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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Football Game Prediction Using Machine Learning |
| Authors Name: | Akash Debangshu Panda , Ayush Suresh Tambe , Ankesh Lalchand Janbandhu , Antariksh Sushil Kamble , Prof. Aditi Warange |
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IJNRD_192554
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| Published Paper Id: | IJNRD2304540 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | 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. |
| Keywords: | Dataset, Origin, Features, Data Pre-processing , Analysis and Modelling, Exploratory Analysis, Modelling and Tuning |
| Cite Article: | "Football Game Prediction Using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.f361-f366, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304540.pdf |
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| 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 |
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Published Paper ID:IJNRD2304540 Registration ID: 192554 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: f361-f366 Country: Mumbai, Maharashtra, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304540 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304540 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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