Learning to predict soccer results from relational data with gradient boosted trees

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00321375" target="_blank" >RIV/68407700:21230/19:00321375 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s10994-018-5704-6" target="_blank" >https://doi.org/10.1007/s10994-018-5704-6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10994-018-5704-6" target="_blank" >10.1007/s10994-018-5704-6</a>

Result language
angličtina
Original language name
Learning to predict soccer results from relational data with gradient boosted trees
Original language description
We describe our winning solution to the 2017’s Soccer Prediction Challenge organized in conjunction with the MLJ’s special issue on Machine Learning for Soccer. The goal of the challenge was to predict outcomes of future matches within a selected time-frame from different leagues over the world. A dataset of over 200,000 past match outcomes was provided to the contestants. We experimented with both relational and feature-based methods to learn predictive models from the provided data. We employed relevant latent variables computable from the data, namely so called pi-ratings and also a rating based on the PageRank method. A method based on manually constructed features and the gradient boosted tree algorithm performed best on both the validation set and the challenge test set. We also discuss the validity of the assumption that probability predictions on the three ordinal match outcomes should be monotone, underlying the RPS measure of prediction quality.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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