You know the part in The Big Short where Michael Burry (Christian Bale) is sitting there at his desk, explaining to an irate investor that the housing market is guaranteed to crash, it’s just that no one knows it yet? And the fact that it has never crashed in modern times has nothing to do with his certainty that this future crash is now inevitable?

I feel that way about stats and data in football.

I believe with utter certainty that stats and data will play a huge role in the future of the sport. This is despite knowing that my certitude makes me sound like a bit of a loon to parties that approach this subject with some skepticism.

Today I’m going to explain why I carry this certainty about the future, but it requires the audience to shed one big misconception most people seem to carry about the game.

Football is not unique and unrelated to other sports.

Football actually bears reasonable similarities to basketball and both forms of hockey, such that certain ways of analyzing those sports are easily adaptable to football. And this goes beyond stats – German coaches have long consulted with elite field hockey coaches about defensive tactics, and Pep Guardiola includes legendary water polo player Manuel Estiarte in his coaching staff. Even now, researchers like Luke Bornn are taking what they learned applying spatial statistics to SportVU data in the NBA and seeing what they can learn from football’s tracking data.

Yes, football has its own idiosyncrasies and you need to understand the game at a high level to get the most out of your analysis. No one intelligent disputes that. But the fact that we’ve seen massive revolutions in how other sports are analysed that have lead to changes in how the sports are also played means we should expect a revolution to hit football in the future as well.

Chasing Perfection
I have been reading Andy Glockner’s book recently, which catalogs and explains the NBA’s analytics evolution, and it continually amazes me how much it parallels a movement still in its infancy in football.

There is way more money involved in [the league] today than even ten years ago, and teams have to work harder and harder to find and maintain competitive edges. How they are doing so varies wildly from team to team, and heavily involves state-of-the-art technology to try to move ever closer to solving an impossibly complex and nuanced sport.

Is that quote about basketball or football? The NBA or the English Premier League? It could be either, right? Except in the Premier League there are bushels full of competitive edges sitting in easy reach of anyone who knows where to look.

Another thing I believe for certain is they won’t stay that way for long. Spending money now to obtain the low hanging fruit and discover new ones also gives a team a head start in what will assuredly be a brain race at some point down the road, and more importantly, will likely yield huge dividends in terms of points, money, and potential titles now.

That’s the thing that I think the analytics movement in football may have gotten wrong through absolutely no fault of anyone involved. The stats guys developing new ideas and doing the work often think of it as, “how do we apply stats to football to learn new things?”

That’s technically correct. However, it misses the major point.

The real goal for the analytics movement in any sport should be: how do we discover and deliver new competitive edges?

Stats and data are a very useful tool in doing that, but it’s a big tool box.

Another thing Glockner highlights in an early chapter is this piece by Chris Ballard discussing the new stats movement in the NBA, circa 2005. What amazes me is not that most of the names mentioned are still quite prominent in NBA front offices, but instead how bloody young they are.

Sam Presti is 29. Sam Hinkie is 27. Celtics “Senior Vice President for Operations” Daryl Morey is… 31.

Anyway, Glockner’s book is excellent, especially if you read it with an eye that it may be foretelling the future of a football analytics movement that has yet to start across most of Europe.

Statheads Are The Best Free Agent Bargain in Baseball
FiveThirtyEight is a mixed bag, but their sports stuff is still generally pretty good. This piece, which examines the expansion of “numbers-savvy front-office staffers over time” is excellent.

“Although the analytical gold rush began before the period we examined, hiring has accelerated at an almost exponential rate over the last few years.”

One of the main takeaways from the article is that baseball teams are spending more and more on stats dorks because they provide a dramatically bigger boost to win totals on a per dollar basis than many free agent signings. Part of that is because baseball’s player market has become more efficient over the years thanks to improved use of stats, but a bigger part comes down to basic economics.

They estimate a five-man analytics team costs about $350,000 per year, which still lags behind the minimum salary for a single player.

The takeaway: It paid to invest in analytics early. Teams with at least one analyst in 2009 outperformed their expected winning percentage by 44 percentage points over the 2012-14 period, relative to teams who didn’t — an enormous effect, equivalent to more than seven extra wins per season. 

Even the minimum estimate of two extra wins per year would represent a return roughly 30 times as efficient as spending the same amount on the free-agent market.

One more thing that really struck me out of that piece and that I feel is hugely applicable to football.

Although the big-budget Boston Red Sox were also one of the first teams to demonstrate that an analytics department could help win a World Series, a number of low-payroll, small-market teams — including not only the Moneyball A’s, but also the Rays, Indians, Padres and Pirates — were among the first to form quantitative departments and develop systems to house and display statistical data. It made sense: The more pressing a team’s financial imperative to stretch every dollar and wring out every win, the more likely it was to try a new approach.

How can teams compete with the traditional giants beyond just spending more money?

  • Apply the marginal gains.
  • Make consistently better decisions than other teams.
  • Play more efficient football.
  • Recruit better coaches.
  • Recruit better players.
  • Make fewer mistakes in the transfer market.

Find. The. Edges!

Baseball and Basketball are hugely different sports. In fact, they are more different from each other than basketball is from football. And yet in both of these areas we have seen teams dramatically ramp up spending to get smarter faster than the competition.

Why?

Because it helps them win more.

This WILL happen in football.

The only questions are how long it takes before it happens in scale across not just England, but European football as a whole, and which teams will lead the charge and reap the rewards as early adopters.

–TK

mixedknuts@gmail.com

  • Abel

    Yeah Morey started out w the Cs and Hinkie getting fired was an amalgamation of ownership getting fed up with losing, them whiffing on some key picks (injuries) and the league stepping in and basically almost imploring them to hire the Colangelo clan.

    • Ron IsNotMyRealName

      Hinkie got fired because he was a complete disaster. He drafted not one but two injury-prone big men with high draft picks. It was complete madness and the Browns seem to have repeated it today.

  • Ron IsNotMyRealName

    The problem you have with this is that pretty much all the people you have mentioned have been mediocre at best.

    Hinkie was fired after failing spectacularly by taking ridiculous risks. Presti has been kept afloat by 2 players after stupidly practically giving away James Harden to…Morey, who in turn has failed to get his club beyond one-man band status. The Cleveland Browns’ Harvard analytics team basically just redefined completely puzzling and borderline hilarious drafting having drafted 5 wide receivers, which is something like signing 6 fullbacks in futbol — WRs are the last piece of the puzzle, not the first, and the Browns are terrible at basically everything, yet used something like 35% of their draft (in which they several times traded to acquire MORE picks at a lower position in the draft) on wide receivers.

    So far the poster boy “analytics guys” have basically been pointyheads educated at schools of prestige desperate to prove they’re the smartest people in the room, and they have all failed to one extent or another. Meanwhile, Dayton Moore built a champion in Kansas City of all places (not dissimilar to Leicester winning the PL) the old-fashioned way — by putting the ball in play, running the bases well, and playing good defense; all qualities the Beane-counters discarded.

    In short, the analytics revolution is not off to a great start, not because analytics are bad, but because the people have been too cocky about them and discarded conventional wisdom — some of which may be conventional for a reason.

    I strongly suspect that like in baseball, some people trying to reinvent the wheel in other sports are trying to do so without first ensuring that they’re not throwing out the baby with the bath water, leading to a lack of credibility and confidence among industry veterans in analytics.