TY - GEN
T1 - A network-driven methodology for sports ranking and prediction
AU - Xia, Vincent
AU - Jain, Kavirath
AU - Krishna, Akshay
AU - Brinton, Christopher G.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/21
Y1 - 2018/5/21
N2 - Recent years have seen increasing interest in ranking elite athletes and teams in professional sports leagues, and in predicting the outcomes of games. In this work, we draw an analogy between this problem and one in the field of search engine optimization, namely, that of ranking webpages on the Internet. Motivated by the famous PageRank algorithm, our TeamRank methods define directed graphs of sports teams based on the observed outcomes of individual games, and use these networks to infer the importance of teams that determines their rankings. In evaluating these methods on data from recent seasons in the National Football League (NFL) and National Basketball Association (NBA), we find that they can predict the outcomes of games with up to 70% accuracy, and that they provide useful rankings of teams that cluster by league divisions. We also propose some extensions to TeamRank that consider overall team win records and shifts in momentum over time.
AB - Recent years have seen increasing interest in ranking elite athletes and teams in professional sports leagues, and in predicting the outcomes of games. In this work, we draw an analogy between this problem and one in the field of search engine optimization, namely, that of ranking webpages on the Internet. Motivated by the famous PageRank algorithm, our TeamRank methods define directed graphs of sports teams based on the observed outcomes of individual games, and use these networks to infer the importance of teams that determines their rankings. In evaluating these methods on data from recent seasons in the National Football League (NFL) and National Basketball Association (NBA), we find that they can predict the outcomes of games with up to 70% accuracy, and that they provide useful rankings of teams that cluster by league divisions. We also propose some extensions to TeamRank that consider overall team win records and shifts in momentum over time.
UR - http://www.scopus.com/inward/record.url?scp=85048555306&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048555306&partnerID=8YFLogxK
U2 - 10.1109/CISS.2018.8362324
DO - 10.1109/CISS.2018.8362324
M3 - Conference contribution
AN - SCOPUS:85048555306
T3 - 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
SP - 1
EP - 6
BT - 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 52nd Annual Conference on Information Sciences and Systems, CISS 2018
Y2 - 21 March 2018 through 23 March 2018
ER -