Analytics ruin sports. No, they add new depth to an old game. Analytics turn classic gridiron clashes and ball diamond dust-ups into soulless points and clicks. Uh-uh, analytics improve the competitive balance of leagues and create more exciting games.

Ask a fervent sports fan about the role of analytics in competitive sports, and you’ll get an opinion — likely, a strong one. Old-school fans and players grouse that analytics mark the ruination of the sport while numbers fans marvel at the “launch angle” a baseball batter exhibits or what the “expected point value” says about the design of a particular basketball play.

That’s because competitive sports have always been about quantifying and comparing one performance to another: who has the most runs batted in, who posted the best lap times or who scored the most goals. All serious competitors look for anything they can do (within the rules of the league, one hopes) to improve their performance and enhance their chances of winning, even if that edge seems vanishingly small. Bicyclists shave their legs to reduce wind resistance by 1/100 of a second per lap, for cripes’ sake. By comparison, using statistical analysis to find performance improvements seems fairly pedestrian.

What’s different today is the way analytics can look at a game both from a broad perspective and at individual actions at the same time, revealing new connections so valuable that they are changing the way games are coached, staffed and played. That’s probably at the root of fan and player discontent. Football game plans are formulated only after an exhaustive analysis of the team’s past performance against an opponent to understand both teams’ tendencies and their ability to execute certain types of plays. Following the numbers, defensive shifts once frowned upon in baseball have become commonplace, and batters now try to create fly balls that increase their chances of getting on base. Analytics are even changing the qualities recruiters are looking for in new players. In basketball, for example, the speed and agility often characteristic of smaller players is gaining favor. After decades of drifting higher, the average height and weight of players is starting to drop.

The biggest impact may be the degree to which analytics can, figuratively speaking, level the playing field between small-market and large-market teams. Teams in major metropolitan markets with hefty revenue streams from a large fan base and broadcast revenues can offer big contracts to the league’s most talented players, putting small-market teams at a competitive disadvantage. By comparison, the investment in analytics is comparatively small and affordable enough for even small-market teams. What these teams learn from their analyses can help them spot “bargains” among high-performing players, identify and exploit the weaknesses of more moneyed teams or change the nature of the game altogether.

Houston Rockets general manager Daryl Morey, a former statistical consultant, explained to The Economist Films how he used analytics to reset the team’s thinking on the value of three-point shots. Morey’s analysis revealed that the 50 percent gain in points from a successful three-point attempt outweighed many two-point shot attempts. Morey’s insight led the team to redraw the playbook and look for ways to maximize three-point attempts, not avoid them. The team’s improved shot selection helped the team rise from its usual middle-of-the-pack location to the top of the conference nearly overnight.

Houston’s widely noted success has hastened the adoption of analytics around the league, which means other teams will adapt, strategies will change and Houston’s current edge will diminish. That’s the kind of competitive dynamic that keeps leagues fresh and interesting. And it’s all thanks to the numbers.

Argue all you want, fans, this one goes to the geeks. Sports analytics are here to stay.