Machine Learning Approaches for Predicting Player Position in Football

Title

Machine Learning Approaches for Predicting Player Position in Football

Creator

Tyagi, Tamanna; Naveen, J.; Rao, B Satyam; Dhabuwala, Prince

Description

This paper presents a comparative study of different machine learning algorithms, including K- Nearest Neighbor (KNN), Random Forest, Gradient Boosting, XG Boost, Support Vector Machine, Voting Classifier and Logistic regression to develop a Player Position Prediction System in football. Initially, the study utilized a modified dataset containing 18434 records, focusing on simplicity for ease of analysis. Through experimentation, it was found that Logistic regression provided a strong balance between efficiency and scalability, making them ideal for rapid decision-making in environments with limited features. In contrast, Support Vector Machine, XGboost and voting classifier excelled in offering more detailed, feature-rich analyses, which are particularly beneficial when handling complex data. Building on these findings, the plan is to apply the same algorithms to improve the system's overall accuracy and efficiency. By leveraging the strengths of each approach, the aim is to create a scalable, effective recommendation system tailored for real-world applications in the car industry. This study highlights the importance of choosing the right algorithm based on the tradeoffs between computational efficiency and the depth of analysis required in recommendation systems. 2025 IEEE.

Source

Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Gradient Boosting; KNN; Logistic Regression; Random Forest; Support Vector Machine; Voting Classifier; XG Boost

Coverage

Tyagi T., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India; Naveen J., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India; Rao B.S., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India; Dhabuwala P., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833152118-9;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/InC465408.2025.11256390

https://www.scopus.com/pages/publications/105030339993?origin=resultslist