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The book Moneyball popularized a new approach to baseball taken by Billy Beane and the Oakland A's. Beane believed that the traditional analysis of baseball was flawed: stats such as batting average were poor predictors of how many runs a batter would score. He also believed the collective wisdom of managers, coaches and scouts to be subjective and prone to bias.
Beane understood that a statistical record of a player's past performance was a much better predictor of future performance than expert opinion based on watching only a few performances. Beane became manager of the A's in 1998: by 2002 they were consistently performing above average with a wage bill 20% as big as the New York Yankees'.
Since 2006 the A's have not been as successful: Beane's secret is out and most Major League teams now have their own stats departments. We are currently witnessing a similar revolution in basketball: The general manager of the NBA's Houston Rockets is a computer science graduate, and Beane's approach has been replicated by many NBA teams.
Football is not Baseball
Most of baseball is a series of one-on-one encounters between pitcher and batter. Many pitches are seen during a match and each ball tells you something about the quality of the players involved. Football is a highly collective game where each player's performance depends on the performance of his team mates. There may only be five saves and two goals in a match - not much on which to base player analysis compared to baseball.
Does statistical analysis work in football? Yes. Since 2002, Decision Technology's football predictions, published in the Times every Saturday, have routinely outperformed tipsters and experts and in five seasons out of six outperformed bookmakers.
I would like to give some examples of research that Decision Technology has done for the Times Fink Tank column to illustrate how statistical analysis can be applied to teams and players.
How Do You Win A Game?
By scoring more goals than the opposition. One method of measuring a player's contribution to a match is to work out which actions correlate with scoring and conceding goals. If a player generates lots of shots during a match we are likely to see a goal. If a keeper has a high save percentage, his team is unlikely to concede.
Using data from Premier League matches between 2000 and 2009 I asked the question "How many points did a team win given that they had more corners than the opposition?" Likewise, we can ask the same question for passes, shots or anything we like.
The results? Teams with more shots won 1.93 points per match - much better than the league average 1.37 points per match. How valuable is a corner? Teams with more corners won 1.48 points per match.
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