What is it about?
Professional soccer players are playing more matches than ever, and injuries are on the rise. Traditional tools like the Acute:Chronic Workload Ratio (ACWR) are too basic to accurately predict injury risk. Our team at the University of Granada developed the Footballer Workload Footprint (FWF), an AI-driven method that transforms GPS data from training and matches into a unique “workload fingerprint” for each player. This allows us to detect patterns that indicate when a player is at greater risk of injury.
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Photo by Omar Ramadan on Unsplash
Why is it important?
Injuries not only shorten players’ careers but also impact team performance and financial investment. By predicting risk earlier and more precisely than ACWR, FWF helps coaches, medical staff, and sports scientists manage training loads smarter, prevent injuries, and keep players on the field. It’s a step toward safer, data-driven training in elite soccer — and could shape injury prevention across many sports.
Perspectives
As a data scientist and lifelong sports fan, I’ve always been fascinated by the gap between what data can reveal and how we actually protect athletes’ health. Watching players suffer preventable injuries is frustrating — for them, for teams, and for fans. With this study, we wanted to move beyond simple ratios and create a smarter way to understand training loads. What excites me most is that the Footballer Workload Footprint isn’t just a research concept — it’s something coaches, medical staff, and clubs could start using in the real world. If this tool helps even one player avoid a serious injury, all the effort behind this work will have been worthwhile.
PhD Jaime B. Matas-Bustos
Universidad de Granada
Read the Original
This page is a summary of: Advanced feature engineering in Acute:Chronic Workload Ratio (ACWR) calculation for injury forecasting in elite soccer, PLOS One, July 2025, PLOS,
DOI: 10.1371/journal.pone.0327960.
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