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

Michel de Haan1, Stephan van der Zwaard1,2, Jurrit Sanders3

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Summary
This summary is machine-generated.

Soccer players

Keywords:
ClusteringMASMSSV̇O2maxfootballsprint speed

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Area of Science:

  • Sports Science
  • Exercise Physiology
  • Biomechanical Analysis

Background:

  • Sprint and endurance capacities are often considered mutually exclusive in athletes.
  • This relationship remains under-investigated in soccer, a sport demanding both attributes.
  • Understanding these capacities is crucial for optimizing player performance and training.

Purpose of the Study:

  • To explore machine learning's potential in identifying soccer players based on sprint and endurance profiles.
  • To examine the relationships between physical capacities and match-specific running performance in elite young soccer players.
  • To determine if sprint and endurance capacities are opposing or complementary in this population.

Main Methods:

  • Collected match-specific running data from 31 elite young male soccer players over two seasons.
  • Conducted 20-meter sprint tests and incremental treadmill tests for maximal oxygen uptake (V̇O2max) assessment.
  • Utilized k-means clustering and subgroup discovery to identify distinct player profiles based on physical capacities and performance metrics.

Main Results:

  • Machine learning identified three distinct subgroups: high sprint capacity (n=4), high endurance capacity (n=6), and neither (n=14).
  • No significant relationship was found between 20-meter sprint speed and normalized V̇O2max (R² = 0.085, P = 0.17).
  • Sprint speed positively correlated with average match sprint distance (R² = 0.168, P = 0.03), and V̇O2max with moderate/high intensity match distance (R² = 0.151, P = 0.04).

Conclusions:

  • Sprint and endurance capacities show moderate, positive relationships with corresponding match-specific performance in young elite soccer players.
  • These capacities do not appear to be mutually exclusive, suggesting they can coexist and be complementary.
  • Machine learning-driven player profiling can inform individualized training, strategic role assignment, and injury risk management.