Learning to Rank Sports Teams on a Graph
Source abstract
Abstract
To improve the prediction ability of ranking models in sports, a generalized PageRank model is introduced. In the model, a game graph is constructed from the perspective of Bayesian correction with game results. In the graph, nodes represent teams, and a link function is used to synthesize the information of each game to calculate the weight on the graph’s edge. The parameters of the model are estimated by minimizing the loss function, which measures the gap between the predicted rank obtained by the model and the actual rank. The application to the National Basketball Association (NBA) data shows that the proposed model can achieve better prediction performance than the existing ranking models.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sports Analytics and Performance · Software Engineering Research · Advanced Text Analysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Statistics, Huaqiao University, Xiamen 361021, China
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
References (21)
Show source-provided references
1. Elo, A.E. (1978). The Rating of Chessplayers, Past and Present, Arco Pub.
2. The Perron-Frobenius Theorem and the Ranking of Football Teams10.1137/1035004 ↗
3. Massey, K. (1997). Statistical Models Applied to the Rating of Sports Teams, Bluefield College.
4. Colley, W. (2002). Colley’s Bias Free College Football Ranking Method. [Ph.D. Thesis, Princeton University].
5. A logistic regression/Markov chain model for NCAA basketball10.1002/nav.20170 ↗
6. A logistic regression/Markov chain model for American college football10.1515/ijcss-2017-0014 ↗
7. A network-based ranking system for US college football10.1088/1742-5468/2005/10/P10014 ↗
8. Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The Pagerank Citation Ranking: Bringing Order to the Web, Stanford InfoLab. Technical Report.
9. Radicchi, F. (2011). Who is the best player ever? A complex network analysis of the history of professional tennis. PLoS ONE, 6.10.1371/journal.pone.0017249 ↗
10. Govan, A.Y., Meyer, C.D., and Albright, R. (2008, January 16–19). Generalizing Google’s PageRank to rank national football league teams. Proceedings of the SAS Global Forum, San Antonio, TX, USA.
11. Xia, V., Jain, K., Krishna, A., and Brinton, C.G. (2018, January 21–23). A network-driven methodology for sports ranking and prediction. Proceedings of the 2018 52nd Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA.10.1109/CISS.2018.8362324 ↗
12. Lazova, V., and Basnarkov, L. (2015). PageRank approach to ranking national football teams. arXiv.
13. Cintia, P., Coscia, M., and Pappalardo, L. (2016, January 18–21). The Haka network: Evaluating rugby team performance with dynamic graph analysis. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA.10.1109/ASONAM.2016.7752377 ↗
14. Predicting the NHL playoffs with PageRank
15. Generic temporal features of performance rankings in sports and games10.1140/epjds/s13688-016-0096-y ↗
16. Ribeiro, H.V., Mukherjee, S., and Zeng, X.H.T. (2016). The advantage of playing home in NBA: Microscopic, team-specific and evolving features. PLoS ONE, 11.10.1371/journal.pone.0152440 ↗
17. Zhang, S., Gomez, M.Á., Yi, Q., Dong, R., Leicht, A., and Lorenzo, A. (2020). Modelling the Relationship between Match Outcome and Match Performances during the 2019 FIBA Basketball World Cup: A Quantile Regression Analysis. Int. J. Environ. Res. Public Health, 17.10.3390/ijerph17165722 ↗
18. Chapelle, O., and Chang, Y. (2010, January 26). Yahoo! Learning to rank challenge overview. Proceedings of the 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel.
19. Learning to rank on graphs10.1007/s10994-010-5185-8 ↗
20. Learning to Rank in Entity Relationship Graphs10.1287/ijoc.2018.0837 ↗
21. Properties of sports ranking methods10.1057/s41274-017-0266-8 ↗