International Journal For Multidisciplinary Research

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Player Winning Probability Model (PWPM): A Review

Author(s) Shashank Gaur, Naveen Kumar Pandey, Archna Pandey, Mohammad Hasrat, Anuj Gupta
Country India
Abstract The Olympic Games are an international event and a source of pride for all countries. With over 200 countries participating in the Olympic Games, it is an international sporting event. The Olympic Games stand as a pinnacle of athletic achievement and global unity, showcasing the finest talents from diverse nations across numerous sporting disciplines. In this review we used machine Learning Algorithms to do prediction on Olympic medal and applying various tools and techniques for Data Analysis using historical data. The process involves Data Collection, Data Cleaning, Data Processing, Exploratory Data Analysis and Medal Prediction. The main objective of this work is to study the complete set of Olympic data to discover pattern and relationship between variables using analysis of data to assess how the Olympic Games changed over time and to do prediction on medals. In this work a PWP model has been proposed in which the Random Forest classifier algorithm has been selected for the classification of player winning probability with accuracy of 70.223325%.
Keywords Player Winning Probability Model (PWPM), RandomForest classification, AdaBoost classification, Overfitting, Bagging classifier
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-22
Cite This Player Winning Probability Model (PWPM): A Review - Shashank Gaur, Naveen Kumar Pandey, Archna Pandey, Mohammad Hasrat, Anuj Gupta - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.18036
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.18036
Short DOI https://doi.org/gtrsst
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