The sabermetrics revolution in baseball has been around long enough to warrant a bestselling book and a Hollywood film with several Oscar nominations. Advanced statistics in football, however, haven’t even come close to a Moneyball moment. They haven’t overturned the conventional wisdom or precipitated a titanic struggle with management over how to evaluate players. It’s possible they never will. But football statistics might still be nearing a tipping point, and they’d have very different consequences than sabermetrics. They may not prove worthy of a movement, but they could fundamentally alter how football is played.
The comparisons between advanced football and baseball statistics aren’t quite fair. Nearly every event in baseball can be categorized and quantified: walk or strikeout, homerun or flyout, ball or strike. Football lacks baseball’s clarity: Did the offensive lineman execute a good block, or not? Even if it was possible to judge a block with objectivity and clarity, it doesn’t appear in the box score. The success of individual football players is also far more dependent on their teammates than in baseball, where a strikeout or a homerun is the result of an interaction between just two players. How do you assign credit for a 10-yard running play—was it a great block, or a great run? And with only 16 games a year, there’s far more uncertainty in football statistics than in baseball’s 162 game season.
Despite the challenges, new and advanced football statistics are getting more attention. Brian Burke of Advanced Football Statistics built WPA, which attempts to measure how much each play increased or decreased a team’s chance of winning based on the score, down, and time left in the game. The same concept is applied by ESPN’s Total Quarterback Rating to assess the performance of quarterbacks, albeit with an added splash of subjectivity (such as judgment calls about whether passes were dropped, overthrown, underthrown, or defended well). Football Outsiders offers DVOA, a statistic that judges whether a team did better or worse than an average team under similar circumstances, play by play.
Such statistics are valuable, but they’re not isolating a single player’s value from the rest of his team like baseball’s VORP or WAR (which then come in handy when it’s time to debate, say, the season’s MVP). And although advanced football statistics contest the assumption that all yards are created equal—gaining eight yards on 3rd and 10 doesn’t advance a team’s chances of victory—the new football statistics don’t often contradict the traditional ones. Indeed, Aaron Schatz of Football Outsiders thinks that the conventional wisdom about the Super Bowl--that the San Francisco 49ers are one of the top teams and the Baltimore Ravens have gotten hot at the right time--“pretty much jives with reality.” In contrast, baseball’s award season is now dominated by debates between adherents of sabermetrics and the sports media’s old guard: whether the Cy Young should go to the pitcher with the lowest FIP or the most wins, or if a young centerfielder’s speed, defense, and positional value can trump a Triple Crown winning third baseman.
That doesn’t mean advanced statistics couldn’t answer the types of questions addressed by baseball’s number crunchers. But if similar gains can or are being made in football, it’s likelier to happen out of the public eye. Unlike Bill James’ outsider-led movement in baseball, Schatz notes that much of the statistical work in football started on the inside. Front offices that have been employing data-driven techniques for longer than most baseball teams, since the salary cap demanded added attention to efficiency in the mid-90s. Even the statistics developed outside of the league tend to be proprietary, since “the play by play logs aren’t as easy to read as in baseball” and the new football stats require “creating measurements where there were none before.”
“Most of the analytical brain power in football is on the financial side,” according to Burke, but teams are “waking up” to the on-field possibilities. The San Francisco 49ers decision to replace Quarterback Alex Smith with the less-experienced Colin Kaepernick, for instance, might have been aided by advanced metrics. At the very least, it was highly consistent with the numbers. While the conventional wisdom holds that Kaepernick’s running ability was his decisive advantage, Burke says that Kaepernick’s real value is that he’s a “much bigger deep threat than Alex Smith.” That fact could have been inferred intuitively from watching game tape, but advanced metrics would allow the 49ers to “very accurately and very meaningfully account” for Kaepernick’s big play edge. Of course, Harbaugh might have made the stathead’s choice for the wrong reason. It’s impossible to know how teams are employing advanced stats behind closed doors, and even a well-trained eye would struggle to identify whether and how football teams are using advanced metrics to shape roster decisions and salaries. Schatz didn’t think that any of Baltimore or San Francisco’s personnel decisions “stood out” as the result of less traditional, more quantitative approach, even though both Super Bowl competitors are near the cutting edge in analytics.
The one place where fans could see analytics at work is in play calling, which also happens to be the place where analytics could impact the average fan’s experience of the game. The numbers suggest, for instance, that teams should be aggressive on fourth down, and that it’s better to go for first down with a lead in a game’s final minutes than to run the ball on third down to run out the clock. Yet even the teams with well-regarded analytics departments, including San Francisco and Baltimore, largely adhere to a conservative and traditional play calling approach: The coaches “just aren’t listening to them yet,” Burke says. And the few coaches with a reputation for following the statistics, like New England Patriots coach Bill Belichick, aren’t even close to as aggressive as the numbers would advise.
If coaches begin to adopt the lessons of advanced football statistics, the changes would be noticeable to even a casual fan: Teams would go for it on fourth down, stop running so much on first down, go for the jugular with a late lead, and take big risks as an underdog in the first quarter. In that sense, statistics might promise more fundamental changes to football than baseball. Fans watching a data-driven baseball manager might not notice any big changes at all, unless they were fans of bunting.
Schatz “didn’t get into football statistics to revolutionize how teams are run,” just how football is covered. But once the first coach succeeds while embracing a data-driven approach to play calling, football statisticians might claim credit for producing striking changes in gameplay that neither Schatz nor baseball statisticians ever sought. Burke believes that there will eventually be such a “tipping point,” when the first successful adoption of advanced stats is quickly followed by universal adoption. If the tipping point comes as he hopes, football might get its Moneyball moment.