T20 cricket, like baseball, is a game which lasts about three hours. When close and competitive, it’s a pleasure to watch. When one-sided, you understand why both sports feel the need to have fireworks, dancers, music etc at the ready, to keep the thoroughly lubricated crowd at bay – and some nice graphics for the home viewers.
Channel 10’s viewer-experience-enhancing graphics
And whilst we’re enjoying the colour and movement of it all, we can take in a few key stats about the players. For batsmen, there’s strike rate (run scored per 100 balls) and for bowlers economy rate (runs conceded per 6 balls). Anything over 115 or so is good for batsman, anything under 7.2 or so is good for bowlers (“or so” is inserted because a few more years on T20 cricket are probably needed to put batting and bowling performances in context).
Both economy rate and strike rate are calculated by taking the number of runs and dividing it by the number of balls. Then – for a reason that escapes me – economy is calculated by multiplying by 6, strike rate multiplying by 100. Aren’t these figures two sides of the same coin? Batsman try to maximise runs scored off an over, bowlers try to minimise runs scored off an over?
In the interests of making easy comparisons between batting and bowling performances – for those of us who can’t quickly multiply or divide by 16⅔ – perhaps a majority position can be reached on whether the factor should be 100, 6, or something else?
In an earlier, ahem, somewhat long (but now updated and completed) post I described an experiment to test the Duckworth-Lewis Method against the Best Scoring Overs (BSO) and Best Scoring Sequence (BSEQ) methods, using the domestic one-day competition as a test tube.
Hey – I wasn’t the only one watching the series who was looking for alternative ways to pass the time…
When statisticians talk about a “trend”, they mean that they have a data set of trustable size which will let them predict future data.
When Malcolm Conn writes about a trend, as he does in “Runaway trend an ominous omen for Australia ahead of hosting 2015 World Cup”, he means several cricket matches which happened six years ago, four matches which happened in the last week or so, and nothing that happened in between.
Ominous omen? Sounds… significantly significant.
Or possibly it’s the sort of ominousness that can be treated like this?
The international cricket season isn’t far away, so doubtless we can soon expect a steady diet of stories like these, carving up a whole pile of numbers and telling us that the reason a cricketer / cricket team is succeeding / failing is… well, just about anything.
S Rajesh of cricinfo, though, is obsessed with the idea that performance is affected by the location of a match – whether it’s the ground, the country, one’s home country, one’s home country (again), or even the continent. (Indicative quote: “Clearly, Hafeez’s problem has been facing the new ball outside Asia.” Ummm… yeah. Clearly.)
His theories may be right. But there is so much wrong with the way he tests them that I’m not really sure where to begin. The formula seems to be something like this:
Pose a question, attributing something to a single variable: could Sri Lanka’s recent success be linked to the colour of their shoelaces? Continue reading
In an earlier post I promised to monitor domestic and international one-day matches this summer, and hypothetically compare the Duckworth Lewis (DL) method to the old best-scoring overs (BSO) method. From the 20th over onwards in the second innings, each method will predict a winner by providing a target score, as if the match was suddenly rained out. So there will be a prediction after over 20, 21, 22… all the way up to 49; 30 predictions in total if the match goes that distance (no points awarded for predicting the winner after over 50).
[Update as of 3/11/2013 – I’ve made the executive decision to add the highest-scoring consecutive overs method to this.]
In the first match Tasmania spoiled the experiment by not lasting until over 40 – but all methods correctly chose the winner correctly 15 times, until the match ended at the end of over 35.
You’ll notice that the BSO line is pretty smooth, but the DL is jagged – that’s because the target changes markedly each time the chasing team loses a wicket. (In this match, Tasmania were kind enough to demonstrate this frequently.)