Classifying Soccer Players Based on Physical Capacities and Match-Specific Running Performance Using Machine Learning

Sprint and endurance capacities seem to be mutually exclusive or at least at odds with each other. However, this relationship has not been investigated in soccer, which appeals to both well-developed sprint and endurance capacities. This study explores the potential of machine learning to identify soccer players based on their unique combinations of sprint and endurance capacities and sprint and endurance match-specific running performance. In this context, the relationships between sprint and endurance capacities and between physical capacities and match-specific running performance are examined in detail. Match-specific running data were collected from 31 young elite male soccer players over two consecutive seasons. Additionally, these participants underwent exercise testing, consisting of a 20-meter sprint test and an incremental treadmill test to measure maximal oxygen uptake (V̇O_2max). Subgroups were identified using <i>k</i>-means clustering and subgroup discovery, based on players' sprint and endurance capacities, sprint and endurance match-specific running performance, and playing position. Three distinct subgroups were identified using machine learning: players with high sprint capacity and sprinted meters (<i>n</i> = 4), players with high endurance capacity and meters ran at moderate and high intensities (<i>n</i> = 6), and players without high physical capacities or matching match-specific running performance (<i>n</i> = 14). Across all players, there was no significant relationship between 20-meter sprint speed and normalized V̇O_2max (<i>R</i> ² = 0.085, <i>P</i> = 0.17), although 20-meter sprint speed was positively related to average match sprint distance (<i>R</i> ² = 0.168, <i>P</i> = 0.03) and normalized V̇O_2max to average match distance at moderate and high intensities (<i>R</i> ² = 0.151, <i>P</i> = 0.04). In young elite soccer players, sprint and endurance capacities show positive, moderate, relationships with corresponding match-specific running performance, but those capacities do not appear to be mutually exclusive or opposing. Clustering allows for identification of players who may benefit from alternative strategic roles during matches, are at risk of overuse, or could benefit from individualized training. This method can assist coaches in designing tailored training programs and optimizing overall match strategy.