Player Stats Analysis Using Machine Learning

Authors : Sylvester Anthony A; Dr. S.L. Jayalakshmi; Akash D

Volume/Issue : Volume 6 - 2021, Issue 2 - February

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A significant area that needs critical thinking to ensure a team performs well is the strategizing of a specific team. The secret to overcoming this dilemma is to use the talent of the players inside the team that can be disregarded at times. With ever growing rivalry, a talented team, with an old and obsolete plan, could have to face undesirable and bad outcomes. In this article, we have performed an experimental analysis in the field of outdoor sports for soccer. The approach considered in the current paper work focuses on deciding the lineup of a squad by measuring the abilities of the soccer players. To collect the data set in the proposed method, we created our own web scraping algorithm. To predict the best location of a player, machine learning classifiers such as Neural Network (Multilayer Perceptron), Random Forests, KNN, Naïve Bayes and Logistic Regression are used. Using various ultra-modern classifiers, the precision of the method proposed was evaluated.

Keywords : KNN, Naïve Bayes, ANN, Random Forests, Logistic Regression, Machine Learning, Strategy Management.


Paper Submission Last Date
31 - August - 2022

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