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Analysis and Prediction of Football Statistics using Data Mining Techniques

by Anurag Gangal, Abhishek Talnikar, Aneesh Dalvi, Vidya Zope, Aadesh Kulkarni
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 132 - Number 5
Year of Publication: 2015
Authors: Anurag Gangal, Abhishek Talnikar, Aneesh Dalvi, Vidya Zope, Aadesh Kulkarni
10.5120/ijca2015907263

Anurag Gangal, Abhishek Talnikar, Aneesh Dalvi, Vidya Zope, Aadesh Kulkarni . Analysis and Prediction of Football Statistics using Data Mining Techniques. International Journal of Computer Applications. 132, 5 ( December 2015), 8-11. DOI=10.5120/ijca2015907263

@article{ 10.5120/ijca2015907263,
author = { Anurag Gangal, Abhishek Talnikar, Aneesh Dalvi, Vidya Zope, Aadesh Kulkarni },
title = { Analysis and Prediction of Football Statistics using Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 132 },
number = { 5 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 8-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume132/number5/23588-2015907263/ },
doi = { 10.5120/ijca2015907263 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:28:19.573973+05:30
%A Anurag Gangal
%A Abhishek Talnikar
%A Aneesh Dalvi
%A Vidya Zope
%A Aadesh Kulkarni
%T Analysis and Prediction of Football Statistics using Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 132
%N 5
%P 8-11
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

To solve the problem of loss of interest in Fantasy Football over the season, a game-changing strategy was thought of which led to the creation of this idea. Powered by an exhaustive dataset of all football statistics from 1992 i.e. the start of the Premier League era, it seemed exciting to allow the use of Data Mining techniques to forecast future statistics. A points system based on the success of predictions (explained later in detail), which in turn allow buying/auctioning better players adds a greater interactive feeling to the existing FPL system. This would prevent the churning of players of the season, since they would be attracted to getting more points and better players through such predictions.

References
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  2. J. Hucaljuk and A. Rakipovic. Predicting football scores using machine learning techniques. In MIPRO, 2011 Proceedings of the 34th International Convention, pages 1623 –1627, may 2011.
  3. A. Joseph, Norman E. Fenton, and Martin Neil. Predicting football results using bayesian nets and other machine learning techniques. Knowledge­Based Systems., 19(7):544–553, 2006.
  4. A. Tsakonas, G. Dounias, S. Shtovba, and V. Vivdyuk. Soft computingbased result prediction of football games. In V. Hrytsyk, editor, The Ist International Conference on Inductive Modelling (ICIM’2002), pages 15–23, Lviv, Ukraine, 20­25 May 2002.
  5. Kou­Yuan Huang and Wen­Lung4 Chang. A neural network method for prediction of 2006 world cup football game. In The 2010 International Joint Conference on Neural Networks (IJCNN), pages 1 –8, july 2010
Index Terms

Computer Science
Information Sciences

Keywords

data-mining sports football prediction statistics analysis fantasy football

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