuous on-a-rupee-coin bowling, resulting in 5 singles. The other 187 balls were mostly padded away. Based on today's rules, Nadkarni would have got 80 wickets, and another 20 on DRS. Barrington and Bolus: I still get nightmares since I heard the whole innings on radio. The ratios here are smaller but Nadkarni leads with an SPF-2 value of 12.06. The second effort, also by an Indian left-arm spinner, cheese to chalk of Nadkarni, yielded an SPF-2 ratio of 4.86, just over a third of Nadkarni's figure. At least Maninder Singh was more attacking and picked up 3 wickets. It is possible that a two might have been scored off him. Nigel Mann and Compton (surprise !!!) take the next two places. Compton bowled 16 8-ball overs for 11 runs. Then Nadkarni appears again: this time for a spell of 34-24-24-1. It would be of interest to note that Nadkarni's spell in the other innings was an attacking 52.4-38-43-4. What does one say of a match performance of 86.4-62-67-5. One just gives up making sense. Muralitharan, Oram and Ambrose appear in this collection of ancients.
This is the peer comparison of the bowler strike rates. McDermott is the unlikely bowler at the top. He was in the news recently because of the way he has re-vitalized Australian bowling. His 8 for 141 was off 36 overs out of a huge score of 482 in 142 overs. McDermott's SPF-3 value is a huge 23.33. I like this since this is not one of those freak 5 for 2 type spells. Morkel's recent spell is quite close in the second place. His strike rate was 16.7 compared to the team strike rate of 384 leading to an SPF-3 value of 23.04. Two earlier acquaintances, Hadlee and Lohmann appear next. Then follows a a nine-wicket spell of Noreiga in the historic series-winning match, also happened to be Gavaskar's debut Test. Then comes Laker's ten-wicket spell. Kumble's 10-wicket spell comes at the end of the section with an SPF-3 value of 12.83.
|Bowler||Team||Debut||Tests||Wkts||Avge||Spells||TotPts||C P F|
This is a career summation and averaging of of all qualifying SPF-1 values. The cut-off for this table is 50 such spells. As an honorary invitee I have included Barnes who had 50 spells but one was excluded since that was 4-1-18-0. If anyone deserved it, Barnes is the bowler. Muralitharan and Barnes are the only bowlers who exceeded 2.0 in the Career Peer Factor value. Think of the significance of this. It has always been said that Muralitharan got his wickets because he played in a weak team. Fair enough. But, gentlemen, this is a peer comparison of performance measures. Not just did he get more wickets but got those at half the average of his peer bowlers. So let us give the great men, Muralitharan in particular, the credit. Just for a comparison, Bradman's figure for this value was 3.348.
Hadlee is next with an excellent career ratio of 1.87. A bow to one of the indisputable all-time greats. Then comes Imran Khan. He does not lose out because of the Tests he did not bowl in. A well-deserved fourth place at 1.73. Then comes Laker, not surprising because he out-performed his colleagues by wide margins. Finally a surprise sixth position for Fraser and a surprise seventh position for Morkel. I am happy to see Streak from Zimbabwe in the top-10. Then another surprise, B.R.Taylor of New Zealand. Tayfield, the incomparable South African spinner rounds off the top-10.
It is wonderful to see the classic leg-spinner, Subash Gupte topping the Indian bowlers, and nice also that Vinoo Mankad is the next Indian bowler. Colin Croft and Alan Davidson are the leading West Indian and Australian bowlers.
Finally I am back to my favourite BCG charts for plotting the two contrasting measures, Career RpO and BpW Peer Factor values, which are the two components of the Batting average. The BCG chart will give a good idea of the way the accuracy and strike rate have interacted for the top bowlers. Remember these are not absolute values but peer values within the same innings. The top right quadrant houses bowlers who had above average figures for both accuracy and strike rate. The bottom left quadrant houses bowlers who had below average figures for both accuracy and strike rate. The top left quadrant houses bowlers who were strike rate centric. Generally the pace bowlers. The bottom right quadrant houses bowlers who were accuracy centric. Generally the spinners. But not written in stone. There are different types of bowlers mixed in all quadrants.
The top right group is the elite one and has six bowlers. Barnes has justified his special inclusion and could be termed the leader in this quadrant. Muralitharan is right up there, very close to Barnes. Imran Khan is also well placed. Hadlee has an excellent strike rate index value and just about manages to be in the right of the RpO middle line. Ambrose does it the other way. Pretty good on the RpO front and just about manages to be above the middle BpW line. But the real surprise is Kumble. He is also comfortably placed in the elite quadrant. Let us not forget that this is a peer index.
The bottom left group houses bowlers who have not been that great when compared to their compatriots. There are four bowlers in this quadrant. Lee has only a RpO peer factor of around 0.82, indicating that he has been a philanthropist when it comes to runs. He has a reasonable strike rate index. Hoggard, Benaud and Anderson are the other three bowlers here, possibly indicating that these bowlers played with other good bowlers through their career.
In the other two quadrants, the notable bowlers are Steyn, whose BpW peer factor value is simply amazing. Close to him in this regard are Donald and McDermott. Gibbs is there at the best RpO peer factor level. Bedi and Vettori are also there. Underwood, Garner and Walsh are the closest to getting into the elite quadrant. McKenzie, Kapil Dev and Lillee are the bowlers who are pushing to the elite quadrant from the BpW side.
The x-axis and y-axis lines are drawn at the median value positions. If the number of entries are counted it can be seen that both the lines divide the total population into 21-20 splits. The non-centric positioning of the two lines is because these are the top bowlers and their Peer Factor values run in the following non-centric manner. For lesser bowlers, the median values are likely to be closer to 1.00.
Career RpO Peer Factor: 1.38 to 0.83 (Median 1.10) Career BpW Peer Factor: 1.77 to 0.93 (Median 1.23)
To download/view the Excel sheet containing the three Spell Peer Factor Tables please click/right-click here.
To download/view the Excel sheet containing the Career Peer factor tables please click/right-click here.
Anantha Narayanan has written for ESPNcricinfo and CastrolCricket and worked with a number of companies on their cricket performance ratings-related systemsFeeds: Anantha Narayanan
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Anantha spent the first half of his four-decade working career with corporates like IBM, Shaw Wallace, NCR, Sime Darby and the Spinneys group in IT-related positions. In the second half, he has worked on cricket simulation, ratings, data mining, analysis and writing, amongst other things. He was the creator of the Wisden 100 lists, released in 2001. He has written for ESPNcricinfo and CastrolCricket, and worked extensively with Maruti Motors, Idea Cellular and Castrol on their performance ratings-related systems. He is an armchair connoisseur of most sports. His other passion is tennis, and he thinks Roger Federer is the greatest sportsman to have walked on earth.