  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%.
