Sports by the Numbers: Predicting Winners and Losers

Over the past six weeks, Wharton statistics professor Abraham Wyner and several MBA students, along with Wharton practice professor of legal studies and business ethics Scott Rosner, studied data provided by ESPN to determine whether the amount of money that sports teams pay for their players can predict how well the team will perform. Here is what they found:

Surprisingly, among the major American professional sports, it is ice hockey for which performance is most predictable, given team salary data. In baseball, salary matters a lot, but there is still tremendous uncertainty as to predictability - i.e., teams that don't spend a lot can actually win more games than teams that do. In football, how much a team spends on its players has very slight predictive power, due to a strong revenue sharing system and salary caps, among other reasons.

Basketball is not as easy to predict as hockey or baseball, but it's easier than football. Again, such factors as individual coaches, strategies and combinations of teams and players can all influence outcomes. The fifth sport, European soccer, was by far the most predictable of the five. Based on data related to the European premier league, Wyner and his students concluded that the teams that spend the most money on players win year after year. "Not only do they win, but we know they are going to win," says Wyner.

While his analysis of the five different sports is relevant to owners and managers of sports teams, it is not part of his statistical research, which focuses more on scientific fields. But one research area overlaps both business and science: data mining - the process of discovering patterns out of large data sets, or as Wyner describes it: "extracting gems from a mine full of stuff, some of it valuable and some of it not, and looking for patterns that you can apply to your business."

... In hockey and baseball, Wyner concludes that defense is a more productive investment than offense (pitching is considered defense). In football, however, Wyner and his team found no obvious value proposition. "We're not sure why, except that maybe it's because on-field performance is so unpredictable on a yearly basis. Coaching, and the entirety of the team, matter more than any individual player you can acquire, even a quarterback. The problem is, there are too many quarterbacks who are paid a lot of money who don't do well. That distorts the statistical view." Football teams are built to be on parity with one another, Wyner adds. "Whatever they are doing to make football competitive, it's working. It's good for the fans and the owners, probably not so great for the players."

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See also a paper Wyner co-authored: Bayesball: A Bayesian Hierarchical Model for Evaluating Fielding in Major League Baseball.