# Distinguishing Between Roles of Football Players in Play-by-play Match Event Data

arXiv: https://arxiv.org/abs/1809.05173 · HTML: https://mlsanity.com/paper/1809.05173/

## 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)

- [FOOTPASS: A Multi-Modal Multi-Agent Tactical Context Dataset for Play-by-Play Action Spotting in Soccer Broadcast Videos](https://mlsanity.com/paper/2511.16183/) (2025)
- [Characterizing player's playing styles based on Player Vectors for each playing position in the Chinese Football Super League](https://mlsanity.com/paper/2205.02731/) (2022)
- [Passing Heatmap Prediction Based on Transformer Model and Tracking Data](https://mlsanity.com/paper/2309.01526/) (2023)
- [Wide Open Gazes: Quantifying Visual Exploratory Behavior in Soccer with Pose Enhanced Positional Data](https://mlsanity.com/paper/2602.18519/) (2026)

## Abstract (arXiv)

Over the last few decades, the player recruitment process in professional football has evolved into a multi-billion industry and has thus become of vital importance. To gain insights into the general level of their candidate reinforcements, many professional football clubs have access to extensive video footage and advanced statistics. However, the question whether a given player would fit the team's playing style often still remains unanswered. In this paper, we aim to bridge that gap by proposing a set of 21 player roles and introducing a method for automatically identifying the most applicable roles for each player from play-by-play event data collected during matches.