Showing posts with label moneyball. Show all posts
Showing posts with label moneyball. Show all posts

Monday, January 9, 2012

Lies, Damned Lies and Statistics: Cricket's Moneyball effect

Ben Roberts

It's my turn to throw my voice into the vortex of latter opinion and desire borne out of the 2011 release of Moneyball. I only recently watched this film, and enjoyed it immensely. It's release has awoken the rest of the sporting world to a concept that was not even new in the period portrayed in the movie, but has been around for over 30 years. Suddenly everyone wants a piece of the action, and to find the killer measurable statistic for their sport of choice that separates the wheat from the chaff.

Baseball is a sport made for such a concept. Without going into much detail, in my opinion baseball lends itself so easily to this analysis due to: 1) being very static in gameplay with players not moving around the field randomly but in a definite order; 2) having direct cause-and-effect relationships in the game play, (for example an out always equals a run saved); 3) Major League Baseball being a market based sport (also replicated in many others but not cricket) meaning that value is something more easily determined as it comes in dollars and cents.

Cricket by contrast does not have such a static nature nor cause and effect relationships. While commentators always say that the best way to restrict scoring is to take wickets this is not an absolute (like baseball) until you talk about the 10th wicket falling. Neither does the sport have a market-based nature, although players are shifting first-class teams more today, the game remains a sport played at the highest level as a regional representative.

The key premise of the theory is stated early in the movie when Jonah Hill's character tells Brad Pitt's character that for years they have been asking the wrong question. They should be trying to buy wins (a direct result of runs scored and restricted) not players. The improvement in statistics themselves had been around for many years, the trouble was the ignorance of the users.

Cricket has a multitude of data already at its disposal. Former Australian coach John Buchanan was known for recording extensive data and this became the norm for most first-class teams. The difficulty is that unlike baseball - where you can name what you want - in cricket, you cannot be as sure. Yes, more runs are important, but in Test matches you need to take wickets also.

So what if we just use such analysis for limited over matches where it's all about runs. Good idea, except last night I saw a rain interrupted T20 match get decided by the Duckworth-Lewis method which relies on wickets in a calculation of a par score. As well, we still seem to value bowling in limited over games, if we are truly only after more runs why not simply stock your team with 11 batsman who can nominally roll their arm over and field well?

The difficulty is that we do not know what the question to ask is; that is, what constitutes total value in a game of cricket? The entire premise of using such statistics is to restrict the questions that you want the statistics answer, unless you want your statistics to prove any and all manner of things. 

To give an extreme example: You have two batsmen, 1 and 2. In traditional statistics both average 36 and have a strike rate of 72 runs per 100 balls. A normal innings therefore for either is to score 36 runs off 50 deliveries.  We have a dilemma: if we need to choose, both look equal - based on traditional measures. Turning more detailed statistical analysis, we find that Batsman 1 gets those runs in 36 singles, where Batsman 2 usually hits 6 sixes (I told you the example was extreme). Which batsman is the more valuable?

My initial reaction is to say Batsman 1 is more valuable in that they turn the strike over to the other batsman giving greater chance for team scoring while they are at the crease whereas Batsman 2 faces a stack of dot balls. But what is the effect on the bowlers? Does the potentially greater runs scored per single ball by player 2 make them more valuable? Unless you know what you really want statistics can tell you anything.

Don't read me wrong - such analysis has every place in the game but requires a liberal amount of common sense to be applied. You can easily measure the worth of two identically skilled players as above. You may use the above analysis in comparison to what the team needs, but you cannot make the clear cut decisions that they can in baseball as there is no single measure of value.

Ed Cowan at his best; (c) Balanced Sports
How would you statistically make the decision (as for the recent Melbourne Test) whether to play an opening batsman Ed Cowan or all-rounder Daniel Christian? To do so compares apples with oranges. In baseball, you can use a standard measure of total value to the team and cut through inconsistencies, in cricket understanding and intuition must still be applied.

I have only a rudimentary understanding of statistic usage, and someone more esteemed than I may be able to prove that there is a methodology escalating statistical analysis beyond being a support category in cricket decision making. But until that time remain wary of the limitations when trying to apply to cricket. Mark Twain believed it was Benjamin Disraeli who said "There are three kinds of lies: lies, damned lies, and statistics." Though it remains historically an un-sourced statement, there is still much truth to it.

Thursday, July 7, 2011

Scoring Stats - Messi and Ronaldo lead all (again!)

Any statistical analysis of a sporting event can go down one of two main routes. Firstly, they could be raw data, usually expressed as totals or percentages. In football, examples include the number of total corners a team concedes (or forces), the number of minutes a player is on the park for over the course of a season or even how many points a team accumulates. The second format usually depends on rate - goals per possession, corners per dribble, dribbles per game or even passing completion percentage.

Both have their uses, but as football (and other sports) become more and more subject to the moneyball theory and so-called "advanced metrics", rate has taken precedence over simple raw data. For instance in football, teams play at different tempos - one team may slowly build from the back while other teams favour quick incisive bursts. A team with a "lump it up" and lose possession philosophy is likely to create less chances by dint of not having the ball as often. It's fortunate we have two very visible clubs with vastly different methods of operating to easily point to: Barcelona dominate possession no matter who they play, while almost any team coached by Sam Allardyce feels more content without the ball than with it.

A Scoring Stat is defined as any goal or assist a player is credited with, therefore a player's total thereof is the number of goals and assists he accumulates over a season. So far we've examined trends throughout Europe, which players were individual total leaders and finally how much the dependence on a particular player varied across the four professional divisions in one country. Now it is time to evaluate which players provided the greatest lift, per game, to their individual teams.

Unfortunately, access to minutes-played data was very difficult to come by, so this is evaluated according to the number of games in which a player participated. In this analysis, only Team Leaders are evaluated (players who led their club in total Scoring Stats) - a follow-up analysis will include all of Europe.

Europe's Top Team leaders by match

League Team Player Games % Stats per Game
La Liga Barcelona Lionel Messi 33 0.516 1.485
La Liga Real Madrid Cristiano Ronaldo 35 0.490 1.429
EPL Arsenal Robin Van Persie 25 0.347 1.000
Serie A Udinese Antonio Di Natale 36 0.538 0.972
Bundesliga Bayern Munich Mario Gomez 32 0.370 0.938
Serie A Napoli Edinson Cavani 35 0.542 0.914
Serie A AC Milan Zlatan Ibrahimovic 29 0.385 0.862
Serie A Inter Milan Samuel Eto'o 35 0.435 0.857
EPL Man City Carlos Tevez 31 0.433 0.839
EPL Tottenham Rafael Van der Vaart 28 0.382 0.750
EPL Man United Dimitar Berbatov 32 0.308 0.750
Bundesliga Koln Milivoje Novakovic 28 0.426 0.714
Bundesliga Hannover Didier Ya Konan 28 0.408 0.714
La Liga Espanyol Pablo Osvaldo 24 0.370 0.708
Bundesliga Freiburg Papiss Demba Cisse 32 0.537 0.688
Average Levante's Felipe Caciedo



0.559
Complete table can be found at Balanced Sports Scoring Stats page.

Once again, this only goes to highlight how far clear of the pack Leo Messi and Cristiano Ronaldo remain as footballers. While the average Team Leader contributes about what teams look for from an effective, workmanlike striker (the adage goes "a goal every second game" and remember there are probably some relatively below-average Strikers leading their teams here), they nearly triple that average. After them, the next best - the perpetually injured Van Persie - only managed to average one goal or assist per match.

These totals, however, could be swayed for total numbers. Both Real and Barca scored a boatload of goals during season 2010-11. To evaluate the top fifteen clubs by goals-per-game across Europe is telling:

This table - comprised nearly completely of the usual suspects - leads us to suggest that while Messi and Ronaldo's influence is remarkbable, it is in part due to the increased number of goals their clubs score. This of course gives rise to the perpetual (and now, frankly, boring) Messi versus Ronaldo debate and prompts us to pose the "chicken or the egg" question once more - do they top the individual list because of their team rate, or do their teams top the goals-per-game table because of their phenomenal skill? Unfortunately the answer isn't easily forthcoming but suffice to suggest that both generate remarkable amounts of opportunities for their teams, and benefit from their teammates doing the same.

Finally, a note about VfB Stuttgart. The Reds finished a disappointing twelfth in the Bundesliga this year, yet pounded in 1.76 goals per game without having a player in the top 45 team leaders by rate. Their most productive forward by totals as Martin Harnik with 15 total scoring stats, who contributed to a quarter of their scores (averaging less than one scoring stat every two matches).

Stats for all players, teams and leagues can be found at Balanced Sports' Scoring Stats page.
Image courtesy: www.nevercaptainnickybutt.com