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arXiv · 2006.15521

A Confidence-Calibrated MOBA Game Winner Predictor

Dong-Hee Kim·Changwoo Lee·Ki-Seok Chung
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Abstract

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%.

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Dong-Hee Kim, Changwoo Lee, Ki-Seok Chung. 2020-06-28. A Confidence-Calibrated MOBA Game Winner Predictor. https://arxiv.org/abs/2006.15521

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