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Next time you're off to the bookies to place your footie bets, you might be better off consulting a statistician than a football expert. On his Understanding, uncertainty website self-confessed football un-enthusiast David Spiegelhalter used a simple statistical model to predict the results of the last ten Premier League matches, which were played on the May 24, 2009. In terms of predicting whether a game ended in a win, draw, or defeat for the home team, Spiegelhalter's model was right nine out of ten times, compared to the seven out of ten score achieved by the official BBC football expert Marc Lawrenson, and the model predicted two scores exactly.
Spiegelhalter and his co-authors Mike Pearson and Ian Short quantified the individual teams' attack strength and defense weakness based on their past performance, and then, with a little help from probability theory, used these ratings to work out the most likely outcome of a particular match. (You can see the details of this model on his website.) "These types of models have been refined over the years and are now used by bookies and sports betting companies, who employ experienced statisticians and make use of the latest computational methods," says Spiegelhalter. But he concedes that his very basic model might have been a bit lucky this time: "One thing you can bet on is that simple models like this one will be very unlikely to out-perform the odds being offered by bookies, so don't use them to spot good bets!"
Tactical analysis in football has rarely been conducted using a mathematical model with numerical data (although tactical analysis through objective data has been used more often). Therefore, Akira Yamanaka and Hiroshi Otsuka with their team made a study to establish principles for tactical analysis in team sports using numerical data, through a mathematical model based on the location of the players and the ball.
The main procedure in the research flow was to establish a mathematical offence/defense model based on tactical concepts in football, which was applied for the location of players, which, in turn, was quantified from video images in order to categorise a team's tactical performance (in relation to attacking or defending). Furthermore, the authors focused on attacking categories and identified different types of passes during a specific period, as well as comparing these findings with an actual match video. The results obtained from the numerical data derived from applying the offence/defiance model led to the same overview as the tactical analysis produced by a team analyst. In addition, the results when categorising types of passes (as extracted through the mathematical model) again mirrored those retrieved from an actual match video. This leads to the conclusion that the offence/defence model could provide relevant insight into types of attacks.
The data revealed that football tactical analysis can be successfully performed using a numerical model, which might possibly enable automatic tactical analysis of football games without a match analyst.
Read the full paper at the journal of Insight - Sports Science:
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