a, Behind the Bench, Sports

The mathematics of sports

Bill James first pioneered it for baseball in the 1980s, Moneyball made it popular, and now it is playing an increasing role within the dealings of NBA teams.

Sports analytics, as it is known,  is fuelled by self-described sports-loving stat geeks, and is the go-to metric for professional sports teams to get ahead of the competition. It has taken sports by storm, and given PhD scholars and die-hard analytical fans a place in the same front office as old-school executives. To plan new strategies, to better evaluate player performance, to improve overall outcomes—this has become an extensive, numbers driven game.

Analytics uses data to formulate models to make forecasts about future outcomes, however specific the domain may be. It goes beyond the scope of traditional box-scores to gain an edge over the competition. The vast amounts of data are all there; the hidden truths and patterns lie within. It is what Nate Silver, an influential political forecaster who started out as a baseball analyst, refers to as “the signal in a universe of noise” when making predictions.

Do these models, then, make instinct and in-game decisions and out-of-date plays irrelevant? No, to the contrary. Analytics can either validate or dispel those intuitions with numbers from collections of big data: from 82 games, to outcomes of shots taken from the same spot, to tendencies of a pitcher at certain distinct situations, to data points from the same combination of players on the court—the possibilities are endless.

One of my favourite basketball analytical tidbits is that not all three-point shots are created equal: the corner three is generally scored at a significantly higher rate than other spots, a statistically validated fact. To Gregg “Pop” Popovich, head coach of San Antonio Spurs, this means more plays drawn for “corner three specialists” like Danny Green or retired shooting specialist Bruce Bowen. Since the three-point line is two feet closer to the basket from the corner than it is from the perimeter, it is no wonder that players tend to shoot at a higher efficiency. Shane Battier of Miami Heat is another example of a corner three specialist, who lives and breathes from the corner. That is how he (mostly) earns his $3M yearly salary, by knocking down those shots, and in doing so, spreading the court and space for LeBron to dominate the paint.

While it may be tempting to be consumed by such models and detailed statistics—trust me, there are plenty to browse through on a leisurely Sunday afternoon—it is equally important to keep in mind that these models, while useful, are not black-boxes to the future. There is, and will always be, some form of inherent randomness.

When Billy Beane, the general manager of the Oakland A’s took charge in Moneyball, he had far fewer financial resources to play around with than his competitors, yet he still had to compete against high-payroll teams like the New York Yankees. As the modern pioneer of analytics, he trusted numbers and data over traditional methods of scouting and watching. His goal was to find undervalued players who contribute distinct value to the team and come together to provide a good return on the team’s small investment. The A’s, a small market ball club, performed well and Beane’s theories were defended in practice. The victory wasn’t only beneficial for the franchise, but it also changed the field of sports analytics. At that point, the revolution had just started. Now, it’s moving to the forefront of sports management—a reality that will change sports for the better.

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