The Economics of AI-Driven Injury Prevention in Professional Football: A Machine Learning Approach to Cost-Benefit Analysis

Abstract

Machine learning achieves high accuracy in predicting football injuries, yet no published study has quantified the economic return on deploying these systems. This paper develops the first integrated cost-benefit framework that links machine learning prediction performance to empirical injury-cost data through Monte Carlo simulation. Five algorithms - logistic regression, random forest, XGBoost, support-vector machine, and a multilayer perceptron - were benchmarked on a controlled football injury dataset; the best model (logistic regression) achieved a mean cross-validated AUC of 0.664 and a held-out AUC of 0.754. Direct medical costs and market-value depreciation were estimated from a Transfermarkt panel of 39,034 injury–valuation pairs covering 6 injury categories; anterior cruciate ligament injuries showed the largest mean post-injury market-value drop (−11.6%, median 215 days out). A 10,000-iteration Monte Carlo simulation produced a median return on investment of -13.7% with a break-even probability of 0.43, and partial-rank correlation analysis identified prediction recall (|ρ|=0.84) as the dominant driver of ROI variance. The economic case for AI injury prevention is therefore sensitive to the quality of the underlying predictive signal as well as to the organizational capacity of the adopting club to translate that signal into avoided injuries.