Big Data in sports

According to the New York Times, National Football League teams are often years behind their peers in other professional sports when it comes to data analysis

Machine learning could help teams make more sense of the myriad variables that currently keep them relying on human intuition.

When it comes to using data to determine how to build a team or manage a game, the National Football League appears years behind its professional sports brethren such as Major League Baseball and the National Basketball Association. But perhaps the increasing popularity of machine learning can change that by helping NFL teams make more sense of their very complex datasets.

Delving deep into the world of computer science might sound like overkill, but professional football is big business in America, and an analytic edge off the field might be just as important as athletic or strategic edges on the field. Heck, it might help create them.

The New York Times highlights the current state of statistical reliance among NFL teams in an article. The NYT’s Judy Battista reports that teams are finally beginning to hire statisticians and take statistical analysis seriously in limited areas — but there’s always a disclaimer. Football is such a variable-rich and complex game, her sources claim, that the human eye and human intuition will always be best at assessing certain things.

As one anonymous source put it when discussing the difficulty of evaluating players before the NFL draft: “At the end of the day, the tape is going to be our first choice. They have to look good on film.”

His point and those of others with whom Battista spoke are fair. For example:

– Offensive line play can be difficult to gauge because the line is a five-person unit designed to work well together, not as a collection of individuals.
– How do you statistically assess a middle linebacker who doesn’t make a lot of tackles but who’s always in the mix and disrupting the offense?
– When it comes to calling plays, there might be limited data on any given situation (e.g., a particular down and distance to go from a particular spot on the field), and the outcomes might be very much influenced by the players on the field in each of those prior situations.

This is a lot different than in a sport like baseball, NFL analytics expert Tony Khan told Battista, where it’s much easier to break down statistics to an individual level and make Moneyball-like decisions about given players and circumstances.

Maybe that’s why there’s an innate anti-statistic bias among many NFL executives, as well as those in other leagues. Dashiell Bennett at The Atlantic uncovered a lot of disbelief about the statistical analysis of major sports leagues even at the MIT Sloan Sports Analytics Conference in March. “[T]ime again,” Bennett noted, “… when a reasoned and ably researched idea was presented, we heard some variation from those in the crowd of ‘That’s interesting, but…’.”

However, companies of all sorts are increasingly using machine learning algorithms (and related techniques) to detect patterns and correlations among complex datasets, and there’s a growing number of software products hitting the market that either incorporate machine learning or are built entirely upon it. It stands to reason that NFL teams might consider giving these techniques and technologies a chance, too.

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