ArticleSports health2026
Can Field-Based Screening Predict ACL Injury Risk in Women Footballers? External Validation of a Prediction Model.
Article in Sports health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAnterior cruciate ligament (ACL) rupture is a devastating injury that occurs 3 to 7 times more frequently in women footballers than in their male counterparts. Identifying players at elevated risk is critical for targeted injury prevention; however, no ACL injury prediction model has undergone external validation in women. Therefore, this study aimed to externally validate a field-based ACL injury prediction model in women footballers. HYPOTHESIS: A previously developed field-based ACL injury prediction model would demonstrate good predictive performance in an independent cohort of women footballers. STUDY
designProspective cohort study. LEVEL OF EVIDENCE: Level 2.
methodsWomen footballers (
resultsIn the external validation cohort, the original model demonstrated reduced discrimination (AUC, 0.67; 95% CI, 0.49-0.82) and poor calibration (intercept, -0.79; slope, 0.53), indicating risk overestimation. The updated 5-variable model improved discrimination (apparent AUC, 0.76; optimism-corrected AUC, 0.72; 95% CI, 0.61-0.82), and calibration intercept (-0.00), but the calibration slope (1.38) and instability across bootstrapped samples indicated imprecise individual risk estimates.
conclusionThe original ACL injury prediction model did not generalize well to an independent cohort of women footballers. Although model updating improved overall predictive performance, calibration remained suboptimal, and predictions were unstable. Field-based strength and biomechanical assessments may support group-level ACL injury risk stratification but are not yet suitable for precise individual risk prediction. CLINICAL RELEVANCE: Field-based measures of strength and biomechanics can help identify groups of women footballers at elevated ACL injury risk, achieving 72% classification accuracy. These screening measures are practical and scalable, requiring <10 minutes per player, and assess modifiable factors that may inform targeted injury prevention strategies.
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