Deep similarity learning for soccer team ranking

Habeebullah Manack, Terence L. Van Zyl

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780578647098
DOIs
Publication statusPublished - Jul 2020
Externally publishedYes
Event23rd International Conference on Information Fusion, FUSION 2020 - Virtual, Pretoria, South Africa
Duration: 6 Jul 20209 Jul 2020

Publication series

NameProceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020

Conference

Conference23rd International Conference on Information Fusion, FUSION 2020
Country/TerritorySouth Africa
CityVirtual, Pretoria
Period6/07/209/07/20

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management
  • Instrumentation

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