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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Month: January 2016
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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It’s the variance, stupid
Things happen when a top club draws with a squad at the bottom of the fourth division, even when the top club played a team of young prospects. People start wondering if those young prospects really have what it takes. People start to speculate about how the young prospects arrived at the club. And people start to point fingers. A few months ago I asked if it was possible that the vaunted Liverpool analytics department, with its seat on the club’s transfer committee, simply wasn’t very good. After all, the public knows almost nothing about its activities and its influence…
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