Classification of Football Players' Abilities Using The Naïve Baiyes Method

This study aims to classify the abilities of football players in the BRI Liga 1 Indonesia season 2023/2024 using the Naïve Bayes method. The player data used includes individual stats such as the number of goals, assists, passing accuracy, tackles, and overall performance in the match. The Naïve Bayes method was chosen because of its efficient ability to process data with independent features. In this study, players were classified into several ability categories, such as excellent, good, adequate, and poor, based on their performance during the current season. The results of this classification are expected to provide useful information for coaches and club management in determining strategies and player development. The study also provides insight into the key factors that affect a player's performance in the league. Model testing shows that Naïve Bayes' method has an adequate level of accuracy for the classification of football players' abilities in BRI Liga 1. The practical implications of this study are increased efficiency in the process of evaluating players and making strategic decisions in professional football teams.

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Publication Details

Journal
Indonesian Journal of Artificial Intelligence and Data Mining
Published
2024-08-10
DOI
https://doi.org/10.24014/ijaidm.v7i2.31997
Primary Topic
Sports Analytics and Performance
Type
article
Field-Weighted Citation Impact
0.00
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article

Classification of Football Players' Abilities Using The Naïve Baiyes Method

Riski Abdul Hakim Nasution, Muhammad Ikhsan
Indonesian Journal of Artificial Intelligence and Data Mining
Sports Analytics and Performance
article

Classification of Football Players' Abilities Using The Naïve Baiyes Method

Riski Abdul Hakim Nasution, Muhammad Ikhsan
article en

Abstract

This study aims to classify the abilities of football players in the BRI Liga 1 Indonesia season 2023/2024 using the Naïve Bayes method. The player data used includes individual stats such as the number of goals, assists, passing accuracy, tackles, and overall performance in the match. The Naïve Bayes method was chosen because of its efficient ability to process data with independent features. In this study, players were classified into several ability categories, such as excellent, good, adequate, and poor, based on their performance during the current season. The results of this classification are expected to provide useful information for coaches and club management in determining strategies and player development. The study also provides insight into the key factors that affect a player's performance in the league. Model testing shows that Naïve Bayes' method has an adequate level of accuracy for the classification of football players' abilities in BRI Liga 1. The practical implications of this study are increased efficiency in the process of evaluating players and making strategic decisions in professional football teams.

Indonesian Journal of Artificial Intelligence and Data MiningVol. 7(2)
Islamic University (BD), Universitas Islam Negeri Sumatera Utara (ID)
Openalex Percentile: Top 81%
Sports Analytics and Performance
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