Study casts doubt on claim that soccer players have superior brains
A new analysis of widely-cited research reveals flawed methodology that overstates cognitive differences between professional and amateur soccer players. The finding matters because it highlights how machine learning models can appear to detect meaningful patterns in data when none actually exist—a cautionary tale for companies using AI to screen job candidates or talent.
Originaltitel: Revisiting the cognitive advantages of professional soccer players
Bonetti et al. (2025, Proceedings of the National Academy of Sciences) find that professional soccer players in Brazil and Sweden exhibit detectable cognitive advantages compared to a sample of Brazilian control participants, and that machine learning models trained on cognitive and personality characteristics can distinguish the professional players from the control participants with 97% accuracy. Analyzing the study's replication data, we find that some of the study's statistical analyses are mischaracterized, and we document potential issues in the sampling of control participants. In light of the latter, we focus on quality differences between professional Swedish players previously analyzed by some of the study's authors. We find that the paper's machine learning models can only distinguish high-quality Swedish professional players from lower-quality players with just 53% average accuracy, near the no-information rate.