Advancing Sports Analytics Through AI Research
I&#x27;ve done some work in sports analytics for an NBA team, and while I think there is some value there, I also think that most of the low hanging fruit has already been plucked at this point. People have been pointing to the same examples of &quot;Moneyball&quot; in baseball, or 3-pointers in the NBA, for at least a decade at this point. The reality is that for a lot of sports, they are complex enough systems that even if insights are generated, they are complicated enough that its hard to action on them. Look at the animation of the predicted player movement on the field. Its pretty, but what can a manager or player reasonably <i>do</i> with that information? Not a lot. Right now, it seems like most analytics in sports are solutions looking for a problem, which is understandable so many of us in ML and statistics are sports fans. But ultimately, I don&#x27;t see much impact being made, certainly not on a Moneyball-esque scale.<p>There are still areas where analytics will continue to be important in sports and player assessment&#x2F;team composition will always heavily rely on it. However the next steps for sports analytics in my mind are things that go on behind the scenes of the sport: personal training routines for players, managing wear and tear and predicting injuries, etc.<p>Also, the leagues themselves have lots of room to improve their products with analytics. Many complaints have been raised (which I agree with) about how boring modern basketball&#x2F;baseball has become due to analytics causing teams to converge on strategies that maximize efficiency but are also quite boring. It isn&#x27;t on the players&#x2F;coaches to try something more exciting at the expense of winning. It&#x27;s the league&#x27;s job to make the product interesting. Analytics can be part of the solution, like determining that by pushing the 3-point line out several feet, the value of the shot is brought in line with other options that simply aren&#x27;t viable right now.