Football Game Prediction Using Machine Learning

Football Game Prediction Using Machine Learning

Registration ID: IJNRD_192554

Published ID: IJNRD2304540

DOI: Click Here to Get

Akash Debangshu Panda , Ayush Suresh Tambe , Ankesh Lalchand Janbandhu , Antariksh Sushil Kamble , Prof. Aditi Warange

Dataset, Origin, Features, Data Pre-processing , Analysis and Modelling, Exploratory Analysis, Modelling and Tuning

Football game prediction is a challenging task that has gained increasing attention in recent years due to its potential applications in sports betting, fantasy football, and other related fields. In this paper, we present a comparative study of several machine learning algorithms for predicting the outcome of football matches. We compare the performance of logistic regression, decision trees, random forests, and gradient boosting on a dataset of historical football matches. We pre-process the dataset to extract relevant features such as team rankings, player statistics, and match location. We then train the models using different sets of features and evaluate their performance using metrics such as accuracy, precision, recall, and F1 score. Our results show that gradient boosting outperforms the other models with an accuracy of 65%, a precision of 63%, a recall of 66%, and an F1 score of 64%. Logistic regression and random forests also perform well with accuracies of 63% and 61%, respectively. We also investigate the impact of different feature subsets on the model performance and find that team rankings and recent performance are the most important features for predicting football match outcomes. Finally, we discuss the limitations of our study and suggest future research directions in this area. We formulated this study as a classification framework in the machine learning (ML) context to distinguish the winning team from the losing team in a match. This allowed us to check the effectiveness of different performance metrics considered a feature vector for ML models. Different ML models were considered for this classification task, and the logistic regression-based model was considered the best performing model, with more than 80% accuracy. Multiple feature selection methods were leveraged to identify players’ performance metrics that could be considered as contributing factors to determine the match result.

Published Paper   E-Certificate

"Football Game Prediction Using Machine Learning", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 4, page no.f361-f366, April-2023, Available :https://ijnrd.org/papers/IJNRD2304540.pdf

Volume 8 Issue 4, April-2023

Pages : f361-f366

Paper Reg. ID: IJNRD_192554

Published Paper Id: IJNRD2304540

Downloads: 000121167

Research Area: Computer Engineering 

Country: Mumbai, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304540.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304540

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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