Previous studies have shown that variation in PlayerLoad (PL) could be used to detect fatigue in soccer players. Machine learning techniques (ML) were used to develop a new locomotor efficiency index (LEI) based on the prediction of PL. Sixty-four elite soccer players were monitored during an entire season. GPS systems were employed to collect external load data, which in turn were used to predict PL during training/matches. Random Forest Regression (RF) produced the best performance (mean absolute percentage error = 0.10 ± 0.01) and was included in further analyses. The difference between the PL value predicted by the ML model and the real one was calculated, individualized for each player using a z-score transformation (LEI), and interpreted as a sign of fatigue (negative LEI) or neuromuscular readiness (positive LEI). A linear mixed model was used to analyze how LEI changed according to the period of the season, day of the week, and weekly load. Regarding seasonal variation, the lowest and highest LEI values were recorded at the beginning of the season and in the middle of the season, respectively. On a weekly basis, our results showed lower values on match day − 2, while high weekly training loads were associated with a reduction in LEI.

Coaches in professional football need to estimate how many minutes a player can tolerate in a match before relevant fatigue occurs. This study aimed to develop a framework to translate monitoring information into individualised, minute-based fatigue thresholds. Over four seasons in an elite club, external load (total distance, high-speed running, mechanical work) and heart rate were collected in training. Machine-learning-derived fitness and fatigue indices were computed and combined with 7- and 28-day load variables in a Random Forest regression model predicting match minutes. The trained model was then used to simulate four fatigue conditions by fixing the match-day fatigue index (z-FA_match = 0, −1, −2, −3). In an independent test season, the model showed a mean absolute error of 22.5 min and R² = 0.17 for playing time prediction, with z-FA_match as the most influential predictor. Simulated fatigue thresholds occurred in an ordered way (0 = 57.1, −1 = 64.9, −2 = 84.8, −3 = 84.4) and differed across season period, playing position, overall seasonal minutes, and return-to-play status. Integrating external load with fitness and fatigue indices via machine learning can provide individualised estimates of when players are likely to reach fatigue states, supporting decisions on selection, substitutions, and return-to-play management.