Comparative study of football team rating system using elo rating and pi-rating for Switzerland Super League

A sports rating system is a system that analyses the results of sports competitions to provide ratings for each team or player. Usually, in a football match, the audience will predict which team will win based on the goals scored by the team at half-time or penalty. This prediction is importan...

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Main Authors: Sukarno, Siti Nurzulaika, Saringat, Mohd Zainuri, Mustapha, Aida, Razali, Nazim
Format: Other
Language:English
Published: Penerbit UTHM 2021
Subjects:
Online Access:http://eprints.uthm.edu.my/6722/1/P13642_742b0c6f792ad27b6d54c7d34f130ec7.pdf
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author Sukarno, Siti Nurzulaika
Saringat, Mohd Zainuri
Mustapha, Aida
Razali, Nazim
author_facet Sukarno, Siti Nurzulaika
Saringat, Mohd Zainuri
Mustapha, Aida
Razali, Nazim
author_sort Sukarno, Siti Nurzulaika
collection UTHM
description A sports rating system is a system that analyses the results of sports competitions to provide ratings for each team or player. Usually, in a football match, the audience will predict which team will win based on the goals scored by the team at half-time or penalty. This prediction is important because when evaluating match results, it is important to first compare the potential strength of the teams involved in the match. Due to this, the main goal of this research is to compare the performance of the team rating system using Elo Rating and Pi Rating when forecasting match outcomes in association football. The well-known Elo Rating system is used to calculate team ratings, whereas a Pi Rating is used to predict the football match results based on a team’s performance to win the match when playing home or when playing away. Two different techniques are used to generate forecasts. Both types of models can be used to generate pre-game forecasts. The Elo ratings worked better when predicting matches from a large data set. The Pi Rating system applies to any other sport where the score is considered as a good indicator for prediction purposes, as well as determining the relative performances between adversaries. Data used in this study focuses on the dataset football match by Switzerland Super League. The dataset from the Football-Data.co.uk website is a dataset composed of around 1421 data of matches of the Switzerland Super League. The research figures out the classification model based on the Decision Forest classifier is an effective classifier with 68% f-measure for Pi Rating and 73% for f�measure Elo Rating. Therefore, Elo Rating is the best team rating system to predict football competitions.
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spelling uthm.eprints-67222022-03-15T02:32:16Z http://eprints.uthm.edu.my/6722/ Comparative study of football team rating system using elo rating and pi-rating for Switzerland Super League Sukarno, Siti Nurzulaika Saringat, Mohd Zainuri Mustapha, Aida Razali, Nazim T Technology (General) A sports rating system is a system that analyses the results of sports competitions to provide ratings for each team or player. Usually, in a football match, the audience will predict which team will win based on the goals scored by the team at half-time or penalty. This prediction is important because when evaluating match results, it is important to first compare the potential strength of the teams involved in the match. Due to this, the main goal of this research is to compare the performance of the team rating system using Elo Rating and Pi Rating when forecasting match outcomes in association football. The well-known Elo Rating system is used to calculate team ratings, whereas a Pi Rating is used to predict the football match results based on a team’s performance to win the match when playing home or when playing away. Two different techniques are used to generate forecasts. Both types of models can be used to generate pre-game forecasts. The Elo ratings worked better when predicting matches from a large data set. The Pi Rating system applies to any other sport where the score is considered as a good indicator for prediction purposes, as well as determining the relative performances between adversaries. Data used in this study focuses on the dataset football match by Switzerland Super League. The dataset from the Football-Data.co.uk website is a dataset composed of around 1421 data of matches of the Switzerland Super League. The research figures out the classification model based on the Decision Forest classifier is an effective classifier with 68% f-measure for Pi Rating and 73% for f�measure Elo Rating. Therefore, Elo Rating is the best team rating system to predict football competitions. Penerbit UTHM 2021 Other NonPeerReviewed text en http://eprints.uthm.edu.my/6722/1/P13642_742b0c6f792ad27b6d54c7d34f130ec7.pdf Sukarno, Siti Nurzulaika and Saringat, Mohd Zainuri and Mustapha, Aida and Razali, Nazim (2021) Comparative study of football team rating system using elo rating and pi-rating for Switzerland Super League. Penerbit UTHM, UTHM. https://doi.org/10.30880/aitcs.0000.00.00.000
spellingShingle T Technology (General)
Sukarno, Siti Nurzulaika
Saringat, Mohd Zainuri
Mustapha, Aida
Razali, Nazim
Comparative study of football team rating system using elo rating and pi-rating for Switzerland Super League
title Comparative study of football team rating system using elo rating and pi-rating for Switzerland Super League
title_full Comparative study of football team rating system using elo rating and pi-rating for Switzerland Super League
title_fullStr Comparative study of football team rating system using elo rating and pi-rating for Switzerland Super League
title_full_unstemmed Comparative study of football team rating system using elo rating and pi-rating for Switzerland Super League
title_short Comparative study of football team rating system using elo rating and pi-rating for Switzerland Super League
title_sort comparative study of football team rating system using elo rating and pi rating for switzerland super league
topic T Technology (General)
url http://eprints.uthm.edu.my/6722/1/P13642_742b0c6f792ad27b6d54c7d34f130ec7.pdf
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AT saringatmohdzainuri comparativestudyoffootballteamratingsystemusingeloratingandpiratingforswitzerlandsuperleague
AT mustaphaaida comparativestudyoffootballteamratingsystemusingeloratingandpiratingforswitzerlandsuperleague
AT razalinazim comparativestudyoffootballteamratingsystemusingeloratingandpiratingforswitzerlandsuperleague