Forecasting adverse outcomes in athletes, whether during training or competition, remains a complex and evolving challenge. As data availability increases and analytical methods mature, our understanding of both the problem space and the most effective modelling approaches continues to improve.
Recent developments in healthcare analytics and “bad outcome” forecasting offer particularly relevant insights that can be adapted to enhance existing systems in sport. One of the most important lessons is that the most accurate predictive model is not always the most effective operational model. In environments with limited resources, decision thresholds must account for capacity constraints, intervention availability, and athlete response behavior, not just predictive accuracy (Chan et al., 2026).
Balancing False Positives and False Negatives
One of the most important challenges in any predictive system is managing the trade-off between False Positives (FP) and False Negatives (FN). In practical terms, these errors directly affect how resources are used and how effectively high-risk individuals are identified.
- Reducing False Negatives is especially critical in operational settings. Missing a high-risk athlete or failing to flag a genuine positive case can lead to preventable adverse outcomes. At present, system utilization sits at approximately 55%, suggesting there is capacity to safely increase sensitivity and capture more at-risk cases without overwhelming operational limits.
- Reducing False Positives remains equally important. Excessive false alarms can dilute focus, create unnecessary interventions, and reduce trust in the system. One promising approach is to better define and refine high-risk clusters, allowing the model to distinguish more precisely between genuinely elevated risk and normal variation.
Ultimately, this modelling challenge reflects a broader operational balance between capacity, prioritization, and efficiency.
Balancing Risk Detection with Operational Capacity
There are two primary competing effects that should be highlighted that influence forecasting performance:
- Underutilization occurs when thresholds are too conservative, resulting in available investigative or intervention capacity going unused.
- Cannibalization occurs when thresholds are too broad, generating excessive alerts that compete for limited resources and potentially reduce focus on the highest-risk individuals (Chan et al., 2026).
The optimal forecasting strategy is therefore not simply identifying more risk events but finding the threshold that balances both effects.
Exploring Advanced Modelling Approaches
Several machine learning methods show strong potential for improving prediction accuracy:
- Random Survival Forests (RSF): Effective for time-to-event prediction and complex data relationships.
- Quantile Regression (QR): Useful for modelling prediction intervals and uncertainty.
- Conditional Inference Trees (CTree): Provide an interpretable framework for decision-making.
- Artificial Neural Networks (ANNs): Strong at capturing nonlinear patterns.
Among these, Random Survival Forests (RSF) emerged as the most consistently effective. Recent healthcare studies found RSF outperformed alternative approaches while supporting both initial and continuously updated predictions (Huang et al., 2025). Its ability to model time-dependent outcomes makes it particularly well suited to athlete risk forecasting.
While model selection remains important, future evaluation should consider operational effectiveness alongside traditional accuracy metrics. A highly accurate model is not always the most effective when deployed within real-world resource constraints (Chan et al., 2026).
Dynamic Prediction and Model Adaptation
A key development in modern forecasting is the shift from static to dynamic prediction.
Rather than generating a single fixed risk estimate, dynamic models continuously update predictions as new information becomes available. This allows for:
- Ongoing refinement of risk estimates
- Adjustment to changing conditions
- Better alignment with evolving athlete risk profiles
Recent healthcare applications have shown that continuously updated predictions can improve forecast quality compared with relying solely on an initial assessment (Huang et al., 2025).
RSF models are particularly effective in this setting because they can incorporate new information and adjust forecasts in real time.
Nested Simulation: A Deeper Layer of Forecasting
Another important advancement is the use of Nested Simulation (NS), a two-layer simulation framework designed to improve predictive performance and better represent uncertainty.
- The outer simulation models how daily risk scores evolve over time.
- The inner simulation generates multiple potential outcome scenarios at key decision points (e.g. race entry).
Beyond improving predictions, nested simulation helps estimate the level of uncertainty that cannot be eliminated. Some prediction errors are inherently irreducible due to factors such as human behavior and natural variability (Huang et al., 2025).
Understanding these limits helps establish realistic expectations and provides a benchmark for achievable forecasting performance.
Real-Time Evaluation and Calibration
For any forecasting system to remain effective, it must be continuously evaluated against real-world outcomes.
In this framework:
- Risk scores are updated in real time
- Individuals may move between risk categories as conditions change
- Forecast performance is regularly recalibrated
Calibration helps ensure forecasts remain aligned with observed outcomes and operational realities.
Beyond statistical accuracy, evaluation should also consider practical impact. A model that performs well on paper may not deliver the greatest value if it does not align with available resources or intervention capacity (Chan et al., 2026).
Sensitivity, Specificity, and Post-Hoc Refinement
Fine-tuning model performance ultimately comes down to threshold calibration and understanding trade-offs between sensitivity and specificity.
- Adjusting thresholds can improve specificity and reduce false positives
- Increasing tolerance for false negatives improves sensitivity and captures more true risks
However, post-hoc analysis suggests that simply targeting high coverage (e.g. 70%) can lead to an overly wide range of false positives, particularly when outliers are present. A more effective strategy is to:
- Raise the lower quantile threshold
- Lower the upper quantile threshold
This produces a better balance between coverage and precision, improving the usability of the predictions in practice.
Conclusion
Recent advances in healthcare analytics provide a strong foundation for improving bad outcome forecasting in athletic performance settings. Techniques such as Random Survival Forests, Dynamic Prediction, and Nested Simulation offer meaningful improvements in both accuracy and adaptability.
When combined with careful calibration and ongoing evaluation, these methods allow for more reliable identification of high-risk cases, better allocation of resources, and ultimately improved athlete safety and performance outcomes.
By Lesya Berbeka, AI and Quantitative Analytics Lead at Kodershop
Works Cited:
Chan, C. W., Han, Y., Li, H., & Ranard, B. L. (2026). Deployment of AI-assisted interventions: Capacity constraints and noisy compliance. arXiv. https://arxiv.org/abs/2604.14370
Huang, Y., Senderovich, A., & Shaposhnik, Y. (2025). Dynamic prediction of waiting time intervals: Application to cancer care facilities. Manufacturing & Service Operations Management. Advance online publication.