# H-VAEP and H-xT: Valuing Offensive On-the-Ball Actions in Handball by Estimating Probabilities

arXiv: https://arxiv.org/abs/2608.12926 · HTML: https://mlsanity.com/paper/2608.12926/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 13 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation](https://mlsanity.com/paper/1805.11088/) (2018)
- [Action Valuation in Sports: A Survey](https://mlsanity.com/paper/2504.06163/) (2025)
- [Development of an expected possession value model to analyse team attacking performances in rugby league](https://mlsanity.com/paper/2105.05303/) (2021)
- [The trade-off between model flexibility and accuracy of the Expected Threat model in football](https://mlsanity.com/paper/2511.09457/) (2025)

## Abstract (arXiv)

Traditional player evaluation in professional handball relies on basic box-score metrics or heuristic indices, which fail to credit the multi-player build-up chain. While football (soccer) analytics has adopted Expected Threat (xT) and Valuing Actions by Estimating Probabilities (VAEP), these event-based action valuation frameworks have not yet been adapted to handball. In this paper, we present the first comprehensive adaptation and evaluation of xT and VAEP for handball, utilizing five seasons of tracking-derived event data from the Handball Bundesliga. We develop Handball-xT (H-xT) using a handball-native court zoning layout, demonstrating via simulations that it is systematically more robust than standard rectangular grids. We optimize Handball-VAEP (H-VAEP) by tailoring its feature space and selecting the context length to limit team-identity leakage. Our evaluation shows that H-VAEP yields exceptionally stable, discriminative, and intuitive player ratings that highlight build-up play. Finally, we release our complete code repository to help professional clubs deploy these models.