# A Confidence-Calibrated MOBA Game Winner Predictor

arXiv: https://arxiv.org/abs/2006.15521 · HTML: https://mlsanity.com/paper/2006.15521/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Optimal Team Economic Decisions in Counter-Strike](https://mlsanity.com/paper/2109.12990/) (2021)
- [Explainable e-sports win prediction through Machine Learning classification in streaming](https://mlsanity.com/paper/2510.19671/) (2025)

## Abstract (arXiv)

In this paper, we propose a confidence-calibration method for predicting the winner of a famous multiplayer online battle arena (MOBA) game, League of Legends. In MOBA games, the dataset may contain a large amount of input-dependent noise; not all of such noise is observable. Hence, it is desirable to attempt a confidence-calibrated prediction. Unfortunately, most existing confidence calibration methods are pertaining to image and document classification tasks where consideration on uncertainty is not crucial. In this paper, we propose a novel calibration method that takes data uncertainty into consideration. The proposed method achieves an outstanding expected calibration error (ECE) (0.57%) mainly owing to data uncertainty consideration, compared to a conventional temperature scaling method of which ECE value is 1.11%.