Using Machine Learning to Analyse Football Teams and Predict the Outcome of a Football Match

Open Access

Year : 2022 | Volume : | Issue : 1 | Page : 30-40
By

    Adish Golechha

  1. Akshat Muke

  1. Student, Department of Electronics and Communication, Nirma University, Gujarat, India
  2. Student, Department of Electronics and Communication, Nirma University, Gujarat, India

Abstract

Football, as one of the most popular sports on the planet, has always attracted a large number of fans. Over 150 million men and women of all ages play it in over 200 countries. Modern football has seen a paradigm shift from being just one of the most physical sports to now being one of the most complex sports due to the involvement of multiple new factors such as Home games, away games, form of an individual, total shots taken, total shots blocked, weather conditions etc. In recent years, the importance of these statistics in football and its usage has been seen by all. In this paper, football statistics have been used to predict the outcome of football matches via various machine learning algorithms as well as by analyzing them. A thorough examination of these statistics enables a manager to devise strategies for team management as well as upcoming matches. To calculate accuracy, three Machine Learning algorithms were used: Logistic Regression, Support Vector Machine, and Multinomial Nave Bayes. All three of them were compared at it was observed that the most efficient accuracy was that of SVM which was (61.29%). It was also noted that the accuracy achieved by Logistic Regression (60.9%) was very close to that of SVM

Keywords: Football Match, Machine Learning Algorithms, Support Vector Machine, Logistic Regression, Multinomial Naive Bayes

[This article belongs to Journal of Communication Engineering & Systems(joces)]

How to cite this article: Adish Golechha, Akshat Muke Using Machine Learning to Analyse Football Teams and Predict the Outcome of a Football Match joces 2022; 12:30-40
How to cite this URL: Adish Golechha, Akshat Muke Using Machine Learning to Analyse Football Teams and Predict the Outcome of a Football Match joces 2022 {cited 2022 May 20};12:30-40. Available from: https://journals.stmjournals.com/joces/article=2022/view=90291

Full Text PDF Download

Browse Figures

References

1. Klaus Rosmanitz, European Football-Soccer, English Online AT, Accessed on: April 19, 2022. [Online]. Available: https://www.english-online.at/sports/soccer/european-football.html.
2. Frank Keogh, Football betting-the global gambling industry worth billions, BBC, 3rd October,2013, Accessed on: April 19, 2022. [Online]. Available: https://www.bbc.com/sport/football/24354124
3. Hugo Mathien, European Soccer Database, Kaggle, October 2016, Accessed on: April 19, 2022. [Online]. Available: https://www.kaggle.com/datasets/hugomathien/soccer.
4. Football Data Co. UK, Historical Data in England Football, Accessed on: April 19, 2022. [Online].Available: https://www.football-data.co.uk/
5. Sci-kit Learn, Support Vector Machines, Accessed on: April 19, 2022. [Online]. Available:http://scikit-learn.org/stable/modules/svm.html
6. Statistics Solutions, What isLogisticRegression? Accessed on: April 19, 2022.[Online].Avail- able: http://www.statisticssolutions.com/what-is-logistic-regression/
7. Sci-kit Learn, Naive Bayes, Accessed on: April 19, 2022. [Online]. Available: http://scikit-learn.org/stable/modules/naive-bayes.html
8. Stylianos Kampakis, Andreas Adamides, Using Twitter to predict foot- ball outcomes, Accessed on: April 19, 2022. [Online]. Available: https://arxiv.org/ftp/arxiv/papers/1411/1411.1243.pdf
9. Matheus Kempa, Machine Learning Algorithms for Football Predictions, Medium, Accessed on:April 19, 2022. [Online]. Available: https://towardsdatascience.com/machine-learning- algorithms-for-football-prediction-using-statistics-from-brazilian-championship-51b7d4ea0bc8 [10]Md. Ashiqur Rahman, A deep learning framework for football match pre-diction, SpringerLink, Accessed on: April 19, 2022. [Online]. Available:
10. https://link.springer.com/article/10.1007/s42452-019–1821–5
11. Mark Lawrenson, How do handicaps predict outcomes?, Pinnacle, September 12 2013, Accessed on: April 19, 2022. [Online]. Available: https://www.pinnacle.com/en/betting- articles/Soccer/Mark-Lawrenson-vs-Pinnacle-Sports/VGJ296E4BSYNURUB
12. Siraj Raval, Predicting Winning Teams, GitHub, Accessed on: April 19, 2022. [Online]. Available: https://github.com/llSourcell/Predicting-Winning- Teams/blob/master/Prediction.ipynb
13. Yoni Lev, The Most Predictable League, Kaggle, Accessed on: April 19, 2022. [Online]. Avail- able: https://www.kaggle.com/code/yonilev/the-most-predictable-league/notebook
14. N. Razali, A. Mustapha, F. M. Clemente, M. F. Ahmad, M. A. Salamat. Pattern Analysis of Goals Scored in Malaysia Super League 2015, Indonesian Journal of Electrical Engineering and Computer Science, vol. 11, no. 2, pp. 718–724, 2018.
15. B. Min, J. Kim, C. Choe, H. Eom, R. B. McKay. A compound framework for sports results prediction: A football case study, Knowledge-Based Systems,vol. 21, no. 7,pp.551–562, 2008.
16. C. Anthony, N. Fenton, and M. Neil. Pi-football: A Bayesian network model for forecasting Association Football match outcomes, Knowledge-Based Systems,vol. 36, pp. 322-339, 2012.


Regular Issue Open Access Article
Volume 12
Issue 1
Received April 29, 2022
Accepted May 16, 2022
Published May 20, 2022