‘Net Gains’ and the Slow-Motion Analytics Revolution in Soccer and Higher Ed
Net Gains: Inside the Beautiful Game’s Analytics Revolution by Ryan O’Hanlon
Published in October 2022
Net Gains has persuaded me of two things. The first is that soccer lags significantly behind other major sports in incorporating data into the game. The second is that from an analytics point of view, soccer is miles ahead of higher education.
For many of us in both education and sports, the dream of making decisions based on data rather than intuition was born in 2003 after reading the Michael Lewis book Moneyball. What academic would not want to have themselves portrayed by an older and wiser Brad Pitt in a future university-centered Aaron Sorkin–written film?
In Net Gains, ESPN writer and former Holy Cross soccer player Ryan O’Hanlon tries to explain why soccer has lagged behind baseball, basketball and football in reliance on analytics for coaching and player decisions.
I’m a huge soccer fan, but my favorite team is the U.S. Women’s National Team (USWNT), and my favorite league is the NWSL. Ask me about the best players in the world, and I’ll talk about Sophia Smith, Mallory Pugh, Rose Lavelle, Trinity Rodman, Alex Morgan and, of course, Megan Rapinoe.
To my chagrin, the world of women’s professional soccer is entirely absent from O’Hanlon’s account. Reading Net Gains convinced me that I should be watching the Premier League more, and I have been wondering how to take a mini-sabbatical for the upcoming FIFA World Cup (men’s).
But really, women’s soccer is just better. Women almost never take dives. I’m not sure if professional women’s soccer players are tougher than their male counterparts (I don’t doubt it), but acting to win a free kick is just not part of the women’s game the way it is for the men. If O’Hanlon had written a book about soccer analytics that included women equally to men, Net Gains would have been a much better book.
Leaving behind the blind spot of not including women (which is challenging for me), what does Net Gains have to say about the slow analytics revolution in soccer? And what does the slow pace of data-driven decision-making in soccer say about higher ed?
Soccer, it turns out, is exceedingly difficult to quantify. Unlike basketball, where data (and Steph Curry) have changed every team into three-point-shooting machines, soccer is wildly complex. Many in the soccer world think the game is too fluid, dynamic and random to model.
The most reliable predictor of the success of any given team is not field tactics, player speed or passing percentages—but instead the total amount that a team spends on its players. Spend more money, win more games.
The fact that soccer does not easily yield to statistical decision-making does not mean that analytics are absent from the sport. O’Hanlon reports that the best teams in the wealthy European leagues are investing ever more dollars in analytics to guide decisions on player acquisitions, sales (transfers) and on-pitch tactics.
If Bayern Munich, Man City, Barcelona and Liverpool (and I suspect the USWNT coach Vlatko Andonovski) can all become soccer analytics enthusiasts, can’t colleges and universities follow a similar trajectory?
Data-driven decision-making in higher ed might be even more of a challenge than data-informed soccer management, but the payoffs for both would be enormous.
It is often said that higher education lives in a data-free zone. I disagree. Learning analytics are starting to appear in more places.
I don’t know of any at-scale online program that does not have data at the heart of its operations. The challenge for higher education will be to take what we know about learning analytics from the design and operation of online courses and apply that knowledge to residential/blended instruction.
One key takeaway from Net Gains is that soccer analytics is being pushed forward less from work inside clubs but rather from outside-soccer data obsessives blogging about the game.
In higher education, perhaps we need more data-obsessed postsecondary analysts (like my friend Phil Hill) to catalyze a learning analytics revolution.
What are you reading?