Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan.
Sci Rep. 2012;2:904. doi: 10.1038/srep00904. Epub 2012 Dec 5.
From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.
从网络的角度来看，运动员或运动队的排名系统相当于体育网络的中心度度量，其中有向链接表示单次比赛的结果。之前提出的基于网络的排名系统是从静态网络中得出的，即随时间聚合比赛结果。然而，运动员（或团队）的分数是随时间波动的。在巅峰状态下击败知名运动员，直观上比在其他时期击败同一运动员更有价值。为了考虑到这一因素，我们提出了这种基于网络的排名系统的动态变体，并将其应用于职业男子网球数据。我们为每个运动员的分数推导出了一组线性在线更新方程。与静态排名系统相比，所提出的排名系统可以更准确地预测未来比赛的结果。
