UC Davis Students Explore Soccer Analytics and the Growing Role of Statistics in Sports
The release of the film Moneyball, based on the Michael Lewis book of the same name, cemented into the public consciousness the value of data analytics in sports. These days, you’d be hard-pressed to find a professional baseball club without a data analytics team. The same goes for basketball, hockey and soccer.
UC Davis statistician Alexander Aue, a lifelong soccer fan and recently elected fellow of the American Association for the Advancement of Science, saw the growing usage of statistics in sports as an opportunity to bring the real world into the classroom. His First-Year Seminar class Soccer Analytics is a crash course in how the sport harnesses the power of statistics and data analytics, from finding value in players to predicting goals and passing networks.
“From an analytics point of view, soccer is more complex than baseball,” said Aue, a professor in the Department of Statistics at the College of Letters and Science at UC Davis. “In baseball, you have the opportunity to focus on the interactions between a pitcher and a hitter. In soccer, you have to follow 22 people at the same time, and so the uncertainty is much bigger.”
“You basically want to hedge against that uncertainty,” he added.
Using soccer analytics to make tactical decisions
In a data-laden sport like soccer, the paradigm of statistical analysis has shifted. No longer are more datapoints needed, according to Aue. There’s a fount of that. Instead, what’s needed are strategies to find the signal in the noise.
“The signal is hard to decipher because there is too much noise,” Aue said. “There’s a lack of signal and that changes the way you approach it.”
For statistical data science students Aneiss Abdellaoui and Mustafa Ali, enrolling in Aue’s Soccer Analytics class was an opportunity to view a game they love from a different perspective. Both are longtime players of the game, Abdellaoui currently coaches at Corte Madera in Marin and Ali plays defender for FC Davis, a semi-professional team.
(Photo Courtesy of Aneiss Abdellaoui)
“There’s only so much you can get tactic-wise from playing on the field,” Ali said. “It’s interesting to see how you can use statistics to improve your game or give you insights to help you on the field.”
"It provides a complete view of the field, allowing for a clearer visualization and a deeper understanding of the game," Abdellaoui added.
Aue based the course material on the book Soccer Analytics: An Introduction Using R, which shows how the programming language can be harnessed to predict match outcomes, rank teams, construct passing networks and assess match play, among other strategies.
“Statistics can offer a number of things,” Aue said. “Where it’s often applied is in terms of figuring out how a new player will fit into a current team, who to sign and for what price.”
“On the other hand,” he added, “you have the tactical decisions where you have to set up your team against an opponent to maximize your strengths and exploit their weaknesses. That’s a statistical task again.”
Since the class is a seminar, it's open to all students regardless of their statistics and data analytics knowledge.
Aue said the class is perfect for someone who “likes sports and has an intuitive understanding of quantitative reasoning.”
Drawing expertise from the big leagues
In a straight-from-the-field example, Aue brought in Luke Bornn, a former Harvard University professor who also served as vice president of strategy and analytics for the Sacramento Kings and head of analytics for AS Roma, to guest lecture. Bornn is currently co-owner of Toulouse FC.
(Photo Courtesy of Mustafa Ali)
Bornn gave the students real-world examples of how he harnessed data analytics to sign key players for his employers. One such player was Mohamed Salah, a decorated Egyptian soccer star who currently plays for Liverpool FC in the Premier League.
While working for AS Roma, Bornn identified Salah as a standout player.
“He didn’t want to give us the exact details, but he and his team were able to identify that he had key characteristics and the potential to be big,” Ali said. “Now, Salah is a multi-year winner of the Premier League Player of the Season award and his value has increased 10-fold.”
"It really shows that statistics is valuable in assessing players and their potential,” he added.
Still a game of chance
For the latter part of the course, the students developed an independent soccer analytics project. Abdellaoui and Ali teamed up to develop a statistical model to identify potential winners of the Ballon d’Or, a prestigious player award organized by the magazine France Football.
Abdellaoui and Ali trained their machine learning model on on-field performance statistics of past award winners. They identified goals and assists as the strongest predictors of winning, with assists having the highest correlation of success, according to their report, which was compiled in May 2024.
“From 2010–2024, nearly every winner had ≥25 goals and ≥10 assists, a benchmark that narrowed the 2024/25 field to five serious contenders,” they wrote. “According to our model, the 2024/25 Ballon d’Or should go to either Raphina or Mohamed Salah, emerging as the top predicted winners from the dataset.”
The winner of this year’s Ballon d’Or won’t be announced until Sept. 22, 2025.
Despite all statistics has done and can do for sports, much is still left to chance. There’s the human factor. A player could have an off day, maybe not getting enough sleep or feeling sick and performing sluggishly. That’s something statistics can’t account for.
“You certainly have other fundamentals,” Aue said. “You have to be fit, you have to be technically strong, your mind has to be in the right place. The psychology of the players is also equally important.”
“A lot of the outcome of a game depends on chance,” he added. “But you can minimize aspects of that randomness by setting up a tactic. You can’t guarantee a win, but you can maximize your chances.”
Learn more about the course Soccer Analytics on the First-Year Seminars page.
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