# 6MapNet: Representing soccer players from tracking data by a triplet network

arXiv: https://arxiv.org/abs/2109.04720 · HTML: https://mlsanity.com/paper/2109.04720/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach](https://mlsanity.com/paper/1802.04987/) (2018)
- [Who can replace Xavi? A passing motif analysis of football players](https://mlsanity.com/paper/1506.07768/) (2015)

## Built on by (most cited first, 1 of 1)

- [SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data](https://mlsanity.com/paper/2206.10926/) (2022)

## Abstract (arXiv)

Although the values of individual soccer players have become astronomical, subjective judgments still play a big part in the player analysis. Recently, there have been new attempts to quantitatively grasp players' styles using video-based event stream data. However, they have some limitations in scalability due to high annotation costs and sparsity of event stream data. In this paper, we build a triplet network named 6MapNet that can effectively capture the movement styles of players using in-game GPS data. Without any annotation of soccer-specific actions, we use players' locations and velocities to generate two types of heatmaps. Our subnetworks then map these heatmap pairs into feature vectors whose similarity corresponds to the actual similarity of playing styles. The experimental results show that players can be accurately identified with only a small number of matches by our method.