Tripping up at the home stretch
A look at ODIs lost from the brink of victory
Charl Langeveldt arguably bowled the best last over in ODI history • Getty Images
This is a follow-up article to the previous one in which I had introduced a new concept of measuring the status of ODI matches using the resources available for the team. In the first article, I had looked at the wins by second batting teams, from perilous situations. In this article I will look at losses by teams batting second, from seemingly impregnable positions.
| Wicket | AllInns | ****** | FirstInns | ****** | SecondInns | ****** |
|---|---|---|---|---|---|---|
| ResUtilized | ResAvlbl | ResUtilized | ResAvlbl | ResUtilized | ResAvlbl | |
| 1 | 16.72% | 83.28% | 14.67% | 85.33% | 18.81% | 81.19% |
| 2 | 30.86% | 69.14% | 29.57% | 70.43% | 32.26% | 67.74% |
| 3 | 45.34% | 54.66% | 44.82% | 55.18% | 45.95% | 54.05% |
| 4 | 57.96% | 42.04% | 58.69% | 41.31% | 57.04% | 42.96% |
| 5 | 68.83% | 31.17% | 70.42% | 29.58% | 66.66% | 33.34% |
| 6 | 77.39% | 22.61% | 79.25% | 20.75% | 74.78% | 25.22% |
| 7 | 84.37% | 15.63% | 85.89% | 14.11% | 82.28% | 17.72% |
| 8 | 90.15% | 9.85% | 91.18% | 8.82% | 88.88% | 11.12% |
| 9 | 95.33% | 4.67% | 95.72% | 4.28% | 94.92% | 5.08% |
| 10 | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% |
This part of the second-innings analysis is quite tricky and far more complex than the more straight-forward chasing wins analysis. Earlier I was looking for the worst situations. As such it was easy to look at the fall of the wicket and determine the TRF-W (Target Resources Factor-Wickets). Compare this with TRF-B (Target Resources Factor-Balls) and select the higher one which would indicate the more critical of the two resource situations. When the team wins from such difficult situations it was easy to gauge the quality of win correctly.
This is a different ball game. Wickets are fine and the wicket resources are determined similarly. Except for a minor but very important tweak. Let us say that the first wicket fell at 120, chasing 250. The wicket resources will be calculated at 120 for 0, not 120 for 1. This means we have to consider the situation before the ball was bowled, not after. The reason is obvious. 120 for 0 is better situation than 120 for 1 and since the team has plummeted into a loss, we have to take as the base the better situation, unlike in the last analysis wherein we looked at the worse situation. This is relatively easy to understand.
Here comes the tougher part. There is no way for me to ignore the balls resource available. If I tell you that a team is at 150 for 1, chasing 250, the sentence does not convey anything. One cannot venture a comment on the team situation without knowing the balls available information. While chasing 250, 150 for 1 in 25 overs is a 95% winning situation, 150 for 1 in 35 overs is a 65% winning situation, 150 for 1 in 40 overs is a 35% winning situation and 150 for 1 in 45 overs is a 5% winning situation. So the balls available data is a must.
So much so, I am forced to exclude the 650 or so chasing wins for which balls-at-wicket-fall data is not available. I tried doing this based on assumptions on scoring rates but the results were skewed because of the dynamics between the two types of resources. When I used the scoring rate at the fall of wicket, many matches were included and I am not sure which match situation is good or worse. I do not want to say that the team lost a certain win from 150 for 1 for someone to say that I am a fool since they had only 7 overs to score 100 runs. So let us leave it at that.
Now comes the other tricky part. The scoring rate at the wicket-fall stage cannot be really taken as the scoring rate for the later part of the innings. If a team is 150 for 1 in 35 over, chasing 250, I cannot really take the actual scoring rate of 4.2 and conclude that the required rate of 6.3 is 50% harder. With nine wickets in hand, this is really a cakewalk. On the other hand, if the score at the same stage had been 150 for 4, the win is not that straight-forward. So the number of wickets in hand plays a part in getting a handle on this scoring rate which can be achieved. I have adopted the following rates. Not necessarily arbitrary since some logic has gone into it. I did not want to over-complicate at this stage of analysis to do an actual scoring rate achieved. Over thousands of matches the scoring rates for the rest of the innings, at the fall of early wickets, is likely to be in a narrow band of 4.5 to 5.0 and that does not help. I have left that for a later improvement, if feasible. I have ranged this between 7.0, which I feel is the highest achievable scoring rate over a number of overs, and 5.0. In addition, these expected rates are incremented by 10% if fewer than ten overs remain. Changing 7.0 to 7.7 will not cause much of a change. Anything above is infeasible.
Wkts-in-hand Exp-S/R
10 7.0
9 6.7
8 6.4
7 6.15
6 5.9
5 5.7
4 5.5
3 5.3
2 5.15
1 5.0
Let me now summarise the calculations.
1. Determine the wickets-resource left by looking at the number of wickets lost, just before the ball is bowled. Divide the target factor (Runs left/Target runs) by this value to arrive at the TRF-W.
2. Determine the Expected SR by looking at the above table, again just before the ball is bowled.
3. Determine the Actual SR required by dividing the number of runs by number of balls left.
4. Determine the TRF-B as the ratio between the above two scoring rates (Actual/Expected). This will have a low value (easy task) when fewer wickets have fallen and the scoring rate required is not high. It will have a high value when more wickets have fallen and the scoring rate has climbed up.
5. Determine the TRF-S (Target Resources Factor-Situation) as equivalent to 0.66667*TRF-W + 0.33333*TRF-B. Wickets are far more important to retain and this fact is recognized in this equation. These are not golden numbers. These are just weights and are driven by common-sense. I tried 0.5 and 0.5 and was not happy with quite a few of the situations. A choice of 0.75 and 0.25 lowered the impact of the Balls resource too much.
6. Select situations in which the TRF-S value is lower than 0.50. This is a very happy situation to be in. And let us not forget that the team proceeded to lose from this invincible position.
I will explain this with a few clear examples.
Let us say that a team is chasing 249. The target is 250. The score before the fall of the second wicket is 150 for 1. The Wkt-resource in front is 81.19%. The target in front is 40.0% (100/250). So the TRF-W value is 0.493 (40.00/81.19).
Now let me take two scenarios. The score of 150 for 1 has been reached in 25 overs. The Expected -RpO is 6.7 (from the table). The required RpO is 4.0 (100/25). The TRF-B value is 0.597 (4.0/6.7). The final TRF-S value is 0.66667*0.493 + 0.33333 *0.597. This works to 0.527. An excellent situation to be in. This match would just miss selection for this analysis, if the team lost.
Now let me say that the score of 150 for 1 was reached in 35 overs. The Expected-RpO remains at 6.7. The required RpO is 6.6667 (100/15). The TRF-B value is 0.995 (6.6667/6.7). The final TRF-S value is 0.66667*0.493 + 0.33333*0.995. This works to 0.660. Still a good situation to be in.
Finally let us say that the openers have dawdled and the score of 150 for 1 was reached in 41 overs. The situation changes drastically. The Exp-RpO changes to 7.37 (6.7 * 1.1). The required RpO is 11.111 (100/9). The TRF-B value is 1.507 (11.111/6.37). The final TRF-S value is 0.66667*0.493 + 0.33333*1.507. This works to 0.830. A reasonable situation only because of the 9 wickets in hand.
Change the situation to 150 for 4 in 40 overs. The TRF-W is 0.931(40.00/42.96). The TRF-B comes to 1.695(10.00/5.9). The weighted TRF-S is 1.186. The balance has clearly shifted to the fielding side. Note how delicately the whole situation is balanced and the interplay of the various factors.
Let me take a breath now.
Out of the 2100 or so matches considered, and the 1056 defending wins/ties therein, 21 matches qualify under these conditions. This time I have not gone on the TRF-S value to feature matches. I have featured the matches which had more than one such situation in the match. In other words, the losing team let go more than one opportunity. Makes these losses special, so to speak. I could get seven such matches. I have selected three more matches from the list of 21; matches that caught my eye. It can be seen that most of the matches have been lost by small margins. It seems logical. A team at 107 for no loss, chasing 209, is unlikely to lose by 50 runs.
Let me now present the table of 21 selected matches. The table is self-explanatory. The downloadable table is complete and presents all the multiple situations in the matches.
| Match Id | Score | Wkt-Res % | T1-Score | T2-Score | Target-% | TRF-W | Equation | Req-RpO | Exp-RpO | TRF-B | TRF-S |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Featured | |||||||||||
| 3080 | 188/8 | 11.1% | 190/10 | 189/10 | 1.6% | 0.141 | 3 in 46 | 0.39 | 5.66 | 0.069 | 0.117 |
| 2600 | 192/3 | 54.0% | 210/ 8 | 209/ 6 | 9.0% | 0.167 | 19 in 28 | 4.07 | 6.77 | 0.602 | 0.312 |
| 3120 | 164/8 | 11.1% | 171/10 | 165/10 | 4.7% | 0.418 | 8 in 17 | 2.82 | 5.66 | 0.498 | 0.445 |
| 1450 | 249/8 | 11.1% | 252/ 9 | 249/10 | 1.6% | 0.142 | 4 in 7 | 3.43 | 5.66 | 0.605 | 0.297 |
| 2642 | 199/4 | 43.0% | 233/ 9 | 219/10 | 15.0% | 0.348 | 35 in 57 | 3.68 | 6.49 | 0.568 | 0.421 |
| 2269 | 196/9 | 5.1% | 198/10 | 196/10 | 1.5% | 0.297 | 3 in 11 | 1.64 | 5.50 | 0.298 | 0.297 |
| 2734 | 271/4 | 43.0% | 282/ 8 | 281/ 6 | 4.2% | 0.099 | 12 in 16 | 4.50 | 6.49 | 0.693 | 0.297 |
| 2243 | 281/6 | 25.2% | 284/ 6 | 283/10 | 1.4% | 0.056 | 4 in 6 | 4.00 | 6.05 | 0.661 | 0.257 |
| 1344 | 272/4 | 43.0% | 307/ 6 | 301/10 | 11.7% | 0.272 | 36 in 39 | 5.54 | 6.49 | 0.853 | 0.466 |
| 1514 | 196/8 | 11.1% | 196/10 | 196/10 | 0.5% | 0.046 | 1 in 6 | 1.00 | 5.66 | 0.177 | 0.089 |
| Included | |||||||||||
| 1283 | 221/5 | 33.3% | 241/ 9 | 235/10 | 8.7% | 0.260 | 21 in 25 | 5.04 | 6.27 | 0.804 | 0.441 |
| 1294 | 125/0 | 100.0% | 228/ 7 | 227/ 9 | 45.4% | 0.454 | 104 in 170 | 3.67 | 7.00 | 0.524 | 0.478 |
| 1405 | 198/4 | 43.0% | 232/ 8 | 222/10 | 15.0% | 0.350 | 35 in 46 | 4.57 | 6.49 | 0.703 | 0.468 |
| 1722 | 196/3 | 54.0% | 242/ 8 | 240/10 | 19.3% | 0.358 | 47 in 59 | 4.78 | 6.77 | 0.707 | 0.474 |
| 1941 | 216/6 | 25.2% | 229/ 7 | 224/10 | 6.1% | 0.241 | 14 in 17 | 4.94 | 6.05 | 0.817 | 0.433 |
| 2520 | 235/5 | 33.3% | 257/ 8 | 252/ 9 | 8.9% | 0.267 | 23 in 26 | 5.31 | 6.27 | 0.847 | 0.460 |
| 2535 | 203/5 | 33.3% | 221/ 9 | 221/10 | 8.6% | 0.257 | 19 in 38 | 3.00 | 6.27 | 0.478 | 0.331 |
| 2682 | 301/3 | 54.0% | 340/ 6 | 340/ 7 | 11.7% | 0.217 | 40 in 38 | 6.32 | 6.77 | 0.934 | 0.456 |
| 2826 | 212/3 | 54.0% | 270/ 7 | 244/ 7 | 14.2% | 0.262 | 35 in 34 | 6.18 | 6.77 | 0.913 | 0.479 |
| 3135 | 222/6 | 25.2% | 243/10 | 225/10 | 9.0% | 0.358 | 22 in 52 | 2.54 | 6.05 | 0.420 | 0.378 |
| 3215 | 155/3 | 54.0% | 200/10 | 174/10 | 22.9% | 0.423 | 46 in 73 | 3.78 | 6.15 | 0.615 | 0.487 |
Let us now see the featured matches now. The sequence is rather arbitrary. In general I have shown the matches where the teams lost despite many chances first.
1. ODI # 3080. South Africa vs. India.
Played on 15 January 2011 at New Wanderers Stadium, Johannesburg.
India won by 1 run. Mom: Munaf Patel
India: 190 all out in 47.2 overs
Yuvraj Singh 53 ( 68)
South Africa: 189 all out in 43.0 overs
GC Smith 77 ( 98)
MM Patel 8.0 0 29 4
This is arguably amongst the most astonishing matches ever played. The more I see what happened in the match, the more I feel like I have been caught in the eye of a typhoon, going round and round. India, batting first, at the Wanderers, reached a very ordinary total of 190. South African, despite losing the first wicket early, were coasting. Starting at 152/4 (the fifth wicket falling at 25.2), South Africa were looking almost certain winners at 160/5, 163/6, 177/7, 188/8 and 189/9. On each of these situations their TRF-S was well below 0.50. Surprisingly the best situations were at 188/8 (0.117) and 189/9 (0.155). But they lost the match by 1 run. How? It is a question they might still be trying to find an answer for. A combination of panic, good bowling by Munaf Patel and good fielding contributed to the disaster.
2. ODI # 2600. Ireland vs. Netherlands.
Played on 11 July 2007 at Civil Service Cricket Club, Stormont, Belfast.
Ireland won by 1 run. Mom: Kevin O'Brien
Ireland: 210 for 8 wkt(s) in 50.0 overs
EJG Morgan 51 (112)
D Langford-Smith 31*( 13)
Netherlands: 209 for 6 wkt(s) in 50.0 overs
Mudassar Bukhari 71 (114)
This was an equally amazing match played between two talented Associate teams. Ireland scored a moderate 210 for 8. Netherlands looked certain to win when they were 138/1, 159/2, 192/3 (0.312) and 192/4. They lost a wicket at each of these situations and were finally at 198 for 5 (0.419), needing 13 to win in 14 balls. They managed to fall couple of runs short. The scores of the later four batsmen, 15 in 24, 10 in 15, 2 in 5 and 5 in 7, tell the complete story.
3. ODI # 3120. England vs. South Africa.
Played on 6 March 2011 at MA Chidambaram Stadium, Chepauk, Chennai.
England won by 6 runs. Mom: Bopara R.S.
England: 171 all out in 45.4 overs
IJL Trott 52 ( 94)
RS Bopara 60 ( 98)
South Africa: 165 all out in 47.4 overs
HM Amla 42 ( 51)
SCJ Broad 6.4 0 15 4
This match is fresh in everyone's memory. World Cup 2011, and the unfancied England played the eternal bridesmaids, South Africa. This time the South Africans had it all figured out. England could not make head or tail of Imran Tahir and Robin Peterson and were dismissed for 171. At 124/3, 160/7 and 164/8(0.445), South Africa looked very clear favourites to win. Although the numbers may not reveal this, they needed 48 in 108 balls with seven wickets at the first-mentioned situation. That was their best chance. But they let go of all these chances and were finally dismissed for 165, six runs short. Stuart Broad was unplayable at the end. But the real culprit was Peterson who scored 3 in 16 balls.
4. ODI # 1450. India vs. Zimbabwe.
Played on 19 May 1999 at Grace Road, Leicester.
Zimbabwe won by 3 runs. Mom: Grant Flower
Zimbabwe: 252 for 9 wkt(s) in 50.0 overs
A Flower 68*( 85)
India: 249 all out in 45.0 overs
S Ramesh 55 ( 77)
HH Streak 9.0 0 36 3
HK Olonga 4.0 0 22 3
This match was during the 1999 World Cup. Zimbabwe posted a very competitive total of 252. This was not like the earlier referred matches in which the losing team had established their ascendancy early in the innings. India struggled at the start and was behind the game at 103 for 5. Then they recovered and reached recovered and were very comfortably placed at 246 for 7, requiring only 7 runs in 10 balls with three wickets left. Then Henry Olonga struck. Even then, at 249 for 8 (0.297) and 249 for 9, they were well placed to win. But then fell 3 runs short. This was an amazing result considering the difference in strength between the two teams.
5. ODI # 2642. Pakistan vs. South Africa.
Played on 29 October 2007 at Gaddafi Stadium, Lahore.
South Africa won by 14 runs. Mom: Makhya Ntini
South Africa: 233 for 9 wkt(s) in 50.0 overs
HH Gibbs 54 ( 61)
JH Kallis 86 (130)
Pakistan: 219 all out in 46.3 overs
Younis Khan 58 ( 65)
Mohammad Yousuf 53 ( 88)
M Ntini 9.0 0 61 4
JA Morkel 8.3 0 44 4
South Africa set Pakistan a fair target of 234. They were well ahead of the game at 199 for 4 (0.421), requiring 35 in 57. Then the score became 209 for 5 and then 219 for 7. Even then 15 in 24 looked easy. At this point Albie Morkel captured three wickets in four balls and sent Pakistan crashing to a 14-run defeat. That too, at home.
6. ODI # 2269. Africa XI vs. Asia XI.
Played on 17 August 2005 at SuperSport Park, Centurion.
Africa XI won by 2 runs. Mom: Ashwell Prince
Africa XI: 198 all out in 44.3 overs
AG Prince 78*(113)
Asia XI: 196 all out in 48.1 overs
Abdul Razzaq 38 ( 77)
SM Pollock 10.0 1 32 3
JH Kallis 10.0 2 42 3
This time it is an Africa XI. Another low score of 198 against the motley collection of players known as Asia XI. Chasing 199, Asia XI lost wickets regularly and were down in the dumps at 59 for 4 and 96 for 7. Then they recovered and were at 193 for 8, requiring 6 in 16. Soon they lost the ninth wicket and were 196 for 9 (0.297), needing only 3 runs. But fell 2 runs short.
7. ODI # 2734. West Indies vs. Australia.
Played on 4 July 2008 at Warner Park, Basseterre, St Kitts.
Australia won by 1 run. Mom: Andrew Symonds
Australia: 282 for 8 wkt(s) in 50.0 overs
A Symonds 87 ( 78)
DJ Hussey 50 ( 51)
West Indies: 281 for 6 wkt(s) in 50.0 overs
CH Gayle 92 ( 92)
RR Sarwan 63 ( 79)
S Chanderpaul 53 ( 71)
B Lee 10.0 0 64 3
For a change this was a close result in a big-scoring match. Australia scored a very competitive total of 282 for 8. At 138 for 1 and 188 for 2 West Indies were quite comfortably ahead. Even when the score was 247 for 3, the situation was very good. However the best situation was at 271 for 4, West Indies requiring only 12 in 16 balls. They were very well placed at a TRF-S value of 0.297. But West Indies floundered inexplicably and scored only 10 runs in the next 12 balls. They ended just a run short.
8. ODI # 2243. West Indies vs. South Africa.
Played on 11 May 2005 at Kensington Oval, Bridgetown, Barbados.
South Africa won by 1 run. Mom: Charl Langeveldt
South Africa: 284 for 6 wkt(s) in 50.0 overs
HH Dippenaar 123 (129)
JH Kallis 87 (109)
West Indies: 283 all out in 49.5 overs
CH Gayle 132 (152)
A Nel 10.0 0 42 3
CK Langeveldt 9.5 0 62 5
This was a most extraordinary match, won by a single bowler, like Albie Morkel did against Pakistan. How often have South Africa figured in these matches on the other side of the fence? South Africa put up a most impressive total of 283. Aided by a top-class century from Chris Gayle, but still losing wickets steadily, West Indies were 281 for 6 (0.257) requiring just 4 runs for a win. Then they were 283 for 6, requiring only two runs. Charl Langeveldt produced, arguably, the best last over in ODI history and claimed a hat-trick, letting the South Africans win by 1 run. A hat-trick was badly needed and he produced it.
9. ODI # 1344. Sri Lanka vs. India.
Played on 7 July 1998 at R. Premadasa Stadium, Colombo.
India won by 6 runs. Mom: Sachin Tendulkar
India: 307 for 6 wkt(s) in 50.0 overs
SC Ganguly 109 (136)
SR Tendulkar 128 (131)
A Jadeja 25 ( 15)
Sri Lanka: 301 all out in 49.3 overs
PA de Silva 105 ( 94)
AB Agarkar 10.0 0 53 4
This is the only featured match that produced two scores of over 300 runs. Helped by two centuries at the top, India scored 307 and looked comfortable winners. But Sri Lanka never gave up. Their best position was 272 for 4 (0.466). They needed only 36 in 39 balls. Reasonably comfortable position. However they started losing wickets regularly and fell 6 runs short. The last 5 batsmen scored 12 runs in 25 balls, a woeful effort indeed.
This match is very similar to ODI 2932, in which the mammoth Indian total of 414 was almost chased down by Sri Lanka, who fell 3 runs short. The only reason it does not get into this list is because at 401 for 6, chasing 414, the balls situation, 14 in 10, was not that favourable to Sri Lanka. And it proved difficult. They could not make those runs.
10. ODI # 1514. Pakistan vs. Sri Lanka.
Played on 15 October 1999 at Sharjah C.A. Stadium.
Match tied. Mom: Abdul Razzaq.
Pakistan: 196 all out in 49.4 overs
Mohammad Yousuf 48 ( 90)
Sri Lanka: 196 all out in 49.1 overs
RS Kaluwitharana 75 (108)
RP Arnold 61 ( 93)
Wasim Akram 10.0 1 38 3
Abdul Razzaq 9.1 2 31 5
This is the only tied match featured here. Pakistan were dismissed for 196. Chasing the relatively modest target of 197, Sri Lanka looked certain winners at various positions such as 157/1, 173/3, 174/3, 177/4, 186/5, 186/6, 194/7 and finally 196/8. The TRF-R values were way under 0.500 in all these situations. Interestingly the best situation was at 196 for 8 (0.089) since the scores were level and 1 run was needed. However, they lost both wickets and the match was tied. The batsmen at positions 4-11 scored 25 runs in 71 balls. These included Aravinda de Silva, Sanath Jayasuriya, Mahela Jayawardene and Chamara Silva, all recognised batsmen.
I have created a document file with details of all matches in which the TRF-S values were below 0.50. This includes multiple occurrences within the same match. To download/view the document, please CLICK HERE.
Anantha Narayanan has written for ESPNcricinfo and CastrolCricket and worked with a number of companies on their cricket performance ratings-related systems