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Single-team models using machine learning identify influential predictors of athlete availability in elite female athletes, with differing predictors between lacrosse and soccer teams.

This study used elastic net regression to identify predictors of athlete availability (percentage of unmodified practices and games) in 52 NCAA Division I female lacrosse and soccer athletes. Combined model RMSE was 17.8%, while single-team models showed RMSEs of 8.9% (lacrosse) and 17.8% (soccer), with no force plate variables selected for lacrosse and no body composition variables for soccer.

Last updated: Jul 11, 2026•0 RCTs•📖 Read as article →

Evidence Score

Evidence Score32/100
Human RCT☆☆☆☆☆
Meta-analysis☆☆☆☆☆
Mechanism★★★★★
Safety★★★★☆
Confidencelow

Study Evidence

Study 1. Using Machine Learning for Identification of Athlete Availability Predictors in a Multisport Elite Female Athlete Cohort.

observational

Moore SR, Cantú EI, Brantner CL, Britton ME, DelBiondo GM, Blue MNM, Bruinvels G, Hackney AC, Register-Mihalik JK, Smith-Ryan AE · Journal of strength and conditioning research (2026)

Participants: N/A
Duration: one season
Intervention: Machine learning (elastic net regression) applied to training load, recovery, wellness, body composition, and force plate jump data.
Outcome: Athlete availability (percentage of unmodified practices and games).
Effect Size: N/A
Population: 52 National Collegiate Athletic Association Division I elite female athletes (28 lacrosse, 24 soccer).

Result:

PubMed: 42413555DOI: 10.1519/JSC.0000000000005611

Mechanism Graph

Collect training load, recovery, wellness, body composition, and force plate data
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Apply elastic net regression to identify influential predictors
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Compare combined vs single-team models
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Result: Single-team models yield lower RMSE and more consistent predictors

Limitations

  • ⚠Small sample size (n=52) limits generalizability
  • ⚠Single-season data may not capture year-to-year variability
  • ⚠No causal inference possible from observational design

Frequently Asked Questions

What machine learning method was used?▼

Elastic net regression was used to identify influential predictors of athlete availability.

Which predictors were most important for lacrosse athletes?▼

Training load, recovery, and wellness variables were selected; no force plate variables were selected for lacrosse athlete availability.

How was athlete availability defined?▼

Athlete availability was defined as the percentage of unmodified practices and games.

Why might single-team models be better than combined models?▼

Single-team models showed lower RMSE (8.9% for lacrosse vs 17.8% combined) and identified different predictors, suggesting team-specific factors are important.

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References

  1. 1.Moore SR, Cantú EI, Brantner CL, Britton ME, DelBiondo GM, Blue MNM, Bruinvels G, Hackney AC, Register-Mihalik JK, Smith-Ryan AE. "Using Machine Learning for Identification of Athlete Availability Predictors in a Multisport Elite Female Athlete Cohort.." Journal of strength and conditioning research, 2026. PMID: 42413555 DOI: 10.1519/JSC.0000000000005611
Disclaimer: This content is for educational purposes only and is not medical advice. Evidence scores reflect the quality and quantity of available research, not clinical recommendations. Always consult a healthcare professional before starting any supplement or intervention.