# GCN-WP -- Semi-Supervised Graph Convolutional Networks for Win Prediction in Esports

arXiv: https://arxiv.org/abs/2207.13191 · HTML: https://mlsanity.com/paper/2207.13191/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 26 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Interpretable and Efficient Heterogeneous Graph Convolutional Network](https://mlsanity.com/paper/2005.13183/) (2020)
- [An Empirical Analysis of Feature Engineering for Predictive Modeling](https://mlsanity.com/paper/1701.07852/) (2017)
- [A new method for comparing rankings through complex networks: Model and analysis of competitiveness of major European soccer leagues](https://mlsanity.com/paper/1307.3412/) (2013)
- [The Influence of Social Ties on Performance in Team-based Online Games](https://mlsanity.com/paper/1812.02272/) (2018)

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

- [Explainable e-sports win prediction through Machine Learning classification in streaming](https://mlsanity.com/paper/2510.19671/) (2025)

## Abstract (arXiv)

Win prediction is crucial to understanding skill modeling, teamwork and matchmaking in esports. In this paper we propose GCN-WP, a semi-supervised win prediction model for esports based on graph convolutional networks. This model learns the structure of an esports league over the course of a season (1 year) and makes predictions on another similar league. This model integrates over 30 features about the match and players and employs graph convolution to classify games based on their neighborhood. Our model achieves state-of-the-art prediction accuracy when compared to machine learning or skill rating models for LoL. The framework is generalizable so it can easily be extended to other multiplayer online games.