Running Predictive Injury Risk Analytics
Injury prevention is no longer reactive — it's predictive!
Project Background
As an athlete, I’ve always been fascinated by the why behind injuries — why they happen, when they strike, and how to stay one step ahead.
When elite athletes push their limits day in and day out, injuries aren't just unfortunate — they're costly. Lost training days, missed competitions, and compromised performance can disrupt careers and derail teams. Our latest analytics project tackled this very issue: how can we predict injury risk in performance runners using real-world training data?
Welcome to an in-depth exploration of Human Performance Analytics, where we blend sports science with machine learning to uncover the hidden patterns that may forecast injury — and ultimately help prevent it.
This study leveraged data from a Dutch national and international-level distance running team. Spanning 42,798 training observations across 74 runners over nine years, the dataset included daily and weekly training metrics like:
- Number of sessions
- Total kilometers run
- Perceived exertion, recovery, and training success
- Injury occurrence (binary)
- Intensity zones
- Derived variables like weighted exertion and recovery averages
These data points gave us a powerful lens into the physical demands placed on athletes and how those demands correlate with injury.
Methodology: From Patterns to Prediction
We began by identifying patterns in athlete load and recovery over time. Using statistical modeling techniques and performance science principles, we crafted a predictive system that flags injury vulnerability based on training history and recovery trends.
Rather than just looking at single data points, we built context-aware indicators — such as changes in exertion vs. recovery — that account for cumulative load and readiness. These indicators were then incorporated into a tailored risk scoring system.
While we’ve kept the details of our approach proprietary, our process emphasizes interpretability and applicability for coaches on the ground.
What We Found
Our predictive model revealed strong signals between injury events and:
- Peaks in training exertion and session volume
- Inadequate recovery during the weeks preceding injury
These insights helped surface athletes trending toward risk, enabling timely interventions — whether that’s rest, reduced volume, or closer monitoring.
How Coaches Can Use This
Based on the data, we created a "Vulnerability to Injury" dashboard — a one-stop tool for coaches to:
- Track team and individual workloads
- Spot emerging injury risks
- Visualize exertion vs. recovery trends
- Get athlete-specific recommendations
Figure 1. Example Dashboard Visualization
The application enables:
- Real-time flagging of at-risk athletes
- Load management recommendations
- Team-level comparisons and alerts
- Visualization of key predictors like exertion and recovery
This tool acts as a command center for injury prevention, adaptable across sports and performance settings — from individual athletes to elite sport teams.
Contact us to request a demo or learn more.
References
- Lovdal, S., Den Hartigh, R. J. R., & Azzopardi, G. (2020). Injury Prediction in Competitive Runners with Machine Learning, International Journal of Sports Physiology and Performance. https://doi.org/10.34894/UWU9PV
- Shashwat. (2021). Injury Prediction for Competitive Runners Dataset. Retrieved from https://www.kaggle.com/datasets/shashwatwork/injury-prediction-for-competitive-runners