TY - GEN
T1 - Deep similarity learning for soccer team ranking
AU - Manack, Habeebullah
AU - Van Zyl, Terence L.
N1 - Publisher Copyright:
© 2020 International Society of Information Fusion (ISIF).
PY - 2020/7
Y1 - 2020/7
N2 - Soccer match prediction has been a difficult domain for machine learning, currently outperformed by bookmakers' odds and human predictions due to the stochastic nature of soccer. In response, we focus on a Similarity learning approach. Our research involves using Siamese networks and RankNet pair-wise models alongside Transfer learning to predict soccer match rankings. We implemented these models in conjunction with traditional sports tally ranking as well as graph-based PageRank to attain list-wise seasonal rankings for English Premier League spanning seasons 2006 to 2018. Our models used two datasets- EPL seasonal team statistics and an augmentation of these team statistics with external (monetary and transfer) data. Our models have a lower F1 score than a standard neural network, however, perform better in identifying draws and seasonal teams rankings. Transfer Learning models performed better on match-wise rankings while for seasonal rankings, augmented data provides the best predictor (0.72 average). Our proposed Tally Rank provides more accurate seasonal rankings than a graph-based PageRank. Since there is no consistently best-performing model, other Similarity Learning/Ranking models can be considered in the future.
AB - Soccer match prediction has been a difficult domain for machine learning, currently outperformed by bookmakers' odds and human predictions due to the stochastic nature of soccer. In response, we focus on a Similarity learning approach. Our research involves using Siamese networks and RankNet pair-wise models alongside Transfer learning to predict soccer match rankings. We implemented these models in conjunction with traditional sports tally ranking as well as graph-based PageRank to attain list-wise seasonal rankings for English Premier League spanning seasons 2006 to 2018. Our models used two datasets- EPL seasonal team statistics and an augmentation of these team statistics with external (monetary and transfer) data. Our models have a lower F1 score than a standard neural network, however, perform better in identifying draws and seasonal teams rankings. Transfer Learning models performed better on match-wise rankings while for seasonal rankings, augmented data provides the best predictor (0.72 average). Our proposed Tally Rank provides more accurate seasonal rankings than a graph-based PageRank. Since there is no consistently best-performing model, other Similarity Learning/Ranking models can be considered in the future.
UR - http://www.scopus.com/inward/record.url?scp=85092689803&partnerID=8YFLogxK
U2 - 10.23919/FUSION45008.2020.9190564
DO - 10.23919/FUSION45008.2020.9190564
M3 - Conference contribution
AN - SCOPUS:85092689803
T3 - Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020
BT - Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd International Conference on Information Fusion, FUSION 2020
Y2 - 6 July 2020 through 9 July 2020
ER -