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The Sports Industry: A Petri Dish of Data Analysis

Introduction


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of nearly every great professional basketball team since the second World War, was essentially made ineffectual and outdated.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.


Figure 2: Dirk Nowitzki of the Dallas Mavericks, one of the earliest players to win a championship through the use of analytically-powered game plans.


A Hard Lesson Learned


This hesitancy to embrace analytics is not unique to basketball and it’s not entirely unfounded either. Many regrettable decisions have been made by sports analysts and executives who fall victim to stat fetishization or misinterpretation. One could have recently seen this play out in the National Football League under Head Coach Brandon Staley of the Los Angeles Chargers who came under fire for his analytics-driven decision to often go for it on 4th downs rather than punting or kicking a field goal. Going for it on 4th down is a risky concept in football; you risk giving up valuable field position by forgoing punts or even leaving points on the field by not taking a field goal. While it has its risks, converting on 4th down can be a major boon to a team; retaining possession of the ball, demoralizing the defense, and temporarily denying the other team an opportunity to score can all drastically improve your chances of winning. Despite converting on the majority of their attempts, their failed attempts would ultimately cost the Chargers a couple of games resulting in their missing of the playoffs.

How did an analytics-based decision that worked the majority of the time end up sinking the Chargers’ season? The likely culprit is that the analytics team within the organization became fixated on a particular adjacent figure such as the value of a Touchdown over a Field Goal and found a number appealing enough to hardline adjust their game plan. Without speculating too much, it’s safe to say that their process ignored the context in which these decisions would be made. To clearly illustrate this, we can look at a specific example in Week 15 where the Chargers hosted the Kansas City Chiefs in the final stretch of a race to the playoffs. Division rivals and competing for the same playoff ticket, the Chargers at 8-5 needed a win over the 9-4 Chiefs if they wanted any realistic hope of locking themselves into the postseason.


At halftime, the Chargers found themselves in the lead with a score of 14-10 despite already forfeiting two scoring opportunities within the 10-yard line by turning it over on downs. Holding on to a tenuous lead after allowing two fourth down stops to reinvigorate the Chiefs’ defense, the Chargers just needed to put points on the board and hold on to their advantage for two more quarters. Getting the ball back after conceding a field goal to the Chiefs on the opening drive of the second half, the Chargers were able to drive 47 yards down the field to the Kansas City 28-yard line where they had to make another game defining decision at 4th and 2. The Chargers elected to keep their offense on the field and Justin Herbert attempted a short pass to Tight End Jared Cook before it fell to the ground incomplete.

After an adrenaline filled second half, the dust settled to show a 34-28 overtime win for the Chiefs and the focus immediately shifted to three key Chargers possessions in regular time that all resulted in a turnover on downs after failing to convert on 4th down; three possessions that ended within 30 yards of the endzone and three missed opportunities to put points on the board by kicking a field goal to win it in regulation. By blinding themselves of the situation’s context and being unwilling to adjust, the Chargers cost themselves 9 points and ultimately a postseason appearance despite having an incredibly talented roster.



Figure 3: Patrick Mahomes leads the Chiefs over the Chargers in week 15.

The ’21 Chargers are a special case, and their hardline approach isn’t typically echoed by other teams within the league but that’s not to say that other teams don’t incorporate analytics. Using league data, many teams have discovered that passing on first downs is far more efficient than running the ball and we’ve seen a significant uptick in first down pass plays compared to just a few years ago. Teams have also become far more aggressive on fourth downs in recent years, however, not to the reckless degree of the Chargers. Overall, as more data has been used to configure game plans, offenses in football have become incredibly efficient, mirroring the transformation of the NBA.


A Final Note on Proper Execution


Despite only relatively recent widespread adoption, data analytics has revolutionized the sports industry. As with any industry innovation, there will be some bumps in the road and it’s imperative to learn from these instances. The failures of those who are unable to see beyond a single stat or properly interpret the data they have are able to shed light on the importance of context when working with analytics. Being able to take a step back and see the big picture rather than mistakenly hyper-fixating on a singular facet of your dataset is paramount to maximize organizational success. Data can be an exceedingly powerful tool, fully transforming organizations into efficient engines and allowing them to reach their previously untapped potential, however, misinterpreting that data or ignoring the context in which that data exists can be incredibly counter-productive and can actively damage progress towards whatever goal you and your organization are trying to achieve. For this very reason, you should always entrust your data with professional analysts who are qualified to properly interpret it. If your organization would like to responsibly maximize efficiency through the use of data analytics, contact Bear Cognition today!

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