Advanced feature engineering in Acute:Chronic Workload Ratio (ACWR) calculation for injury forecasting in elite soccer Matas Bustos, Jaime Mora García, Antonio Miguel de Hoyo Lora, Moisés Nieto Alarcón, Alejandro González Fernández, Francisco Tomás Controlling training monotony and monitoring external workload using the Acute:Chronic Workload Ratio (ACWR) is a common practice among elite soccer teams to prevent noncontact injuries. However, recent research has questioned whether ACWR offers sufficient predictive power for injury prevention in elite competition settings. In this paper, we propose a novel feature engineering framework for training load management, inspired by bilinear modeling and signal processing principles. Our method represents external workload variables, derived from GPS data, as discrete time series, which are then integrated into a temporal matrix termed the Footballer Workload Footprint (FWF). We introduce calculus-based techniques—applying integral and differential operations—to derive two representations from the FWF matrix: a cumulative workload matrix (∑ T FWF) generalizing Acute Workload (AW), and a temporal variation matrix (Δ T FWF) generalizing Chronic Workload (CW) and formulating the ACWR. Our approach makes traditional workload metrics suitable for modern machine learning. Using real-world data from an elite soccer team competing in LaLiga (Spain’s top division) and UEFA tournaments, we conducted exploratory and confirmatory analyses comparing multivariate models trained on FWF-derived features against those using traditional ACWR calculations. The FWFbased models consistently outperformed baseline methods across key performance metrics—including the Area Under the ROC Curve (ROC-AUC), Precision-Recall AUC (PR-AUC), Geometric Mean (G-Mean), and Accuracy—while reducing Type I and Type II errors. Tested on temporally independent holdout data, our top model performed robustly across all metrics with 95% confidence intervals. Permutation tests revealed a significant association between FWF matrices and injury risk, supporting the empirical validity of our approach. Additionally, we introduce an interpretability framework based on heatmap visualizations of the FWF’s cumulative and temporal variations, enhancing explainability. These findings indicate that our approach offers a robust, interpretable, and generalizable framework for sports science and medical professionals involved in injury prevention and training load monitoring. 2025-09-10T08:21:24Z 2025-09-10T08:21:24Z 2025-07-23 journal article Matas-Bustos JB, Mora-García AM, de Hoyo Lora M, Nieto-Alarcón A, Gonzalez-Fernández FT (2025) Advanced feature engineering in Acute:Chronic Workload Ratio (ACWR) calculation for injury forecasting in elite soccer. PLoS One 20(7): e0327960. https://doi.org/10.1371/journal.pone.0327960 https://hdl.handle.net/10481/106209 10.1371/journal.pone.0327960 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Plos One