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IPL Player’s Performance Prediction

Nihal Patel1 , Mrudang Pandya2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 478-481, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.478481

Online published on May 31, 2019

Copyright © Nihal Patel, Mrudang Pandya . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Nihal Patel, Mrudang Pandya, “IPL Player’s Performance Prediction,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.478-481, 2019.

MLA Style Citation: Nihal Patel, Mrudang Pandya "IPL Player’s Performance Prediction." International Journal of Computer Sciences and Engineering 7.5 (2019): 478-481.

APA Style Citation: Nihal Patel, Mrudang Pandya, (2019). IPL Player’s Performance Prediction. International Journal of Computer Sciences and Engineering, 7(5), 478-481.

BibTex Style Citation:
@article{Patel_2019,
author = {Nihal Patel, Mrudang Pandya},
title = {IPL Player’s Performance Prediction},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {478-481},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4268},
doi = {https://doi.org/10.26438/ijcse/v7i5.478481}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.478481}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4268
TI - IPL Player’s Performance Prediction
T2 - International Journal of Computer Sciences and Engineering
AU - Nihal Patel, Mrudang Pandya
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 478-481
IS - 5
VL - 7
SN - 2347-2693
ER -

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Abstract

Fantasy cricket league is a rapidly growing industry in India. It has around 70 million users. Lots of people are really making money from it. Player selection is the most important task in the Fantasy League. The performance of a player depends on various factors such as opposition team, venue, his current form and many more. Fantasy league user has to make own team of 11 players from both the team players. In this paper, we are going to predict the performance of a player in IPL matches by analysing previous year’s ball by ball data (2008-2018) using supervised machine learning techniques. Here we classified the batsman’s runs and bowler’s wickets in a different range to pick or not to pick. We used Decision Tree, Random Forest, Xgboost, Stacking for prediction of the players[6]. Stacking technique found the most accurate classifier for the problem.

Key-Words / Index Term

Fantasy League, Machine Learning, Decision Tree, Random Forest, Xgboost, Stacking

References

[1] Fantasy Cricket, https://en.wikipedia.org/wiki/F antasy_cricket
[2] https://www.iplt20.com/teams
[3] Kalpdrum Passi and Niravkumar Pandey, ‘pre- dicting player’s performance in one-day international cricket match using machine learning’, February 2018.
[4] Fantasy cricket league dream11, https://www.dre am11.com/
[5] https://www.analyticsvidhya.com/blog/2018/06/ comprehensive-guide-for-ensemble-models/
[6] https://en.wikipedia.org/wiki/Machine_learning
[7] https://en.wikipedia.org/wiki/Cricket
[8] C. Deep Prakash, C. Patvardhan, C. Vasantha Lakshmi, ‘Data Analytics based Deep Mayo Predictor for IPL-9’, Volume 152 – No.6, October 2016
[9] Debarghya Das, ‘An Integer Optimization Framework for Fantasy Cricket League Selection and Substitution’.
[10] Tim B. Swartz, ‘Research Directions in Cricket’