Against the Spin

It’s been nearly three years since international Twenty20 cricket kicked off in earnest, with the 2007 World Twenty20.  And over five years have passed since the very first match, a trans-Tasman encounter in February 2005.  In that time, which teams have adjusted well to the 20-over game?  And which ones are still struggling to understand the format?

One thing is clear, even without looking at the data.  Twenty20 levels the playing field — with the shorter matches, it’s easier for a Zimbabwe to surprise a team like Australia.  But, as is my wont, I ended up running the numbers to compare a team’s ODI batting performance from 2006 to 2009, with its batting performance in Twenty20 matches over the same period.

The Duckworth-Lewis system comes in handy here.  It provides a proven way to judge what the 20-over equivalent of a particular 50-over score is.  The magic number is 58.6% (see this post for more info on D-L).  That is, in a T20 innings, a team has 58.6% of the resources it has in a 50-over innings.  That means that an ODI score of 300 is roughly equivalent to a Twenty20 score around 180, which sits well with my intuition.  We can look at the equivalent Twenty20 scores for some ODI teams, and then compare to that team’s actual Twenty20 scores.

Overall, every team except India exceeds the median of the predicted T20 range — perhaps there’s something systematic about D-L underestimating the Twenty20 scores.  Maybe the ODI model doesn’t map well to the T20 format.  Or maybe Twenty20 specialists like Kieron Pollard are adapting to the format better than the ODI regulars.

Twenty20 has been a boost especially to the weaker ODI teams (England, West Indies).  Both of them comfortably surpass the Duckworth-Lewis prediction in T20s.  If you have any explanations for this, please post them in the comments.

Two possibilities I can think of:

1. Duckworth-Lewis is just underestimating the prediction.  One big thing the system doesn’t take into account is the strength of individual players.  In a 20-over match, a single innings from a Gayle or a Pietersen can take the team to a good score, whereas ODI are slightly more of a team effort.
2. Perhaps it’s just that the styles of batsmen like Pollard are suited to the wham-bam game, and can’t easily be adapted to play a 50 or 100-ball innings.  This would undermine my methodology of predicting Twenty20 scores from ODI data.

Despite losing both of their group matches so far, India still have a chance to go through to the semifinals — beat Sri Lanka by 20 runs.

India just posted 163 in their innings, so they need to restrict Sri Lanka to 143, and then hope that Australia beat West Indies.  Then net run rate will take India through, regardless of the margin of WI’s defeat to Australia.  Here’s what the table would look like if India beat SL by 20 runs (Ind 163, SL 143), and then Aus beat WI by 1 run (Aus 160, WI 159).

It’s a long shot, but there’s hope for Indian fans!  I’m tweeting out live updates on the required run rate for SL to knock India out.  Check it out on Twitter: http://twitter.com/againstthespin

Quick update to yesterday’s post on the teams’ semifinal chances, based on the results of today’s games.  Rajasthan are the main movers, with their loss to Mumbai bringing their qualification probability down to 49.8%.  Chennai are the beneficiaries of that move, and now have a 40.3% chance of making it through.  I also added a slightly more sophisticated way of accounting for changes in net run rate with wins/losses, so these new numbers should be more accurate.  Factoring in today’s results, its increasingly likely (86%) that 14 points will be needed to qualify for the semifinals.

Delhi’s loss to Punjab doesn’t change much for either team.  Punjab still have about a much of a chance of going forward as Chris Martin does of hitting a Test century — mathematically possible, but not even worth mentioning.  Delhi too are still in a strong position, though their chances dropped from 89% to 83%.  Even if they lose their third match on the trot to Mumbai on Tuesday, they’ll still have an over 70% chance of going through.  That’s what a strong early season and a healthy net run rate does for you.

Read more…

The round-robin stage of the IPL is drawing to a tense conclusion.  While Mumbai are almost certainly in, and Punjab are almost certainly out, the fate of the other six teams is very much up in the air.  There are 13 games left to play, and a number of possible outcomes.  So, I ran a simulation that calculates all 8192 ways the last 13 games could finish up (assuming no ties), and assessed each team’s position in each of those 8192 cases, breaking ties on the current net run rate.  Without further ado, here are the results:

Rajasthan, despite being level with Bangalore on points, are less assured of going through, thanks to a weak net run rate (NRR).  Meanwhile, a strong net run rate could see Chennai through if they can gain a game on Rajasthan.  The following chart shows the number of points that will be needed to qualify for the semifinals.

Read more…

Michael Lewis’ Moneyball thrust sabermetrics into the baseball mainstream.  The story of Moneyball revolves around Oakland A’s manager Billy Beane, who used objective analysis to better manage his baseball team.  Under his leadership, Oakland used statistics to understand which traits (like on-base percentage) and which players were undervalued by the market.  Beane would buy these undervalued players for cheap, and watch the rest of baseball discover their value as they scored runs and made plays for Oakland.  Then, when they became sought-after stars, he’d sell them to other teams for a profit (see Jason Giambi).

Cricket is perhaps a decade or two behind baseball in its use of objective analysis to better understand players, and better manage teams.  I’d speculate that the big reason for that is the lack of money in cricket, until the IPL.  No matter how good Stuart MacGill was, he couldn’t play Tests for any team but Australia.  Now, if one IPL team doesn’t select a player, another team will — there is now a competitive market for talent.  And the IPL is making this Moneyball scenario possible in cricket as well.  Check out this quote from Amrit Mathur, COO of the Dehli Daredevils (article from Cricinfo):

Read more…

Happy New Year to you all!  I’m happy to announce that Against the Spin has been voted the “Best New Cricket Blog” of 2009 over at World Cricket Watch — thanks for your support and votes.  I’m flattered by this recognition, and hope to live up to that billing in 2010.  Here are Against the Spin’s New Year’s Resolutions:

Read more…

Any cricket fan will tell you that a good-length ball outside the leg-stump will go for more runs than a good length ball just outside the off-stump.  But how many more runs, on average?  That’s where you need some real data to answer the question.  So, in keeping with the theme of this blog, let’s try to quantify scoring & wicket-taking by area of the pitch.

First, let’s look at a bowler’s line and length.  Obviously, this will differ based on whether the batsman and bowler are right or left handed, and whether the bowler is a pace bowler or a spinner.  The handedness of the batsman is accounted for by making the pitch locations relative to the batsman’s off-stump.  The pitch map below shows exactly where pace bowlers land their deliveries.

The totals in the margins indicate that 73% of balls are bowled in the channel outside off stump, and nearly a quarter of balls are bowled short of a good length.  Now let’s look at how fruitful each of these delivery areas is.  Shown on the pitch maps below are the strike rates of batsmen (runs/ball) for balls pitching in that area.

We can quantify the age-old cricketing wisdom that line & length are rewarded.  Good length balls just outside the off-stump go for only about 7 runs per over, while fuller balls are punished at over 9 runs an over.  Similarly, if the bowler strays onto the stumps, the easy on-side runs come at about 9 per over.

The data used to produce the above charts includes all Twenty20 internationals from June to November 2009, plus the 2009 Twenty20 Champions League.  The balls include approximately 4800 balls bowled by fast and medium-fast bowlers.  These pitch maps only include data on right-arm pace bowlers.  Data on left-armers and spinners may appear in a future post.

First, apologies for the lack of posting over the past couple months.  I’m in the midst of a busy semester of university, so I haven’t been able to dedicate much time to cricket analysis.  But I wanted to share an interesting paper I came across, by James, Carré, and Haake.  They build a Newtonian model of what happens when a cricket ball bounces on a pitch.  It turns out that the “pace” of a pitch (ie, what percent of velocity a ball retains after bouncing) can be predicted fairly well by this model, which uses some physical pitch measurements as parameters.

You can find the abstract of this paper at: http://www.springerlink.com/content/g551516250368338/.  Full-text can probably be found through an institutional subscription, if you are affiliated with such an institution.

With the Ashes heating up to a tense finale, Karl van der Merwe wondered on the Against the Spin facebook page how England would have fared if Australia had Glenn McGrath and Shane Warne.  The short answer: not well.  Australia have been a dominant side with the two bowling greats, and merely a quite competitive one without them.

I used Cricinfo’s Statsguru to take a look at how Australia have done in Ashes Tests since 1990, with and without this extraordinary pair.  The arbitrary date restriction is to try to control for the quality of both teams, and still allow for there to be several matches without McGrath & Warne.  It’s not perfect though; for example, an Aussie team containing McGrath and Warne was also more likely to contain Gilchrist, Ponting and Steve Waugh, making it a better team even beyond the presence of the two bowlers.  There are too many complicating factors here for this to be considered anything of a definitive statement, but it’s still a fun fact for Aussie fans to rub in the faces of their English friends.  Even with McGrath or Warne absent, Australia still win nearly half the Tests they play against England, and lose just about a quarter.

When both McGrath and Warne’s names were on the team sheet, Australia won a remarkable 76% of the Ashes Tests they played, and lost only 3 out of 25.  With just one of the two in the lineup, Australia played 16 Ashes Tests, winning 8, and losing 5.  And even with both of these greats gone, Australia won four matches out of ten, and lost just two.  England have been more competitive when facing an Australian side devoid of McGrath and Warne, but have still struggled against their arch-nemesis.

I think one of the biggest barriers to widespread use of statistics in cricket to better understand team & player performance (in the way that sabermetrics is used in baseball) is the scarcity of freely-available data.  I made an amateurish attempt at creating some structured data on my own, and posted it on the data section of this site, but it’s far froom perfect.  That’s why I’m excited that Stephen Rushe has put together some data with an improved version of the yaml format I used.  Its available at:

http://deeden.co.uk/misc/cricket/

Specifically, there’s better information about wickets that fell, more player names, and non-striker information.  And if you have ideas or data sources of your own, do leave a note in the comments.