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The Sports Industry: A Petri Dish of Data Analysis


The sports industry has long had a tumultuous relationship with analytics and has only fully embraced it within the last decade. Due to its traditional unfamiliarity with data and hyper-competitiveness, the sports industry as an entity has become a wild west of sorts for analytics. With very little oversight and free reigns in an ever-growing data driven arms race, sports analysts have provided a perfect lens with which we can examine the use of data.

Figure 1: The meteoric rise in 3-point shooting across the league has completely transformed the game.

It would be difficult to miss the recent impact that analytics have had on the world of sports. The current standard of having an entire analytics team or even department within your organization is in stark contrast to the singular analyst you’d have on staff just a decade ago. This has inevitably led to just about every sport evolving alongside data in ways that have made it nearly unrecognizable when compared to earlier versions of itself. Powered by analytically constructed game plans, offenses have become exceedingly efficient to the point that most defenses can only hope to slow them down rather than stopping them. For example, one could simply look at the National Basketball Association pre and post “3 Point Revolution” and note how in 2011 the Orlando Magic led the league with 2,103 three-point field goal attempts. Despite the 2021 season being 10 games shorter than the 2011 season, 28 of the 30 teams in 2021 would eclipse that 2,103 three-point attempt mark. By harnessing the power of data, NBA teams have experienced a meteoric rise in offensive production, scoring at an unprecedented level; in the last decade, the average points scored per 100 possessions has risen an entire 6.6 points.

The NBA’s Data Revolution

The influx of analytics into the world of basketball in the early 2010’s drastically changed the game and created some of the most dominant teams we have ever seen. Almost overnight, the traditional big man, who had been the centerpiece of nearly every great professional basketball team since the second World War, was essentially made ineffectual and outdated. Teams began to play and build around the concept of “Moneyball”, a reference to Moneyball and named after Daryl Morey, a General Manager in the NBA who pioneered the heavy and overt use of analytics in game plan and roster construction within the league. Rosters around the league began dropping size and moving their scorers away from the post and beyond the arc in order to capitalize on increased movement and perimeter shooting. Over the course of the last decade, the average height of NBA players has dropped half an inch and six pounds as teams continue to emphasize guards and wings rather than big men.

The near-fanatic level of adherence by many in the league to analytics hasn’t been without its critics however, critics including Hall of Famer and notable commentator Charles Barkley.

First of all, they (analytics) are just stats. They just gave it a name. It’s kind of like yoga. Yoga’s nothing but stretching.

Barkley’s statement isn’t incorrect and while it was intended to detract from analytics, it’s a fitting analogy. Yoga (analytics) is an organized and structured method of stretching (interpreting stats) but instead of building strength, improving balance, and promoting better breathing, analytics boost organizational effectiveness, efficiency, and growth. The greatest similarity between the two is that when properly utilized, both can be used to greatly reduce stress.