A Confidence-Calibrated MOBA Game Winner Predictor
This work addresses the need for reliable uncertainty estimation in MOBA game analytics, though it is incremental as it builds on existing calibration techniques.
The paper tackled the problem of predicting the winner in League of Legends by developing a confidence-calibration method that accounts for data uncertainty, achieving an expected calibration error of 0.57% compared to 1.11% for a conventional method.
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%.