When compared to other industries, the world of sports has been by far one of the most resistant to analytics. A combination of longstanding truisms, respect for ‘gut instinct’, difficulties in application, and aversion to outside influence, has seen some sports take an age to adopt data with pioneers ridiculed for their rejection of tradition.
And, in the sports that have embraced the analytics revolution, effects on the game have varied wildly. In baseball, numbers turned the sport on its head - they took Billy Beane’s previously unsuccessful Oakland As to the playoffs in 2002 and 2003 after the coach realized that, despite his team’s limited funds, data could be used to find otherwise overlooked gems. As is the standard arc with the likes of Beane, he was at first ridiculed and later revered, as those in the sport were won over if not by his ingenuity but by his results.
In professional rugby, though, numbers have ultimately damaged the sport - it’s a case of function over form. The numbers behind the sport revealed what many already knew, that playing a kicking game is the safest way to collect points. Keeping the ball, particularly within one’s own half, is dangerous, and the cost of making a mistake there is high.
This manifests itself in an ugly way, with teams ceding possession through kicks and playing primarily for territory. Particularly at international level, teams will sacrifice the ball with the acceptance that the team with possession is more likely to make a mistake. If Rugby’s reliance on data has done anything, it’s made the game more one of incremental gains with the boot and less one of exciting wide play. The issue is such that just about every professional body in the game has considered altering rules to make kicking less profitable.
Soccer, uniquely, is a sport that reaches accepted dogmas with some regularity, only to have them equally routinely dismantled and discredited when the next rolls around. And these dogmas have stemmed from analytics, however crude, for some time. Since statistics were noted and data recorded, those in and around soccer have been analyzing it and making arguments as to the ‘right way’ to play the game.
Take, for example, Charles Reep, the man credited as the ‘founding father’ of soccer analytics. In 1950, the former RAF wing commander began collecting data on Swindon Town’s ineffective attacking performances, from which he drew a number of insights. Reep concluded that moves consisting of three or less passes were more likely to end in a goal when compared with plays with a higher number of passes. So certain was Reep that his findings were correct that others bought into the primitive philosophy, and the amateur analyst is credited with the birth of the English long-ball game.
Reep’s now debunked findings are indicative of the problem with a wholehearted commitment to analytics - data can be both collected and manipulated with an end in mind, or mistakenly applied to support a flawed idea. Soccer’s revolving door of trends currently sees European soccer in particular in the dying stages of an era that sees possession as an end in itself. Soccer is perhaps the sport most resistant to analytics given its fluidity, but there is evidence enough to suggest that more possession equals more chances created equals more goals. Such is soccer’s unique fluidity, though, that against a team playing possession football, a counterattacking game - i.e. quicker, longer passes after soaking up pressure - is often the most effective tactic. Soccer is too reactive to land on best practice.
There’s an interesting case in basketball at present in which teams are attempting and making more three-point shots than ever before. The reason? Players have become better at making three pointers, and as a result, the risk/reward balance when attempting them has shifted in their favor. Per attempt, three-point shots are worth more. Now, the NBA has gone mad for long-range efforts, but it’s college basketball that is the true home of three-point extremism; some 35.2% of college shots are three-point, compared to 28.2% in the NBA.
Again, though, the data needs to be properly deciphered before all teams start making three-point shots the priority - it cannot exist in a vacuum. Hitting three-pointers is all very well if you’ve got Stephen Curry in your team; the point guard is arguably the greatest shooter in NBA history and is probably distorting the data singlehandedly. When the great long shooters like Curry fade, though, will the reliance on three-point attempts fade with them? Time will tell, but if basketball is over-reliant on that nugget of now received wisdom, teams without a Stephen Curry will struggle.
There’s absolutely no doubt at this point that data has a firm place in sports. Teams that apply intelligent insight from data collected, across all sports, will be able to improve weak spots in some cases and get a leg up on the competition in others. The manager will never be replaced by the statistician, but it’s the job of the coach to uncover which metrics to focus on to help the team - every sport has been and will be affected by the analytics revolution, only its a shift more welcome in some sports than others.