# Explainable e-sports win prediction through Machine Learning classification in streaming

arXiv: https://arxiv.org/abs/2510.19671 · HTML: https://mlsanity.com/paper/2510.19671/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions](https://mlsanity.com/paper/1901.03729/) (2019)
- [explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning](https://mlsanity.com/paper/1908.00087/) (2019)
- [A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME](https://mlsanity.com/paper/2305.02012/) (2023)
- [Esports Athletes and Players: a Comparative Study](https://mlsanity.com/paper/1812.03200/) (2018)
- [A Confidence-Calibrated MOBA Game Winner Predictor](https://mlsanity.com/paper/2006.15521/) (2020)
- [Adaptive Hoeffding Tree with Transfer Learning for Streaming Synchrophasor Data Sets](https://mlsanity.com/paper/2501.16354/) (2025)
- [GCN-WP -- Semi-Supervised Graph Convolutional Networks for Win Prediction in Esports](https://mlsanity.com/paper/2207.13191/) (2022)

## Abstract (arXiv)

The increasing number of spectators and players in e-sports, along with the development of optimized communication solutions and cloud computing technology, has motivated the constant growth of the online game industry. Even though Artificial Intelligence-based solutions for e-sports analytics are traditionally defined as extracting meaningful patterns from related data and visualizing them to enhance decision-making, most of the effort in professional winning prediction has been focused on the classification aspect from a batch perspective, also leaving aside the visualization techniques. Consequently, this work contributes to an explainable win prediction classification solution in streaming in which input data is controlled over several sliding windows to reflect relevant game changes. Experimental results attained an accuracy higher than 90 %, surpassing the performance of competing solutions in the literature. Ultimately, our system can be leveraged by ranking and recommender systems for informed decision-making, thanks to the explainability module, which fosters trust in the outcome predictions.