LaLiga transforms fan experience with AI

The AI advantage
LaLiga Tech is leaning on AI and ML for a number of initiatives. For example, LaLiga uses AI to engage and retain fans, by recommending content and providing additional insight into the fan experience via sentiment analysis.

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LaLiga transforms fan experience with AI

The AI advantage

LaLiga Tech is leaning on AI and ML for a number of initiatives. For example, LaLiga uses AI to engage and retain fans, by recommending content and providing additional insight into the fan experience via sentiment analysis. LaLiga has also created an ML solution called Calendar Selector to maximize TV audiences and stadium attendance when scheduling matches. It has also developed predictive models to detect trends, make predictions, and simulate results. These fan engagement, competition management, and advanced performance analytics capabilities are part of LaLiga Tech’s offerings.

During matches, 16 optical tracking cameras installed in each of the league’s stadiums capture real-time data on player positioning, referee positioning, and the ball’s movement to capture 3.5 million data points per match.

“With this huge amount of data per month, we are able to offer stats and reports,” Bruno says. “With 112,000 reports in the system and 8 million bits of information, it’s a huge amount of information for 42 clubs.”

AI takes that data and combines it with historical tracking data from about 2,000 matches to create new insights, such as the Goal Probability model, one of 21 new stats it debuted in 2022.

Created by a multidisciplinary team of football analysts, business intelligence analysts, and the analytics team, the advanced Goal Probability model leverages a range of variables, including the player’s line of sight (taking into account the positions of opposing players), the distance between the ball and the goalkeeper, the distance between the ball and the goal, and the distance and angle to the nearest defender, to measure the probability of finishing a given scoring chance. The calculation also takes into account a player’s efficiency indicator based on variables such as the player’s ratio of goals per match and per shot.

“One of the challenges is, in order to turn this raw data into knowledge, we need not just data scientists, but also football analysts, UX experts, and coaches,” Bruno says.

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