Abstract
This paper develops an interpretable decision-support framework for speedway analytics based on an existing Expected Points (xP) model. The proposed framework adds a two-level interpretive layer to model outputs. First, heat-level entropy is used to describe the dispersion of normalized expected-point shares within a heat. Second, rider-level Bias and the Rider Deviation Index (RDI) quantify the direction and variability of longitudinal deviations between observed and expected performance. The empirical analysis is based on six seasons of Polish PGE Ekstraliga data (2020-2025), comprising 23,500 rider performances across 406 matches. The results indicate that entropy distinguishes heats with stronger structural imbalance from those with more even competition. In contrast, Bias and RDI identify riders with systematic overperformance, underperformance, or stable deviation patterns relative to model expectations. The study contributes a formal interpretive framework for translating existing xP outputs into decision-oriented summaries at both the heat and rider levels.
Paper Type
Full Paper
DOI
10.62036/ISD.2026.43
An Interpretable Decision-Support Framework for Speedway Analytics: Heat-Level Entropy and Rider-Level Deviation Metrics
This paper develops an interpretable decision-support framework for speedway analytics based on an existing Expected Points (xP) model. The proposed framework adds a two-level interpretive layer to model outputs. First, heat-level entropy is used to describe the dispersion of normalized expected-point shares within a heat. Second, rider-level Bias and the Rider Deviation Index (RDI) quantify the direction and variability of longitudinal deviations between observed and expected performance. The empirical analysis is based on six seasons of Polish PGE Ekstraliga data (2020-2025), comprising 23,500 rider performances across 406 matches. The results indicate that entropy distinguishes heats with stronger structural imbalance from those with more even competition. In contrast, Bias and RDI identify riders with systematic overperformance, underperformance, or stable deviation patterns relative to model expectations. The study contributes a formal interpretive framework for translating existing xP outputs into decision-oriented summaries at both the heat and rider levels.
Recommended Citation
Kaczorek, P., Piłka, T. & Górecki, T.(2026). An Interpretable Decision-Support Framework for Speedway Analytics: Heat-Level Entropy and Rider-Level Deviation Metrics. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.43