# QPass: a Merit-based Evaluation of Soccer Passes

arXiv: https://arxiv.org/abs/1608.03532 · HTML: https://mlsanity.com/paper/1608.03532/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2016.

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [Through the Gaps: Uncovering Tactical Line-Breaking Passes with Clustering](https://mlsanity.com/paper/2506.06666/) (2025)
- [Towards optimized actions in critical situations of soccer games with deep reinforcement learning](https://mlsanity.com/paper/2109.06625/) (2021)
- [Automated Explanation of Machine Learning Models of Footballing Actions in Words](https://mlsanity.com/paper/2504.00767/) (2025)

## Abstract (arXiv)

Quantitative analysis of soccer players' passing ability focuses on descriptive statistics without considering the players' real contribution to the passing and ball possession strategy of their team. Which player is able to help the build-up of an attack, or to maintain the possession of the ball? We introduce a novel methodology called QPass to answer questions like these quantitatively. Based on the analysis of an entire season, we rank the players based on the intrinsic value of their passes using QPass. We derive an album of pass trajectories for different gaming styles. Our methodology reveals a quite counterintuitive paradigm: losing the ball possession could lead to better chances to win a game.