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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150736

Title:	Rating players by Laplace’s approximation and dynamic modeling
Authors:	翁久幸;華軒甫 Weng, Ruby Chiu-Hsing;Hua, Hsuan-Fu;Chang, Ching-Ju;Lin, Tse-Ching
Contributors:	統計系
Keywords:	Bradley–Terry;Elo;Laplace approximation;McNemar test;Posterior distribution
Date:	2024-07
Issue Date:	2024-04-11
Abstract:	The Elo rating system is a simple and widely used method for calculating players’ skills from paired comparison data. Many have extended it in various ways. Yet the question of updating players’ variances remains to be further explored. In this paper, we address the issue of variance update by using the Laplace approximation for posterior distributions, together with a random walk model for the dynamics of players’ strengths and a lower bound on player variance. The random walk model is motivated by the Glicko system, but here we assume nonidentically distributed increments to deal with player heterogeneity. Experiments on men’s professional matches showed that the prediction accuracy slightly improves when the variance update is performed. They also showed that new players’ strengths may be better captured with the variance update.
Relation:	International Journal of Forecasting, Vol.40, No.3, pp.1152-1165
Data Type:	article
DOI 連結:	https://doi.org/10.1016/j.ijforecast.2023.10.004
DOI:	10.1016/j.ijforecast.2023.10.004
Appears in Collections:	[統計學系] 期刊論文

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