In many ways watching T20 cricket is like being a spectator at a “noughts and crosses” championship. Such things do exist, apparently. There are only so many options available to the players and only so many ways past their defences. Attack and defence techniques are limited by the parameters of the game.
But that’s not the way those involved in the inner sanctum of the format see it. Apart from the players and the coaches, an entire analysis industry is blooming and no self-respecting T20 team worthy of the name would even consider playing a serious game without input from their team of nerds.
You can’t watch a game on TV these days without hearing the commentators referring to the “matchups” between individuals in the two teams. Some are desirable and others must be avoided, with teams going to great lengths of game manipulation to engineer the right outcome.
These matchups result from the increasingly extreme analysis of the strengths and weaknesses of individual players. Where and how batters score their runs, against what sort of bowlers and at what stage of the innings. Player X has a strike rate of 135 against left arm spin between overs 10 and 15 but only 94 against off-spin during the same period.
Player Y has a strike rate of 145 against right arm seam during the power play when the bowling is “length” but it drops to 102 against left arm seam when the bowling is short. He has been dismissed seven times by slower-ball bouncers and scores 37% of his runs between cover and third man.
That doesn’t even scratch the surface. Coaches can request data on almost anything these days, but they don’t need to because the analysts have established a conveyor belt of numbers to justify their salary and place in the squad. The matchup matchmakers are having the time of their lives with captains and coaches drawing immediate conclusions from the numbers and devising game plans accordingly.
In the final days of the T20 World Cup one of New Zealand’s best and most consistent bowlers, left arm spinner Mitchell Santner, bowled only one over in the semifinal against England while Glenn Phillips, a wicketkeeper and part-time off-spinner, bowled an over that cost 11 runs simply because England had two left-handed batsmen at the crease.
Moeen Ali, good enough to open the bowling in three of England’s six matches, did not bowl at all during the semifinal because the “wrong” batters were at the crease. Chris Woakes bowled his first two overs at a cost of just eight runs while collecting the wickets of Martin Guptill and Kane Williamson. For his final over he bowled short to Black Caps batter Jimmy Neesham because the data said he was less effective against the short ball. Neesham hit him for two sixes, took 20 off the over and effectively won the game.
That’s the trouble with sterile data. It’s unreliable without the context provided by human beings. Just as the information provided by human beings can be unreliable without data. “I’m hitting it really well, coach.” “Oh really? Your boundary percentage and power play strike rate against seamers and spinners are both lower than last year and have dropped further in the last six weeks.”
A few years ago an international batter was labelled “poor” against left arm spin because his average and strike rate were both modest compared with other areas of his game. Apparently, the disproportionate number of games he’d played in franchise cricket against Shakib Al Hasan, the world’s premier white ball left armer, was considered superfluous. Data is data and it doesn’t lie. He had quite the time of it feasting on lesser left arm spinners until the numbers sorted themselves out.
There is no aspect of the analysis that I immediately find uninteresting. It’s just that it raises at least as many questions as it answers. If a batter has a strike rate of 165 and an average of 32 against left arm wrist spin, and is dismissed by it 62% of the time he encounters it, is he good against it or bad against it?
OK, I concede. The noughts and crosses comparison was harsh. T20 cricket has more nuance than that, but it is not and never will be the science that the analysts would have us believe it is. Just ask Jimmy Neesham. Hole out to the deep midwicket boundary against the short ball for a while and the computer will have you “labelled”, no matter how comfortable you are against the short ball or whether your bat just happened to slip playing the hook shot.
For centuries cricket lagged behind other sports in the fields of statistics and analysis. Now it’s finally catching up, but we must still learn what to do with all this information and how best to use it.
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