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Q & A with Net Gains author Ryan O’Hanlon

Tactics Talk Photo by J. A. Hampton/Topical Press Agency/Getty Images

We previously reviewed Ryan O’Hanlon’s book Net Gains on the site here. Today, we share a Q & A session he was kind enough to do with us. Thank you, Ryan, for writing a great book and for taking the time out to answer our questions!

Hey Ryan, thank you so much for answering our questions. First off, what made you decided to write an entire book on the history of soccer analytics?

-I don’t really look at it as a history book. At least, that’s not what I set out to write. The main reason I wanted to write the book — and why I was able to write the book — is that there’s this fascinating tension tugging at the heart of the world’s most popular sport: we still really don’t don’t know that much about how it works. I just wanted to write about that idea and some of the people who are trying to create tiny bits of new knowledge.

Your book deals a lot between a sort of old school eye test vs new school analytics approach to viewing the sport, and even includes a couple stories of that gap being bridged. These same debates take place among fans, what do you think is the best way to explain to lifelong soccer fans how these modern forms of analysis can help their club?

-Everyone still underestimates just how random this sport is. The average basketball game features about 100 shots per team. The average soccer game has about 12. Add in the fact that the conversion rate in a basketball game is somewhere around 50 percent and about 12 percent for a soccer game, and, well, you can start to understand how the result of a single game, let alone 10 or 38, doesn’t really tell us all that much about how good a given team is. I’m not sure the average fan cares about this — or wants to care about it. But soccer clubs are competing for wins and points, so every little way you can find to filter out what’s random and what’s real gives you a tiny leg up on everyone else.

One of the things you make clear in your book is that trying to statistically unlock the secrets of this sport has been something that dates back a long way into soccer’s history, sometimes changing styles of play based on the wrong conclusions from the data. You describe very well in the book what makes soccer so hard to get at with analysis but could you summarize that idea briefly for our readers?

-Baseball has the at-bat: pitcher versus batter, over and over and over again. The NBA has the 24-second-shot-clock possession. And the NFL has the set of downs and the drive. Soccer has ... none of that. If two teams wanted to, they could both just stare at the ball as it sits at the center circle for 45 minutes and no one would be able to do anything about it. Since the game isn’t broken down into these tidy components, it’s so much harder to measure. Well, maybe not harder to measure, but it’s so much harder to confidently ascribe any value to anything that happens on the field.

We see clubs now hiring coaches specifically for throw ins and set pieces, largely in reaction to data on these topics. Do you think this is a short term fad or perhaps is analytics greatest area best use in the sport these more static situations?

-It’s not a fad, at all. If anything, I think you’ll probably see more teams hire specialist coaches. One of the great breakthroughs in recent baseball history is this fine-grained, data-informed coaching method that has made veteran pitchers and batters more efficient. For example, both Liverpool and Manchester City’s recent success was heavily built on, for lack of a better phrase, the ability of Trent Alexander-Arnold and Kevin De Bruyne to simply kick a soccer ball better than anyone else. FC Midtjylland in Denmark has hired a “kicking coach”, and if you can find someone who, say, makes all of your shots one percent more likely to turn into goals, then that’s worth around 5 extra goals a season. Each individual goal in a season is worth millions of dollars, and this hypothetical “kicking coach” would not be on a million-dollar-a-year salary — although maybe he should be!

Most of the readers of our site are fans of Villarreal, a team that constantly has to make the most of its budget to try to compete with the biggest clubs in our league and in Europe. From that perspective, it is very disconcerting to read that one of the biggest correlations to final table success is the amount of money spent on player salaries. How could a smaller budget club like Villarreal use data to get out of that rut and punch above our weight in the league table like we do in Europe?

While wages and points have a very tight correlation, transfer-market spend barely has any relationship with on-field success. Club-record transfers, on average, only play half of the available minutes for their new teams, which tells you that the transfer-market is wildly inefficient. Given that fact, there’s still plenty of opportunity for smaller teams to exploit the irrationality we see every summer. You could just totally opt out of it, which seems to be working pretty well for Athletic Club, or you could try to find undervalued players and move on from overvalued players. I don’t think it was particularly analytics-informed strategy, but the club has done a fantastic job in doing this recently. Last year’s Champions League run was powered by a bunch of so-called “post-hype” players, who moved to big clubs, didn’t work out for whatever reason, and then became available for way less than their true talent levels typically go for in the market.

It’s probably of little comfort to you and your readers now, but this team was quite good in the league last season. Villarreal had the fourth-best expected-goal differential in La Liga, but they finished seventh because there’s a ton of randomness inherent to trying to kick a round ball with a misshapen foot past the only player on the field who’s allowed to use his hands.

What else would you like our readers to know about your book, and do you have any other book ideas in the works?

There are only, like, four charts! This is a book about people and ideas first, and then how data informs both of those things second. More than “teaching people about soccer” or whatever, I wanted to write a book that was just fun to read. Given your interest in talking to me, I’m hopeful that I did!

I certainly did find it interesting to read. Thanks again, Ryan, for taking the time to talk with us and to all our VUSA readers, go get yourself a copy of Net Gains here.