IEEE Style Citation: Sourabh Swain, Shriya Mishra, “Prolego: A Data Science Approach to Predict the Outcome of a Football Match,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.132-136, 2018.
MLA Style Citation: Sourabh Swain, Shriya Mishra "Prolego: A Data Science Approach to Predict the Outcome of a Football Match." International Journal of Computer Sciences and Engineering 6.4 (2018): 132-136.
APA Style Citation: Sourabh Swain, Shriya Mishra, (2018). Prolego: A Data Science Approach to Predict the Outcome of a Football Match. International Journal of Computer Sciences and Engineering, 6(4), 132-136.
BibTex Style Citation:
@article{Swain_2018,
author = {Sourabh Swain, Shriya Mishra},
title = {Prolego: A Data Science Approach to Predict the Outcome of a Football Match},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2018},
volume = {6},
Issue = {4},
month = {4},
year = {2018},
issn = {2347-2693},
pages = {132-136},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1857},
doi = {https://doi.org/10.26438/ijcse/v6i4.132136}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i4.132136}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1857
TI - Prolego: A Data Science Approach to Predict the Outcome of a Football Match
T2 - International Journal of Computer Sciences and Engineering
AU - Sourabh Swain, Shriya Mishra
PY - 2018
DA - 2018/04/30
PB - IJCSE, Indore, INDIA
SP - 132-136
IS - 4
VL - 6
SN - 2347-2693
ER -
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Abstract
Prolego aims to predict results of Premier League football matches accurately by applying machine learning techniques to historical data. The historical data consists of rows where each row consists of several statistics for both the Home Team and the Away Team. The historical data is generated using web scraping libraries such as Selenium and BeautifulSoup. Based on the scraped data, data cleaning and feature engineering is done to generate several features of a football match like Shots, Shots On Target, Possession, Tackles, Corners, Ratting etc. Finally, the features are represented in a vector format and fed as inputs to different Machine Learning classifier algorithms like Multinomial Logistic Regression, SVM, Gradient Boosting Classifier and DecisionTreeClassifier. After the classification, accuracy is measured by calculating percentage of correct predictions and percentage of correct draw predictions. Error analysis is performed using techniques like Region under Curve to tune hyperparameters and identify the features which are more prominent/useful in accurately predicting the results.
Key-Words / Index Term
Prolego, Dataset, Collection
References
[1]. Ben Ulmer and Matthew Fernandez, Predicting Soccer Match Results in the English Premier League. (http://cs229.stanford.edu/proj2014/Ben%20Ulmer, %20Matt%20Fernandez,%20Predicting%20Soccer% 20Results% 20in%20the%20English%20Premier%20League.pdf)
[2]. A. S. Timmaraju, A. Palnitkar,& V. Khanna, Game ON! Predicting English Premier League Match Outcomes, 2013. (http://cs229.stanford.edu/proj2013/TimmarajuPalnit karKhanna-GameON!PredictionOfEPLMatchOutcomes.pdf)
[3]. Kaggle March Machine Learning Mania https://www.kaggle.com/c/march- machine-learning-mania-2017
[4]. Adit Deshpande, Applying Machine Learning to March Madness - Applying Machine Learning To March Madness (https://adeshpande3.github.io/Applying-Machine-Learning-to-March-Madness)
[5]. Premier League website - https://www.premierleague.com/
[6]. EA Sports FIFA Rating - https://www. faindex.com
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