Showing posts with label advanced stats. Show all posts
Showing posts with label advanced stats. Show all posts

Thursday, February 21, 2013

Graphic: Four-year EPL efficiency

This efficiency study tracks how effective each English Premier League team has been since 2009-10.  It does so by mapping a team's offensive efficiency (measured in shots per goal scored) against their defensive resilience (measured in shots faced per goal conceded).  Data is updated to 22nd February 2012 and so includes Liverpool's recent 5-0 win over Swansea City.

The axes cross at "league average" positions, meaning the graph is divided into relatively even quadrants.

As one would expect, twinn'd Manchester clubs appear to have the greatest cumulative combination of offense and defence, Chelsea's numbers lessened by a misfiring striker and ill-fated flirtations with seductive foreign managers (their 2010 season was probably the most efficient EPL club season in recent history).

Click on the graphic to enlarge.

(c) Balanced Sports

As always, thanks to Ben Mayhew at Experimental 3-6-1 for the idea, sourced over a year ago.

Thursday, March 22, 2012

Graphic: England's most efficient goalscorers

The chart below displays the efficiency of forwards and midfielders playing in the English Premier League.  It plots each player's shooting accuracy, in goals per shot, against their scoring rate in goals per game.  The further to the top right corner of the graph the player is, the more efficient they are.  All players in the EPL who have scored five or more goals are considered.

It is obviously swayed towards recent players or those who don't play that often.  This makes Eastern bloc duo Dimitar Berbatov and Pavel Pogrebnyak the most efficient goalscorers in the league to this point.

Click to enlarge the graph.


Tuesday, February 28, 2012

The future isn't so rosy: Australia's next batsmen

Although the first nation into the CB finals series and coming straight from a whitewash of India in the recent Test series, Australia has far from a settled lineup. Ricky Ponting and Mike Hussey are the two oldest First Class cricketers in the nation, while the third was only recently sacked from the Australian team.  The youthful promise of Phil Hughes and Usman Khawaja has tried the country's patience twice too often, while Shaun Marsh and Brad Haddin both endured form slumps which made Mark Taylor's 1996-97 look positively inspired.

With Marsh almost indubitably losing his spot for the tour to the West Indies, the quest begins for a true number 3 batsman.  That role is likely to at first go to Shane Watson, with perhaps Peter Forrest hijacking a spot on the tour bus with some good One Day International form.  Behind those two and the dubious credentials of Dan Christian, Australia simply doesn't have batsmen with the requisite body of First Class work pushing the national incumbents for selection.

How bad is Australia's batting talent drain?  The following chart displays how well Australia's batsmen have performed in First Class matches this season.  They are divided into four quadrants according to the mean age and First Class batting average.  Any First Class player with pretenses to batsmanship (ie. allrounders) are included.  Click on the chart to zoom in.


As you can see, very few young - or even average-aged - batsmen earned their keep, let alone a shot at the Australian team by virtue of their form.  That two of the best-performed young batsmen were wicket-keepers (Matthew Wade and Peter Nevill), stands as testament not only to their talent but to a relative dearth of "comers".

The graph becomes even more stark when narrowing it by appearances.  In the following chart, only players who played at least three Sheffield Shield/Test games were considered.  Based on form alone, we can suggest those players rising most above the average for their age are best placed to take over from the current older generation.  These include Forrest, Liam Davis, Tom Cooper - he of Netherlands' fame - and Christian.


 While Taylor (and Steve Waugh) so helpfully proved that form is hardly permanent, those players in the bottom right quadrant should be those most concerned.  It is a tried and tested premise of sport that fans and administrators alike - want one of two things: success, or hope for the future.  Based on this year's form, those in the bottom right quadrant are hardly likely to offer either.

Wednesday, February 22, 2012

Comparing EPL efficiency across years

Last week I posted a graph which showed the Offensive and Defensive efficiency of teams across Europe's four major leagues.  The chart was measured teams' conversion rates against the number of shots they faced before conceding a goal.  As with all statistics, this is informative in isolation, but doesn't provide a full understanding of the situation without further context - as in,without further information we can't say if Liverpool's offensive profligacy is a one-season trend due solely to Luis Suarez's left boot, Andy Carroll's relative lack of presence or even if Steven Gerrard's long-term absence contributes to such a statistical  malaise.

In order to answer these questions, we need to compare this year to others.

The following charts show first the change in Offensive/Defensive efficiency for each team in the 2010-11 and 2011-12 Premier League, and secondly the change over the last three years (when shots/shots faced statistics became more readily available).  In the first graph, lines chart the year-on-year change for each club.  Such lines aren't present in the second chart as they would make the chart practically unintelligible.  Enjoy!

I  highly recommend clicking each image to enlarge it.

Friday, February 17, 2012

The most efficient teams in Europe

The following chart was inspired by a similar chart on the blog Experimental 3-6-1.  It displays the ratio between the average number of shots a team needs to score a goal, versus the average number shots they face before they concede.  Click the image to zoom in.


From the graph, we can surmise that Borussia Moenchengladbach has by far the sturdiest defence, while Freiburg offer about  as much resistance as wet paper.  The most inefficient teams in front of goal, however are Cesena and Kaiserslauten.  It's probably no surprise that both are in severe danger of relegation.

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.