January 16, 2014

How does one measure T20 proficiency?

The traditional data range of cricket statistics is far too narrow and its focus sometimes irrelevant to the requirements of the shortest format
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We know Aaron Finch is a good T20 batsman but it's time the number crunchers measured factors like
We know Aaron Finch is a good T20 batsman but it's time the number crunchers measured factors like "run-out involvements" and "strike ratio" © Getty Images

Casting a critical eye over Network Ten's new Big Bash League coverage this summer, a number of things have struck me. First and foremost, the broadcast is a refreshing change-up from Channel 9's now relentless pursuit of vaudeville. Recent Australian greats Ricky Ponting and Adam Gilchrist have made a winning transition to the commentary box, and the dry, unscripted additions of Viv Richards have added something unique. In most senses including local ratings it has been a winner.

Still, and this isn't just down to Ten or any other T20 broadcast network, I think we're all missing a trick as fans. T20 statistics, based almost entirely on the same parameters as traditional cricket, are all but useless. Every time the broadcaster flashes them up I'm taken by how little they tell us about the shortest format. They also add nothing to the discussion surrounding individual players.

It's almost perverse really, that a game with such a long-standing fetish for numbers has done so little to adapt and shift the debate around short-form cricket. It puzzles me slightly that the rapid enthusiasts of T20 aren't taking steps to change this, because it could be central to building appeal for the game among traditionalists that goes beyond mere entertainment.

Mostly we're left to debate the merits of technical innovation in a purely physical sense: laps, chips, ramps, slow bouncers et al. The fact is - and I would call myself an engaged agnostic when it comes to T20 cricket - we should be doing better in measuring what it is that makes a T20 player great, good, average or otherwise.

How is there not yet a standardised, widely adopted stat column for dot-ball percentage for bowlers and batsmen? In many instances it would tell you more than strike rates and economy rates do. Two batsmen may score 15 runs from 15 deliveries, but one of them might have hit two sixes and then otherwise have robbed their partner of strike, killing the momentum of the innings and potentially opening the door for the bowling side.

What's clear is that the traditional data range of cricket statistics that we see most commonly is far too narrow and its focus sometimes irrelevant to the requirements of T20. Teams and coaches know this. It's why legions of analysts and number crunchers have slowly migrated into the management ranks of top teams around the world. But their intention is to decode player data to provide performance analysis and a winning edge, not to enhance the knowledge of fans.

Cricket fandom boasts a fair portion of self-described nerds but too few of the statistically minded ones are really drilling into this stuff. All of us are guilty in that sense. Maybe in the relentless procession of cricket we just don't stop long enough to consider it.

Here's a superficial example: the commonly available stats for the current Big Bash season will tell you that two of the better opening batsmen, Aaron Finch and Usman Khawaja, average 52.40 and 54.00 respectively. Finch edges Khawaja on strike rate, 148.02 to 118.24. Finch has 23 fours and 12 sixes, while Khawaja has 20 fours and has not yet cleared the rope in two fewer games.

Beyond highly subjective criteria, which basically boils down to aesthetic preferences, prejudices and an often ill-defined notion of "X factor" that pervades T20 media analysis, the average stat sheet or telecast graphic doesn't tell us much that could objectively establish who is the better opening batsman. That is always the case of cricket statistics to some degree, but the brevity of a T20 game makes it the most suitable for quantification through naked numbers than any of the three formats. The permutations and infinite variables of 50-over and Test cricket can make a liar of even the most brilliant statistician.

Would a stat as simple as dot-ball percentage reveal the chinks in Khawaja's armour that make him seem less effective to the naked eye? If that stat was segregated into ten-ball intervals it certainly would tell us he's a slow starter. "Run-out involvements" might also be of interest, as well as "strike ratio", telling us which of them turns the strike over regularly and who leaves their partner to watch on anxiously in search of a single or to calcify at the other end.

As ever, baseball can show cricket a thing or two. There are many fans of that sport who would argue that the incursion of advanced statistics and sabermetrics has taken a little joy and romance away from the game, but it's also been a boon for numbers nerds and analysts wanting to establish the cold, hard truth about a given player's capabilities. Yet instead of providing cricket and other sports with a "lightbulb" moment, the Moneyball revolution really hasn't done much more for cricket than prop up lazy journalistic analogies, a great deal of which entirely miss the point of the book and the tale it tells.

T20 cricket still awaits its own breakthrough. For a game so endlessly played, watched and analysed, we're still ridiculously low on the capture and dissemination of data that could help us understand it with greater clarity. Ed Cowan hinted at a stats revolution on these very pages two years ago, but still we plug away with our averages, our strike rates and our six counts.

T20 cricket is well past its tenth birthday now. We really should start treating it like a grown-up.

Russell Jackson is a cricket lover who blogs about sports in the present and nostalgic tense for the Guardian and the Wasted Afternoons. He tweets here

Comments have now been closed for this article

  • MinusZero on January 17, 2014, 6:52 GMT

    Another interesting way to see the value of a player is their performance in wins vs losses. Take for example Ponting and Watson in tests.

    Watson averages 36.33 with the bat. In wins 37.39 and losses 30.44. With the ball, 31.83 overall, wins 27.64, losses 30.30. Ponting averaged 51.85. Wins 59.46, losses 32.83. Tendulkar and Kallis were similar to ponting.

    I can already hear people saying.."but Watson is an allrounder".

    True, but the individual performances in wins vs losses show the value of that player to the team's performance.

    The stats show that Ponting, Kallis and Tendulkar had a big influence on wins and if they didnt perform, they lost.

    It also shows that some players just skate by with mediocre performances that make little difference to wins and losses based on how they perform.

  • GeckoGarriock on January 17, 2014, 6:29 GMT

    @alstar I think you are certainly on to something there. So by my reckoning, someone that averages 54 at a S/R of 148 would score those 54 runs in an average of ~36.5 balls. The expected value of those 36.5 balls would be 45.625 runs (36.5*(150/120)), therefore Finch would be producing on average a +8.325 runs per innings. This would have to be adjusted for the average extras per innings, but could certainly be a worthwhile statistic.

    Averages here would become useful, but would need to involve an adjustment removing not out scores to show how many runs a batsman contibutes to every innings they play as wickets in T20 cricket are not nearly as crucial as they are in longer forms.

    I think this is certainly a possible way of combining two statistics from yesteryear to form a statistic relevant to modern day cricket.

  • drinks.break on January 17, 2014, 4:08 GMT

    Here are two other possible batting measures:

    1. Value to the team: % of team score / % of innings balls faced (min. 10 balls).

    2. Average match situation score value (this is more complicated, and the suggested numbers are just gut feeling ... real statisticians could surely work out some more objective measures):

    Each match situation score value (MSSV) = runs x wicket down constant (WDC) x over introduced constant (OIC).

    The theory is that the openers should score more runs than anyone else - each wicket down increases the value of the runs scored (WDC). But, the later a batsman is introduced (in terms of overs), the value of his runs decreases (OIC). Runs earlier in the game provide a platform for a large score; runs late in the game may at best make a poor score respectable.

    My gut feeling constants are: WDC (proportionally increasing values): 0 = 1 1 = 1.1 2 = 1.3 3 = 1.6 4 = 2.0 5 = 2.5 6 = 3.1 7 = 3.8 8 = 4.6 9 = 5.5

    OIC: 0-25% = 1 26-50% = 0.8 51-75% = 0.6 76-100% = 0.4

  • drinks.break on January 17, 2014, 3:04 GMT

    It's not like some of the old measures aren't still useful. I'd have to question the value of Ave in such a short format, but SR is still significant ... and I don't think it really makes much difference how they got the runs - by running lots of 2s (like M Hussey) or by hitting lots of boundaries (like Finch) ... the end result is what counts.

  • alstar2281 on January 17, 2014, 2:42 GMT

    Calculate a players value by the amount of extra runs they contribute to a team's score against Ave. In This BBL the Ave Team score is 150 (based on all the individual averages). Thus if you take into account the number of runs scored and the speed (Finch scores faster the Kwajah thus the hypothetical ave score of the other 9wkts is higher as they have longer to bat) at which they are scored I calculate that Finch is worth an extra 29 runs to his teams innings against Kwajahs 15. Maxwell is leading on 48. The same can be applied to the bowlers by calculating the average team score and then the bowlers contribution based on wickets & economy in reducing that score. Similar to Baseball's WAR or WAA statistic, except measured in runs and not wins.

  • on January 17, 2014, 1:14 GMT

    We need fielding stats too. Distance covered (using GPS), fumbles, runs conceded, runs saved, boundary runs conceded, shies at stumps, direct hits, dropped catches, assists etc. Should be recorded for each player but maybe more useful as a whole team or outfielder/infielder/catcher/keeper set of splits.

  • woody3 on January 16, 2014, 17:54 GMT

    Finch edges srike rate? rounding about 148 to 118? Thats a runs off the bat score of 178 to 142 for twenty overs. Thats not even remotely close, anyone with half a brain can see Finch is a far better 20 over bat, even if he scored all his runs in sixes and dots and the other guy got them all in singles. The only batsmen you cant afford is a slow scoring bat who cant rotate strike, frankly a destructive six hitter not good at strike rotation like gayle you want the other guy to get singles not him, unless you are the fielding captain.

  • ThinkingCricket on January 16, 2014, 16:31 GMT

    py0alb: Smart idea, but doesn't work, because the odds incorporate expectations. I.E. the odds when Dhoni needs 45 off 19 balls are ridiculously low for the situation because the market already expects him to do it....so change in odds only calculates how much better the player is then his market perception, and not his overall value.

  • on January 16, 2014, 16:11 GMT

    @GeckoGariack Nailed it, for me. You could probably use the expected value of one ball (following the Tweedie distribution ) as a measure for both batsmen and bowlers. Loosely, it would describe the proportion of scoring shots to balls faced/bowled * the value of the shot. The only thing missing, then, would be the expected duration of an innings. Batsman X scores at x per ball over y balls

  • BobFleming on January 16, 2014, 13:16 GMT

    @ThinkingCricket - It could well have been Gilchrist (I'm cribbing from memory). Thanks for the correction. And I agree that it isn't perfect, although I would suggest that any player averaging 9 (as per your example) across a career would be fairly obviously dismissed from consideration. But if it were used to compare established batting specialists/all-rounders, I still think there is some merit to it. Perhaps it would be more useful in ODIs, where the accumulating - high-average - top order player and the less reliable middle order destructive types can both add value.

  • MinusZero on January 17, 2014, 6:52 GMT

    Another interesting way to see the value of a player is their performance in wins vs losses. Take for example Ponting and Watson in tests.

    Watson averages 36.33 with the bat. In wins 37.39 and losses 30.44. With the ball, 31.83 overall, wins 27.64, losses 30.30. Ponting averaged 51.85. Wins 59.46, losses 32.83. Tendulkar and Kallis were similar to ponting.

    I can already hear people saying.."but Watson is an allrounder".

    True, but the individual performances in wins vs losses show the value of that player to the team's performance.

    The stats show that Ponting, Kallis and Tendulkar had a big influence on wins and if they didnt perform, they lost.

    It also shows that some players just skate by with mediocre performances that make little difference to wins and losses based on how they perform.

  • GeckoGarriock on January 17, 2014, 6:29 GMT

    @alstar I think you are certainly on to something there. So by my reckoning, someone that averages 54 at a S/R of 148 would score those 54 runs in an average of ~36.5 balls. The expected value of those 36.5 balls would be 45.625 runs (36.5*(150/120)), therefore Finch would be producing on average a +8.325 runs per innings. This would have to be adjusted for the average extras per innings, but could certainly be a worthwhile statistic.

    Averages here would become useful, but would need to involve an adjustment removing not out scores to show how many runs a batsman contibutes to every innings they play as wickets in T20 cricket are not nearly as crucial as they are in longer forms.

    I think this is certainly a possible way of combining two statistics from yesteryear to form a statistic relevant to modern day cricket.

  • drinks.break on January 17, 2014, 4:08 GMT

    Here are two other possible batting measures:

    1. Value to the team: % of team score / % of innings balls faced (min. 10 balls).

    2. Average match situation score value (this is more complicated, and the suggested numbers are just gut feeling ... real statisticians could surely work out some more objective measures):

    Each match situation score value (MSSV) = runs x wicket down constant (WDC) x over introduced constant (OIC).

    The theory is that the openers should score more runs than anyone else - each wicket down increases the value of the runs scored (WDC). But, the later a batsman is introduced (in terms of overs), the value of his runs decreases (OIC). Runs earlier in the game provide a platform for a large score; runs late in the game may at best make a poor score respectable.

    My gut feeling constants are: WDC (proportionally increasing values): 0 = 1 1 = 1.1 2 = 1.3 3 = 1.6 4 = 2.0 5 = 2.5 6 = 3.1 7 = 3.8 8 = 4.6 9 = 5.5

    OIC: 0-25% = 1 26-50% = 0.8 51-75% = 0.6 76-100% = 0.4

  • drinks.break on January 17, 2014, 3:04 GMT

    It's not like some of the old measures aren't still useful. I'd have to question the value of Ave in such a short format, but SR is still significant ... and I don't think it really makes much difference how they got the runs - by running lots of 2s (like M Hussey) or by hitting lots of boundaries (like Finch) ... the end result is what counts.

  • alstar2281 on January 17, 2014, 2:42 GMT

    Calculate a players value by the amount of extra runs they contribute to a team's score against Ave. In This BBL the Ave Team score is 150 (based on all the individual averages). Thus if you take into account the number of runs scored and the speed (Finch scores faster the Kwajah thus the hypothetical ave score of the other 9wkts is higher as they have longer to bat) at which they are scored I calculate that Finch is worth an extra 29 runs to his teams innings against Kwajahs 15. Maxwell is leading on 48. The same can be applied to the bowlers by calculating the average team score and then the bowlers contribution based on wickets & economy in reducing that score. Similar to Baseball's WAR or WAA statistic, except measured in runs and not wins.

  • on January 17, 2014, 1:14 GMT

    We need fielding stats too. Distance covered (using GPS), fumbles, runs conceded, runs saved, boundary runs conceded, shies at stumps, direct hits, dropped catches, assists etc. Should be recorded for each player but maybe more useful as a whole team or outfielder/infielder/catcher/keeper set of splits.

  • woody3 on January 16, 2014, 17:54 GMT

    Finch edges srike rate? rounding about 148 to 118? Thats a runs off the bat score of 178 to 142 for twenty overs. Thats not even remotely close, anyone with half a brain can see Finch is a far better 20 over bat, even if he scored all his runs in sixes and dots and the other guy got them all in singles. The only batsmen you cant afford is a slow scoring bat who cant rotate strike, frankly a destructive six hitter not good at strike rotation like gayle you want the other guy to get singles not him, unless you are the fielding captain.

  • ThinkingCricket on January 16, 2014, 16:31 GMT

    py0alb: Smart idea, but doesn't work, because the odds incorporate expectations. I.E. the odds when Dhoni needs 45 off 19 balls are ridiculously low for the situation because the market already expects him to do it....so change in odds only calculates how much better the player is then his market perception, and not his overall value.

  • on January 16, 2014, 16:11 GMT

    @GeckoGariack Nailed it, for me. You could probably use the expected value of one ball (following the Tweedie distribution ) as a measure for both batsmen and bowlers. Loosely, it would describe the proportion of scoring shots to balls faced/bowled * the value of the shot. The only thing missing, then, would be the expected duration of an innings. Batsman X scores at x per ball over y balls

  • BobFleming on January 16, 2014, 13:16 GMT

    @ThinkingCricket - It could well have been Gilchrist (I'm cribbing from memory). Thanks for the correction. And I agree that it isn't perfect, although I would suggest that any player averaging 9 (as per your example) across a career would be fairly obviously dismissed from consideration. But if it were used to compare established batting specialists/all-rounders, I still think there is some merit to it. Perhaps it would be more useful in ODIs, where the accumulating - high-average - top order player and the less reliable middle order destructive types can both add value.

  • py0alb on January 16, 2014, 10:58 GMT

    There is only one correct answer to this question. Simply look at the betting odds before and after the player's innings or over. The best player is the one that consistently shifts the odds in his teams favour by the largest amount.

  • GeckoGarriock on January 16, 2014, 9:41 GMT

    @ThinkingCricket is bang on here and has beat me to the punch. Even the most set batsman can smoke one to cover or hole out on a miss-hit.

    To me, T20 wickets are things approximating a random event, therefore the value of "playing yourself in" is heavily diminished, and as @ThinkingCricket said, getting out when you have had that time to play yourself in and are 15(17) or similar is the worst result possible, far worse than (0)1 as the expected value of each ball is certainly greater than the 0.9375 runs per ball that are produced from the extra balls used in the 15(17) innings.

    I hypothesise that there is a strong correlation between teams scoring at a high S/R in balls 1-15 and winning, even though this goes against conventional cricketing wisdom.

    T20 cricket will be an expected value-based game very soon, the teams that pick up on it the fastest have a lot to gain.

  • ThinkingCricket on January 16, 2014, 8:31 GMT

    Yujilop and BobFleming your suggested metrics need work.

    Bob: What you suggested was proposed by Adam Gilchrist during Big Bash as the "Gilly Factor", the problem is that it's way too heavily weighted towards SR because Strike Rate just happens to be measured in much bigger numbers than averages. Averaging 9 at a SR of 175 isn't too helpful to a team...but your metric rates it really high.

    Yujilop: A three part metric will leave lots of questions about players who are good at one and bad at another, especially when the three are inversely correlated. On Average deviation though it isn't even clear which is better. If you guarantee me that a guy will score 100 (60) once in 5 games and 0 (1) the rest, I'd still put him No.1 every time, because that wins me 15% or so of games pretty much automatically. The main issue here is that SR's and game situation is so important that average is just not good enough to gauge anything.

  • ReverseSweepRhino on January 16, 2014, 8:09 GMT

    I think strike-rate, dot-ball percentage(DBP) and average balls per boundary (B/B) would be a good triplet for T20 batting statistic.

    Strike-ratio is an interesting idea, but it would have to be contrasted with other stats, the position the batsman bats and how many runs have already been scored before he got to the crease. If a team need 50 off the last 3 overs, one might prefer a big hitter with a higher strike ratio, especially if there is a person with low B/B at the other end. At the top of the order, one might consider someone with the same high strike ratio a big risk.

    One more stat might be Average Deviation. What % of a player's concluded innings are above 70% of his batting average. A player with scores of 10, 2, 13, 122 and 3 and one with 33, 31, 26, 40, 20 would both have an average of 30.00, but the second would have AD 80%, compared to the other's AD of 20%. While the first batsman might make one or two big score, he isn't as reliable as the second one.

  • BobFleming on January 16, 2014, 7:52 GMT

    I think it was in Ed Smith's column a couple of years ago, that he mentioned (while on the topic of Moneyball-esque value ratings) a nicely simple system for evaluating T20 batsmen. Basically you add the player's average to his strike rate, so in the two players above, Finch would rate at 200.64 (52.40+148.24), whilst Khawaja would rate 172.24 (54.00+118.24). It isn't conclusive, but it gives a decent at-a-glance metric.

  • GeckoGarriock on January 16, 2014, 6:26 GMT

    @ThinkingCricket is bang on here and has beat me to the punch. Even the most set batsman can smoke one to cover or hole out on a miss-hit.

    To me, T20 wickets are things approximating a random event, therefore the value of "playing yourself in" is heavily diminished, and as @ThinkingCricket said, getting out when you have had that time to play yourself in and are 15(17) or similar is the worst result possible, far worse than (0)1 as the expected value of each ball is certainly greater than the 0.9375 runs per ball that are produced from the extra balls used in the 15(17) innings.

    I hypothesise that there is a strong correlation between teams scoring at a high S/R in balls 1-15 and winning, even though this goes against conventional cricketing wisdom.

    T20 cricket will be an expected value-based game very soon, the teams that pick up on it the fastest have a lot to gain.

  • highveldhillbilly on January 16, 2014, 6:14 GMT

    @ MinusZero - playing against a good/better team should count for something, assuming it can be quantified however the match situation then also becomes important as well as the scoring of both teams during the match. For example a Tendulkar 100 in a score of 600 for 3 against Aus in the first innings in a drawn test should not count as much as a 75 not out in the 4th innings of a test against Aus to win the test by 3 wickets in a low scoring thriller. Pitch conditions, the quality of the bowlers you are facing, match situation etc etc all need to be quantified which is very, very difficult to do. Hence we fall back on averages. Remember Tendulkar also played a lot of games and scored a lot of runs against Sri Lanka and other middle of the range test teams in the 2000s, shouldn't that also count for something?

  • ThinkingCricket on January 16, 2014, 6:07 GMT

    To continue, there is rich scope for statistical inquiry.

    Given 15 (15), I am not sure I would prefer the guy who did it in singles, big boundary shots tend to change momentum, field placing and bowler confidence a lot more than nurdling it around (though you are right that having the stat would be useful).

    The other problem with averages is they cover very long careers and thus do a spectacularly good job of missing out on change and improvement. Countless current "Stars" are actually hopelessly bad when you exclude performances from the past that have no bearing on their present skill.

  • ThinkingCricket on January 16, 2014, 6:02 GMT

    This is a brilliant article and it tells the truth. Unfortunately, there are issues. These stats are difficult to come up with and are not commercially viable unless these guys are doing it for a franchise, or for sports-betting purposes (of the honest kind) to establish actual odds and probabilities.

    Actual truths about T-20 are shocking. I know, I do this analysis. Usman Khawaja is actually a very poor player. Average is the most misleading stat in T-20 because high averages at low strike-rates actually impede the team. If 180 is a par score, 0 (1) is a better performance for a guy coming in at No.1 and being the first wicket to fall then 40 (40).

    Averages also conceal distributions. Ben Dunk's 96 makes up for 5 or 6 failures in terms of an average, but the innings itself didn't even guarantee a win in the match it was made (though they did win). Batters going at 220 in one innings when they get set can conceal the many innings where they eat up valuable balls and then get out.

  • MinusZero on January 16, 2014, 0:25 GMT

    The more stats the better i say, I am a statistic nut.

    I think another thing to consider with statistics could be the opposition. For example: Players like Ponting, Warne and McGrath were undoubtedly great players of their time, BUT, they never had to play against the best team of their time, Australia.

    Someone like Tendulkar for instance, he played in a team which for most of his career was not up with the best, but he did play against Australia and excelled against them. Should that not count for something?

    Another example would be Shakib Al Hasan from Bangladesh, he has impressive all round figures, comparably similar to Shane Watson (who doesnt seem to ever do wrong). I would rate Al Hasan higher, as he has to always play better opposition and still has a good record.

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  • MinusZero on January 16, 2014, 0:25 GMT

    The more stats the better i say, I am a statistic nut.

    I think another thing to consider with statistics could be the opposition. For example: Players like Ponting, Warne and McGrath were undoubtedly great players of their time, BUT, they never had to play against the best team of their time, Australia.

    Someone like Tendulkar for instance, he played in a team which for most of his career was not up with the best, but he did play against Australia and excelled against them. Should that not count for something?

    Another example would be Shakib Al Hasan from Bangladesh, he has impressive all round figures, comparably similar to Shane Watson (who doesnt seem to ever do wrong). I would rate Al Hasan higher, as he has to always play better opposition and still has a good record.

  • ThinkingCricket on January 16, 2014, 6:02 GMT

    This is a brilliant article and it tells the truth. Unfortunately, there are issues. These stats are difficult to come up with and are not commercially viable unless these guys are doing it for a franchise, or for sports-betting purposes (of the honest kind) to establish actual odds and probabilities.

    Actual truths about T-20 are shocking. I know, I do this analysis. Usman Khawaja is actually a very poor player. Average is the most misleading stat in T-20 because high averages at low strike-rates actually impede the team. If 180 is a par score, 0 (1) is a better performance for a guy coming in at No.1 and being the first wicket to fall then 40 (40).

    Averages also conceal distributions. Ben Dunk's 96 makes up for 5 or 6 failures in terms of an average, but the innings itself didn't even guarantee a win in the match it was made (though they did win). Batters going at 220 in one innings when they get set can conceal the many innings where they eat up valuable balls and then get out.

  • ThinkingCricket on January 16, 2014, 6:07 GMT

    To continue, there is rich scope for statistical inquiry.

    Given 15 (15), I am not sure I would prefer the guy who did it in singles, big boundary shots tend to change momentum, field placing and bowler confidence a lot more than nurdling it around (though you are right that having the stat would be useful).

    The other problem with averages is they cover very long careers and thus do a spectacularly good job of missing out on change and improvement. Countless current "Stars" are actually hopelessly bad when you exclude performances from the past that have no bearing on their present skill.

  • highveldhillbilly on January 16, 2014, 6:14 GMT

    @ MinusZero - playing against a good/better team should count for something, assuming it can be quantified however the match situation then also becomes important as well as the scoring of both teams during the match. For example a Tendulkar 100 in a score of 600 for 3 against Aus in the first innings in a drawn test should not count as much as a 75 not out in the 4th innings of a test against Aus to win the test by 3 wickets in a low scoring thriller. Pitch conditions, the quality of the bowlers you are facing, match situation etc etc all need to be quantified which is very, very difficult to do. Hence we fall back on averages. Remember Tendulkar also played a lot of games and scored a lot of runs against Sri Lanka and other middle of the range test teams in the 2000s, shouldn't that also count for something?

  • GeckoGarriock on January 16, 2014, 6:26 GMT

    @ThinkingCricket is bang on here and has beat me to the punch. Even the most set batsman can smoke one to cover or hole out on a miss-hit.

    To me, T20 wickets are things approximating a random event, therefore the value of "playing yourself in" is heavily diminished, and as @ThinkingCricket said, getting out when you have had that time to play yourself in and are 15(17) or similar is the worst result possible, far worse than (0)1 as the expected value of each ball is certainly greater than the 0.9375 runs per ball that are produced from the extra balls used in the 15(17) innings.

    I hypothesise that there is a strong correlation between teams scoring at a high S/R in balls 1-15 and winning, even though this goes against conventional cricketing wisdom.

    T20 cricket will be an expected value-based game very soon, the teams that pick up on it the fastest have a lot to gain.

  • BobFleming on January 16, 2014, 7:52 GMT

    I think it was in Ed Smith's column a couple of years ago, that he mentioned (while on the topic of Moneyball-esque value ratings) a nicely simple system for evaluating T20 batsmen. Basically you add the player's average to his strike rate, so in the two players above, Finch would rate at 200.64 (52.40+148.24), whilst Khawaja would rate 172.24 (54.00+118.24). It isn't conclusive, but it gives a decent at-a-glance metric.

  • ReverseSweepRhino on January 16, 2014, 8:09 GMT

    I think strike-rate, dot-ball percentage(DBP) and average balls per boundary (B/B) would be a good triplet for T20 batting statistic.

    Strike-ratio is an interesting idea, but it would have to be contrasted with other stats, the position the batsman bats and how many runs have already been scored before he got to the crease. If a team need 50 off the last 3 overs, one might prefer a big hitter with a higher strike ratio, especially if there is a person with low B/B at the other end. At the top of the order, one might consider someone with the same high strike ratio a big risk.

    One more stat might be Average Deviation. What % of a player's concluded innings are above 70% of his batting average. A player with scores of 10, 2, 13, 122 and 3 and one with 33, 31, 26, 40, 20 would both have an average of 30.00, but the second would have AD 80%, compared to the other's AD of 20%. While the first batsman might make one or two big score, he isn't as reliable as the second one.

  • ThinkingCricket on January 16, 2014, 8:31 GMT

    Yujilop and BobFleming your suggested metrics need work.

    Bob: What you suggested was proposed by Adam Gilchrist during Big Bash as the "Gilly Factor", the problem is that it's way too heavily weighted towards SR because Strike Rate just happens to be measured in much bigger numbers than averages. Averaging 9 at a SR of 175 isn't too helpful to a team...but your metric rates it really high.

    Yujilop: A three part metric will leave lots of questions about players who are good at one and bad at another, especially when the three are inversely correlated. On Average deviation though it isn't even clear which is better. If you guarantee me that a guy will score 100 (60) once in 5 games and 0 (1) the rest, I'd still put him No.1 every time, because that wins me 15% or so of games pretty much automatically. The main issue here is that SR's and game situation is so important that average is just not good enough to gauge anything.

  • GeckoGarriock on January 16, 2014, 9:41 GMT

    @ThinkingCricket is bang on here and has beat me to the punch. Even the most set batsman can smoke one to cover or hole out on a miss-hit.

    To me, T20 wickets are things approximating a random event, therefore the value of "playing yourself in" is heavily diminished, and as @ThinkingCricket said, getting out when you have had that time to play yourself in and are 15(17) or similar is the worst result possible, far worse than (0)1 as the expected value of each ball is certainly greater than the 0.9375 runs per ball that are produced from the extra balls used in the 15(17) innings.

    I hypothesise that there is a strong correlation between teams scoring at a high S/R in balls 1-15 and winning, even though this goes against conventional cricketing wisdom.

    T20 cricket will be an expected value-based game very soon, the teams that pick up on it the fastest have a lot to gain.

  • py0alb on January 16, 2014, 10:58 GMT

    There is only one correct answer to this question. Simply look at the betting odds before and after the player's innings or over. The best player is the one that consistently shifts the odds in his teams favour by the largest amount.