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Soccer Analytics using Machine Learning

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Abha Tewari, Tushar Parwani, Ajinkya Phanse, Akshay Sharma, Anush Shetty

Abha Tewari, Tushar Parwani, Ajinkya Phanse, Akshay Sharma and Anush Shetty. Soccer Analytics using Machine Learning. International Journal of Computer Applications 181(50):54-56, April 2019. BibTeX

	author = {Abha Tewari and Tushar Parwani and Ajinkya Phanse and Akshay Sharma and Anush Shetty},
	title = {Soccer Analytics using Machine Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2019},
	volume = {181},
	number = {50},
	month = {Apr},
	year = {2019},
	issn = {0975-8887},
	pages = {54-56},
	numpages = {3},
	url = {},
	doi = {10.5120/ijca2019918773},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Sports Analysis is rapidly growing area of sports science with the ever increasing easy internet accessibility and recognition of Machine Learning. This can be a motivating space of analysis for soccer, as soccer is considered way more complicated and dynamic when put next to a couple of different sports. Additionally its the world’s most liked sport, played in over two hundred countries. Many methodologies, approaches and measures are being taken to develop prediction systems.The paper is developed to predict the outcome of the matches in English Premier League(EPL), by studying the trends from the previous matches and identifying the foremost vital attributes that are required to accurately predict the result. XGBOOST, SUPPORT VECTOR MACHINE and LOGISTIC REGRESSION models were taken into consideration and chosen the most effective among them to build the prediction model. This model is applied on real team information and fixture results gathered from for the past few seasons.


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Football, Prediction, Machine Learning, F-SCORE