Anyone who watches soccer knows that even the world’s best strikers may sometimes fail to score for a few matches in a row. But not every club has the luxury of employing one of the world’s best strikers. Let’s say your club has a fairly average striker. What will his cold streaks look like? To answer this question, let’s create an imaginary striker who takes four shots per match in 25 league matches per season. Two of his shots in each match have a 5% chance of scoring, one has a 10% chance, and the last one has a 40%…
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Metrics
How will that new signing perform?
Can we predict how soccer players will perform when they switch leagues? It’s a question on the minds of many clubs’ coaches and executives during this European transfer window. A host of factors can influence the answer: playing style, role in the squad, age, adjustment to a new home, and more. Today I’ll focus on just one, league quality. Adjusting player ratings for league quality is a critical task when using analytics for recruitment. NYA computes adjustment factors between leagues all over Europe and sometimes further afield as well. I don’t want to disclose too much about the methods, but…
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Finding the weak link
Even the best teams have a weak link. In “The Numbers Game”, Chris Anderson and David Sally suggest that a soccer team is only as good as its worst player. I’m not quite ready to sign on to their “O-ring” theory yet, but I do have a tool for finding the players who drag down their teams or simply don’t fit. Shapley values are a sort of sophisticated plus-minus or with-or-without-you metric. I use them to create a series of statistical hypotheticals that answer the question, “If I formed this team in every possible order, what would the average contribution…
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