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Football has found its new bogeyman


Sooner or later every sport will have an analytics revolution. MLB had Money ball in the early 2000s and has gone much further in the years since. The NBA has used efficiency to kill all but the mid-range jump shot. Football has seen an influx of countless new ways to measure passes and scoring chances in minute detail.

The NFL’s change became most apparent in 2018. Computer models watching thousands of games found an inefficiency: Coaches were too conservative on fourth place, when teams can kick the ball away or go for an all-or-nothing conversion . That year they got a little braver and tried to convert 15 percent of their fourth-place chances, up from 12 percent in previous years. It seems that the quants have won the battle for football’s decision-making soul. In accordance with various statistics, NFL teams now pass the ball now more than before; going into the current season had every NFL front office at least one staff member, and often many moremainly engaged in analytical work.

But somewhere along the way, football ended with an analytical backlash. On social media and on TV, fans and broadcasters constantly pillory the nerds. Last season, after Baltimore Ravens coach John Harbaugh came empty on a late two-point conversion to seal a loss, a team of CBS commentators took turns beating him like a piata. They’ll show you a spreadsheet and say, “Here’s why I made that decision,” said Nate Burleson, a talking head and former player. Another, Super Bowl-winning coach Bill Cowher, was blunt: paralysis by analysis. We overanalyze things. It’s not that hard. You can find similar analytic hatred in the college game. After Texas Tech University faltered in fourth place earlier this month, Fox play-by-play announcer Gus Johnson told Analytics! Throw them in the trash!

That is the crossroads where the sport exists in 2022. On the one hand, analytics have helped countless champions and made soccer, America’s premier entertainment product, even more fun. On the other hand, the fancy stats tear up football commentary and even invite scorn from coaches who have spent their entire career doing whatever it takes to win. The whole concept of analysis has become a football boogeyman that no one saw coming. Maybe we should have.

In theory, sport is the ideal place for intense math work. The stock market and weather, of course, are numerical, but were the only place you have a scoreboard, Alex Auerbach, a sports psychologist for the NBA’s Toronto Raptors, told me. Sports already quantify the most extreme way of benchmarking where people are, he said.

The simple box score has always been around, but even for casual football fans, advanced analytics are now inevitable: Amazon Prime Video, the new rights holder for Thursday night football, performs a statistical simulcast every week to the main broadcast. Player figures from the statistics and evaluation empire Pro Football Focus appear regularly Sunday night football. Delve into the depths of the football internet and you’ll find an alphabet soup of stats: Expected Points Added (EPA) per game, Completion Rate Above Expectation (CPOE), and DVOA (that nobody even knows about). by his full name). It’s a Sunday ritual to watch real-time robot evaluations of decisions in fourth place and on two points.

Some football fans love these innovations. Others very much not. On Twitter, a robot rating of a fourth down often leads to comments like this one from last week: I don’t want to see ridiculous stats like this anymore. Some football media, especially on TV, take a similar approach. It’s still reflexively negative, like, “The nerds don’t really know what they’re talking about,” Bill Connelly, an ESPN writer who covers sports through an analytical lens, told me. The end. Analysis has become an all-encompassing pejorative applied to any bold, unconventional decision a coach might make, especially one that fails. What definitely happens is that when people do a quote-unquote aggressive move, it’s often attributed as an analysis game, even though the numbers don’t say so, Seth Walder, an ESPN analysis writer, told me. (Ironically, projection models shrugged at the Ravens’ oft-derided two-point effort, viewing it as a toss.)

Many coaches also recoil at the way analytics have tarnished football. Consider the two most accomplished coaches of this generation: Nick Saban of the University of Alabama and Bill Belichick, the New England Patriots, who have seven national titles and six Super Bowl victories, respectively. Saban said he is not an analyst and described the work of a quantitative analyst as someone who has never played football and who sits in front of a computer and puts a lot of things into a computer. Belichick, meanwhile, once said about analytics, I don’t care what they say. However, both coaches employ analytical staffers. Saban is known for employing a small army of coaches whose job title is literally analyst. So, what gives?

Maybe this is all simple. Becoming an elite athlete, or coach of elite athletes, requires a lifetime of work that goes far beyond figuring out the most sensible analysis of data. The NFL’s accurate player movement tracking, often illustrated in moving dots, doesn’t know the play-call or a million other subtleties, and in turn doesn’t know all the data derived from it. If I want to know how to cook a boeuf bourguignon, I’m not going to ask Einstein, Hugo Mercier, a cognitive scientist at the Institut Jean Nicod in Paris, told me. People have their field. And even if people could tell you that the MIT crowd is generally smarter than [an MLB] scout, they would still think the scout knows more about baseball.

For us fans, perhaps the whole contradiction boils down to the idea that numbers could be what Mercier calls a black box. Consider a computer spewing out the difference in pre-game winning odds if a coach decides to kick a field goal instead of going for it on fourth. Humans are wired to trust sources of information that we can argue with, Mercier told me. There’s no arguing, not really, with a fourth-down model.

I’m a fan of the Pittsburgh Steelers, a usually solid team that currently has one of the worst records in the league. Before the season started, my more optimistic brethren had a hard time accepting that bad times were coming, even though several statistical analyzes suggested an imminent crash. If you see someone on TV and they talk at length about how the Steelers aren’t doing great this year, and it goes wrong for this and this reason, they might be able to convince you, Mercier told me. But if you just see a statistical analysis that doesn’t explain the reasons, I don’t think it will convince many people.

In a sense, sports analysis is stuck on a hamster wheel. Many who have played and coached the game harbor a natural skepticism about them, which shows up when asked questions about analytics or when talking about stats in their post-career media roles. Then the resistance seeps into the public discourse and becomes stronger and stronger for a regular audience of millions. We value what athletes and coaches say about sports, just as we trust what doctors say about medicine or chefs say about cooking.

But perhaps the simplest reason for all this resistance to analysis, in locker rooms and TV studios and everywhere football is played and watched, is simply that America is analytically tired. Escaping the algorithmic world that overwhelms us with an endless stream of information is impossible. I rely on a fitness watch to tell me exactly how long I’ve been sleeping and how hard my heart has been pumping every minute of the day, then give me advice on how hard I should exercise the next day. TikTok users can’t escape an opaque algorithm that queues up an endless stream of videos. Political observers everywhere rely on a computer model that simulates an election and lets them track opportunities through a moving needle for months or a few frantic hours. Numbers are both the background noise of our daily lives and the battlefield for so many of our societal battles.

But after all, sport should be a form of escapism to get us out of these troubles. What we really want to some extent is to argue. In that sense, analytics should be a godsend. They are an extra weapon in any fans’ crusade to talk about their own teams or their rivals. But the cardinal sin that sports analytics commit against our brains is making arguments that are hard to refute on their face. I could tell my friend that their team’s quarterback has an imprecise arm, and they could reply that the QB’s aim is basically mimicking a precision missile. But then if I contradict that the QBs motion camera generated completion rate above expectations is well below the NFL average, what’s left for my sparring partner to say, other than the stat itself being crap? Where’s the fun in that?

There is, of course, a way for an advanced statistic to gain approval from someone who thinks they are skeptical about such things: it supports your argument. The backlash in the analysis is about the same every year, but at least the teams change, Connelly said. The fanbases are changing that are screaming at me because it really just comes down to this: if the numbers say what I want them to say, they’re good. And if they don’t, they’re ridiculous.




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