Where data meets sport, a revolution has taken hold where only a few short decades ago, such a thing would have been considered improbable. The stat-obsessed sports “wonk,” once an isolated breed, now prospers with the advent of advanced statistics, machine learning, and so much real-time data. From fans to franchises, as sports continue to be inextricably linked to the practice of data-driven decision-making, so too has the paradigm shifted in how we consume, break down, and contextualize sport.
In this examination of the rise of sports analytics, we will dive into how data is shaping everything from draft picks to in-game strategy. Along the way, we’ll also address the resistance to the analytics movement, its implications for the future, and why some fans still prefer the old-school, “gut feeling” approach.
The Roots of Sports Analytics
The analytics movement in sports did not materialize overnight. It has its roots in the back offices of front offices, in nerdy baseball discussions, and with people like Bill James, the father of sabermetrics-the analytical study of baseball statistics. Sabermetrics spawned the now-famous “Moneyball” philosophy, popularized by the 2003 book Moneyball: The Art of Winning an Unfair Game by Michael Lewis. How the concept was applied by Oakland A’s general manager Billy Beane showed the way for smaller market teams to fight big spenders by targeting undervalued players and statistics that traditional scouts wouldn’t or couldn’t consider.
What started off as a very niche methodology in baseball trickled down into other sports. Advanced metrics like Player Efficiency Rating (PER) and Effective Field Goal Percentage (eFG%) are mainstream in basketball today. It’s all about expected points added and win probability models of coaches and analysts in football these days. Data has taken centre stage for player recruitment and tactical analysis in soccer, too-a sport earlier believed to be way more difficult to quantify. Even individual movements of players are tracked and analyzed since the wearables and tracking systems came aboard.
Data’s Influence on Strategy and Tactics
Data’s influence on strategy might be most evident in basketball, where the “three-point revolution” was a product of analytics. Teams found through detailed analysis that the three-pointer was a significantly more efficient shot than the long two-pointer. This shift in strategy-taking more threes and fewer mid-range jumpers-has transformed the way the game is played and has led to a rise of teams such as the Golden State Warriors, who built a dynasty around this type of play.
In football, too, data has driven an explosion in aggressive decision-making at mid-field, particularly when it comes to fourth-down attempts. Historically, coaches in the NFL have taken a conservative approach, always punting or trying a field goal in most situations on fourth down. Analytics have now shown that going for it on fourth down, particularly in short-yardage situations, leads to better long-term results. This has been perhaps epitomized by teams such as the Baltimore Ravens, whose head coach, John Harbaugh, has publicly embraced the influence analytics now has on his play-calling.
Even in baseball, the greater use of shifts-positioning fielders in nontraditional spots, according to the tendencies of hitters-and bullpen specialization-using more pitchers for shorter stints of work-have transformed the tactical landscape. Once resisted, these shifts are now all but universal as teams seek any statistical edge they can get.
Analytics in Player Evaluation
The draft has been one of the most scrutinized areas of player evaluation for a very long time. Traditional scouts relied heavily on things like intangibles and physical measurements such as height, speed, and arm strength. However, analytics have really changed the game in the way teams assess and compare potential draftees. In the NFL, for instance, the “Relative Athletic Score” aggregates various physical metrics into one number that teams can use to compare prospects across multiple decades.
Advanced metrics have augmented college performance in the NBA’s attempt to project a player’s impact on the professional level. This can provide an analytical lens to help teams avoid overvaluing players who put up flashy stats in college systems that may not translate to the NBA style of play. This same trend is reflected in European soccer, with clubs utilizing advanced algorithms in a continuing attempt to find talent that the clubs themselves consider to be undervalued in leagues such as the lower ones or those outside the top five in Europe.
Newer approaches have also involved the use of machine learning and predictive models. Predictive models project player performance into the future based on past performance, injury history, and other relevant factors. In baseball, technologies such as Statcast have tracked things including but not limited to exit velocity, or the speed of the ball leaving the bat, and sprint speed, a measure of player running speed, in order to provide teams with a better understanding of their players and free agents.
Resistance and Pushback
Not everyone in the sports world, however, has embraced the movement despite such clear benefits from analytics. Many traditionalists argue that data cannot measure intangible factors, such as heart, leadership, or chemistry. To many of these people, sports are about more than numbers, and to try to reduce a game to a series of metrics siphons the human element that makes sports so compelling.
One of the most vocal critics of analytics was the Hall of Fame basketball player and broadcaster Charles Barkley, who once famously said, “Analytics is crap. Analytics don’t work at all.” It would seem as though Barkley’s comments seemed to mirror what many were thinking-numbers alone could not tell the complete story of a game or player.
The greatest challenge facing analytics supporters is that data is often only as good as the person interpreting it. If misapplied or misunderstood, analytics can lead to poor decisions. For example, in the 2017 NFL Draft, the Chicago Bears traded up to select quarterback Mitchell Trubisky based largely on analytics and potential upside. Trubisky failed to perform to the expected level, and the move was widely regarded as a failure.
Future of Analytics in Sports
With technology continuing to get better, the depth and breadth of analytics in sports will only improve. Wearable technology, like GPS trackers or heart-rate monitors, delivers real-time information on the state and level of exertion of a player’s body. In soccer, for instance, teams now track players’ sprint speeds, distances covered, and even the force of their tackles.
It also will involve more applications of AI and machine learning. In basketball, AI models can simulate thousands of possible outcomes for in-game situations. Coaches can thus make better decisions. Baseball could apply machine learning to offer a projection based on the biomechanics of a particular throwing motion relating to the injury risk potential of a pitcher.
And fan engagement can even change with analytics. Broadcasting companies are starting to integrate live data into their game feeds so that viewers can see, during broadcasts, win probabilities, player efficiency ratings, and other advanced stats. For die-hard sports wonks, this adds an extra layer of depth to the viewing experience.
Conclusion
The analytics revolution has dramatically shifted how we think about, play, and consume sports; from the front office to on-field strategy, numbers are king, and the sports wonk is no longer the exception but the rule of the modern game. Resistance to analytics most definitely still exists, but undeniable success brought on by data-driven strategies across sports suggests this trend is here to stay.
And as the technology supporting analytics continues to evolve, so too will our understanding of what constitutes building a winning team. For now, sports wonks everywhere can revel in the fact that their passion for numbers and data is finally paying off. Whether through identifying the next draft steal, perfecting in-game strategies, or enhancing fan engagement, the future of sports is shaped by the power of analytics.
