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A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine

Adam L Kelly1, Craig A Williams2, Rob Cook1

  • 1Research Centre for Life and Sport Sciences (CLaSS), Faculty of Health, Education and Life Sciences, Birmingham City University, Birmingham B15 3TN, West Midlands, UK.

Sports (Basel, Switzerland)
|October 26, 2022
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Summary
This summary is machine-generated.

Youth football development involves more than just technical skills. Psychological attributes and practice quality significantly impact player ratings and professional contract success, highlighting a multidimensional approach to talent identification.

Keywords:
elite youth soccerexpertisephysical characteristicspsychological characteristicstalent identificationtechnical and tactical

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Area of Science:

  • Sports Science
  • Talent Development
  • Machine Learning in Sports

Background:

  • Youth football talent development is complex and multidimensional.
  • Traditional methods may overlook crucial developmental factors.
  • A need exists for advanced analytical approaches to understand player progression.

Purpose of the Study:

  • To apply a multidisciplinary, machine learning approach to analyze talent development in youth football.
  • To identify key factors influencing player review ratings in academy players (U9-U16).
  • To determine characteristics associated with achieving a professional contract in academy players (U18).

Main Methods:

  • Utilized a machine learning approach with cross-validated Lasso regression.
  • Analyzed 53 factors from eight data collection methods across two seasons.
  • Examined developmental characteristics of 98 U9-U16 players and 18 U18 players.

Main Results:

  • Advanced predicted adult height, lob pass success, dribble completion, match-play hours, and relative age significantly influenced player ratings.
  • Psychological attributes, specifically PCDEQ Factor 3 and quality practice engagement (PCDEQ Factor 4), were crucial for securing professional contracts.
  • 15 out of 53 analyzed features showed non-zero coefficients for improved subjective performance.

Conclusions:

  • Talent development in youth football is influenced by factors beyond technical and tactical skills.
  • Psychological attributes and practice quality are key determinants for reaching potential and securing professional contracts.
  • Machine learning offers a robust methodology for analyzing complex, multidimensional sports science datasets.