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Efficient talent identification in women’s football: A ranking-based approach for goal scoring analysis

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  • Songyi Song
  • Hee-Su Kim

Abstract

Individual goal-scoring analysis in women’s football faces severe class imbalance and limited scouting resources, where classification metrics alone do not capture operational efficiency. We analyzed 2,535 non-goalkeeper player-match observations from the 2023 FIFA Women’s World Cup (736 unique players) with 51 performance features, excluding match-outcome variables to emphasize individual actions. Using nested cross-validation, LightGBM captured 79.4% of goal-scoring observations within the top 20% of ranked observations; an out-of-bag (OOB) bootstrap gains analysis yielded 73.9% capture at Top 20% (lift = 3.69x; 95% CI: 63.9%−84.3%). Permutation and SHAP consensus highlighted tactical availability (Total Offers) and combined technical/physical workload indicators (Passes Attempted, Jogging Distance, Top Speed). This proof-of-concept study shows that ranking-based evaluation improves scouting efficiency using basic match statistics, while thresholds and feature weights require validation in other competitive contexts.

Suggested Citation

  • Songyi Song & Hee-Su Kim, 2026. "Efficient talent identification in women’s football: A ranking-based approach for goal scoring analysis," PLOS ONE, Public Library of Science, vol. 21(2), pages 1-13, February.
    • Handle: RePEc:plo:pone00:0342115
    DOI: 10.1371/journal.pone.0342115
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    References listed on IDEAS

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    1. Constantinou Anthony Costa & Fenton Norman Elliott, 2012. "Solving the Problem of Inadequate Scoring Rules for Assessing Probabilistic Football Forecast Models," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-14, March.
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