Using Weather Conditions and Advanced Machine Learning Methods to Predict Soccer Outcome. Denny Asarias Palinggi, Francisco Ramos, Joaquín Torres-Sospedra, S. T. & Huerta, J. In Kyriakidis, P., Hadjimitsis, D., & Skarlatos, D. & M., editors, Accepted Short Papers and Posters from the 22nd AGILE Conference on Geo-information Science. Cyprus University of Technology 17-20 June 2019, Limassol, Cyprus, Limassol, Cyprus, 2019. Stichting AGILE. ![Using Weather Conditions and Advanced Machine Learning Methods to Predict Soccer Outcome. [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex Massive amounts of research have been doing on predicting soccer matches using machine learning algorithms. Unfortunately, there are no prior researches used weather condition as features. In this work, three different classification algorithms were investigated for predicting the outcomes of soccer matches by using temperature difference and several other historical match statistics as features. More concretely, the dataset consists of statistic information of soccer matches in La Liga and Segunda division from season 2013-2014 to 2016-2017 and meteorological data in every host city. The results show that the Support Vector Machine model has better accuracy score compare to K-Nearest Neighbours and Random Forest with 45.32% for temperature difference below 5° and 49.51% for temperature difference above 5°. Our test results have shown that weather information can be important factors to improve the prediction accuracy of soccer matches outcome
@inproceedings{DennyAsariasPalinggiFranciscoRamosJoaquinTorres-Sospedra2019,
abstract = {Massive amounts of research have been doing on predicting soccer matches using machine learning algorithms. Unfortunately, there are no prior researches used weather condition as features. In this work, three different classification algorithms were investigated for predicting the outcomes of soccer matches by using temperature difference and several other historical match statistics as features. More concretely, the dataset consists of statistic information of soccer matches in La Liga and Segunda division from season 2013-2014 to 2016-2017 and meteorological data in every host city. The results show that the Support Vector Machine model has better accuracy score compare to K-Nearest Neighbours and Random Forest with 45.32% for temperature difference below 5° and 49.51% for temperature difference above 5°. Our test results have shown that weather information can be important factors to improve the prediction accuracy of soccer matches outcome},
address = {Limassol, Cyprus},
author = {{Denny Asarias Palinggi, Francisco Ramos, Joaqu{\'{i}}n Torres-Sospedra}, Sergio Trilles and Joaqu{\'{i}}n Huerta},
booktitle = {Accepted Short Papers and Posters from the 22nd AGILE Conference on Geo-information Science. Cyprus University of Technology 17-20 June 2019, Limassol, Cyprus},
editor = {{Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. & Mansourian}, A.},
keywords = {Conference,conference},
mendeley-tags = {conference},
publisher = {Stichting AGILE},
title = {{Using Weather Conditions and Advanced Machine Learning Methods to Predict Soccer Outcome.}},
url = {https://agile-online.org/images/conference_2019/documents/posters/65_Upload_your_PDF_file.pdf},
year = {2019}
}
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The results show that the Support Vector Machine model has better accuracy score compare to K-Nearest Neighbours and Random Forest with 45.32% for temperature difference below 5° and 49.51% for temperature difference above 5°. Our test results have shown that weather information can be important factors to improve the prediction accuracy of soccer matches outcome","address":"Limassol, Cyprus","author":[{"propositions":[],"lastnames":["Denny Asarias Palinggi, Francisco Ramos, Joaquín Torres-Sospedra"],"firstnames":["Sergio","Trilles"],"suffixes":[]},{"firstnames":["Joaquín"],"propositions":[],"lastnames":["Huerta"],"suffixes":[]}],"booktitle":"Accepted Short Papers and Posters from the 22nd AGILE Conference on Geo-information Science. 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In this work, three different classification algorithms were investigated for predicting the outcomes of soccer matches by using temperature difference and several other historical match statistics as features. More concretely, the dataset consists of statistic information of soccer matches in La Liga and Segunda division from season 2013-2014 to 2016-2017 and meteorological data in every host city. The results show that the Support Vector Machine model has better accuracy score compare to K-Nearest Neighbours and Random Forest with 45.32% for temperature difference below 5° and 49.51% for temperature difference above 5°. 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