How Analytics Ruins Sports

With the recent success of the Rockets, people are trotting out that old saw about analytics nerds ruining sports. With the Houston Rockets specifically, the question is a combined referendum on the numbers-based approach of GM Daryl Morey and the foul-drawing proclivities of Houston’s two stars, James Harden and Chris Paul. Of course, the latter is linked with the former, since analytics shows us that drawing shooting fouls is extremely efficient offense. But this is just a modern version of a question that’s been asked for many years. And those articles don’t have nearly the level of vitriol that surrounded this discussion in the early 2000’s.

Whether it’s baseball or basketball (or soccer or hockey), once modern quants get a hold of sports data and start influencing decisions from management (or players!), people ask wring their hands over how changes will effect the viewing experience, and the assumption is always that it will effect it in a negative way.

And these concerns are justified: Analytically-efficient teams will make any sport less enjoyable for a sport’s most ardent fans.

People don’t like change

The simplest reason for this is beacuse sports analytics (or statistics as they were called back when it was just Bill James doing it) will tend to find ways that teams can improve their chances of winning. This means changing the way you’re playing, and people don’t like change. This is especially true for a sport like baseball, which has a long and cherished (if checkered) history.

Strategies and tactics in games like baseball and basketball always evolve, particularly as athletes improve their skills over the years. But analytics tends to accelerate that change, bring new ideas to the forefront more quickly than they would have otherwise. (This is why analytics are valuable!)

Think about it like an economist for a moment: The biggest fans of a sport will in some ways be self-selected for enjoying aspects of that sport that occur frequently and with high importance. Aspects of individual or team skill (a stolen base, a behind-the-back pass) appeal to fans, and fans who love games most intensely will tend to appreciate the particular skills that the game values highly. This means that whenever anything causes the mix of skills or tactics used within a game to change, you’re going to get grumbling from fans and traditionalists.

In baseball, it’s fun to watch speedy, slap-hitting contact hitters who can put the ball in play, get on base, and then force the action on the base paths with their speed. However, for a variety of reasons, we’re now seeing fewer balls put in play and more at-bats that end in a strike-out or walk. This makes the game less fun to watch, especially if you grew up loving folks like Ichiro Suzuki or Tony Gwynn.

What matters for winning?

But accelerating change isn’t the main way analytics ruin sports. The thing that really gets folks angry is when the analytics community points out that particular skills which here-to-fore had been highly valued are actually not that important for winning. (As well as the opposite, when skills which aren’t highly valued are shown to be hugely important.) This is also why players in particular will push back on analytics: If you’ve worked for decades becoming an expert in a field only to be told (by some neophyte no less!) that the skills you’ve prioritized developing are actually rather unimportant, you’re going to be pissed!

One way to think about sports is as a non-linear process that produces wins and losses for teams. The inputs to this processes are the skills of the players involved and the tactics employed. Every player, coach, GM, and fan has a mental model for what this process looks like, but none are completely accurate. I may think a team should be running more high pick-and-roll to maximize their chances of winning, but you might think they should work towards a favorable match-up in the post. I might think the bruising PF gives us a better chance by improving our rebounding but you might favor a stretch-four who gives our offense more room to operate. A player might think the best thing for him to do in a particular instant is to crash the boards while the coach may want them hustling back on defense.

What analytics does is quantitatively derive an estimate of this process. Or at least help us refine our existing mental models of that process. By illustrating that on-base percentage is more useful than batting average alone for evaluating players, the baseball analytics movement forced fans, players, and managers alike to change their own thinking about what made a good baseball player. Basketball is undergoing similar changes now, as the value of 3-point shots is forcing us to rethink how we evaluate certain players, both in the current game and historically.

Misaligned incentives

Really, the two points above aren’t that different. Analytics is useful in the sports context when it identifies misalignments in the existing incentive structures for players, coaches, and managers (what plays to run, what tactics to employ on the court, how much to pay for certain skill sets in free agency or via trade, etc.) and ones the sport contemporaneously runs under. This leads to changes in behavior on the court (more 3s, more attempts to draw fouls, etc.) and off the court (not paying for Power Forwards who can’t shoot). However, this can cause misalignment between what fans want to see and have learned to love and value over their years of fandom and what actually matters for winning.

It’s this misalignment that “ruins” the game for fans. Because analysts are getting proficient at showing exactly which plays and players are the most useful for winning, teams that focus exclusively on winning will tend to be less fun to watch.

Analytics ruins sports not by creating this misalignment but by pointing them out.