Sport analytics leverage AI and ML to improve the game
“With this huge amount of data per month, we’re able to offer stats and reports,” says Ana Rosa Victoria Bruno, innovation manager at LaLiga. “With 112,000 reports in the system and 8 million bits of information, it’s a huge amount of information for 42 clubs.”
One of the tools that’s also provided to broadcasters for fan engagement is a Goal Probability model, which leverages a range of variables, including the player’s line of sight (taking into account the positions of opposing players); distances between the ball and the goalkeeper, and 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.
Bruno’s advice: Create a multidisciplinary team.
Bruno says it required a multidisciplinary team of football analysts, business intelligence analysts, and the LaLiga analytics team to find success. “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,” she says.
