# Measuring Football Players' On-the-ball Contributions From Passes During Games

arXiv: https://arxiv.org/abs/1810.02252 · HTML: https://mlsanity.com/paper/1810.02252/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2018.

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

- [Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction](https://mlsanity.com/paper/2206.01899/) (2022)
- [Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning](https://mlsanity.com/paper/2305.17886/) (2023)
- [A Multi-stage deep architecture for summary generation of soccer videos](https://mlsanity.com/paper/2205.00694/) (2022)
- [Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer](https://mlsanity.com/paper/2512.00203/) (2025)

## Abstract (arXiv)

Several performance metrics for quantifying the in-game performances of individual football players have been proposed in recent years. Although the majority of the on-the-ball actions during games constitutes of passes, many of the currently available metrics focus on measuring the quality of shots only. To help bridge this gap, we propose a novel approach to measure players' on-the-ball contributions from passes during games. Our proposed approach measures the expected impact of each pass on the scoreline.