Abstract:
: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.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Motegi S,Masuda Ndoi
10.1038/srep00904subject
Has Abstractpub_date
2012-01-01 00:00:00pages
904issn
2045-2322journal_volume
2pub_type
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