| Title |
A network-based dynamical ranking system for competitive sports
|
|---|---|
| Published in |
Scientific Reports, December 2012
|
| DOI | 10.1038/srep00904 |
| Pubmed ID | |
| Authors |
Shun Motegi, Naoki Masuda |
| 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. |
X Demographics
The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 2 | 13% |
| Indonesia | 1 | 6% |
| United Kingdom | 1 | 6% |
| India | 1 | 6% |
| Unknown | 11 | 69% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 14 | 88% |
| Scientists | 2 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 1% |
| United States | 1 | 1% |
| Italy | 1 | 1% |
| Switzerland | 1 | 1% |
| Unknown | 65 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 25% |
| Student > Master | 12 | 17% |
| Student > Bachelor | 8 | 12% |
| Researcher | 6 | 9% |
| Professor > Associate Professor | 5 | 7% |
| Other | 13 | 19% |
| Unknown | 8 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 19% |
| Physics and Astronomy | 9 | 13% |
| Mathematics | 8 | 12% |
| Agricultural and Biological Sciences | 5 | 7% |
| Sports and Recreations | 5 | 7% |
| Other | 16 | 23% |
| Unknown | 13 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 December 2022.
All research outputs
#3,568,729
of 25,193,883 outputs
Outputs from Scientific Reports
#30,337
of 138,505 outputs
Outputs of similar age
#34,441
of 290,955 outputs
Outputs of similar age from Scientific Reports
#80
of 296 outputs
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We're also able to compare this research output to 296 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.