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Statistical Analysis of IPL Player Performance using Advanced Computational Methods

by Nirat J. Patel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 30
Year of Publication: 2024
Authors: Nirat J. Patel
10.5120/ijca2024923882

Nirat J. Patel . Statistical Analysis of IPL Player Performance using Advanced Computational Methods. International Journal of Computer Applications. 186, 30 ( Jul 2024), 48-52. DOI=10.5120/ijca2024923882

@article{ 10.5120/ijca2024923882,
author = { Nirat J. Patel },
title = { Statistical Analysis of IPL Player Performance using Advanced Computational Methods },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2024 },
volume = { 186 },
number = { 30 },
month = { Jul },
year = { 2024 },
issn = { 0975-8887 },
pages = { 48-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number30/statistical-analysis-of-ipl-player-performance-using-advanced-computational-methods/ },
doi = { 10.5120/ijca2024923882 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-07-26T23:00:35.496995+05:30
%A Nirat J. Patel
%T Statistical Analysis of IPL Player Performance using Advanced Computational Methods
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 30
%P 48-52
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cricket in India is rapidly expanding, largely due to the Indian Premier League (IPL), which is now valued at nearly $16.4 billion. This growth has led to the emergence of new opportunities in Sports Analytics in Cricket. The IPL is a franchise cricket tournament where teams acquire players through an auction format. With numerous choices available during the auction and a limited budget, it is crucial for franchises to make informed player selections and create well-balanced teams with strong batting, bowling, and fielding capabilities. In this paper, player performance was analyzed using the IPL matches and ball-by-ball dataset from 2008 to 2024, obtained from Kaggle. Batting performance was evaluated using adjusted batting scores and the stochastic dominance method for comparison, while bowling performance was assessed using a measure called Combined Bowling Rate (CBR), which combines three existing statistics. Python libraries such as Numpy for scientific computing, pandas for data analysis, and Matplotlib for visualization of the results were utilized for the analysis.

References
  1. Wood, G. H. (1945). Cricket scores and geometric progression. Journal of Royal Statistical Society,108, 12–22. Series A (Statistics in Society).
  2. Damodaran, U. (2006). Stochastic dominance and analysis of ODI batting performance: The Indian cricket team, 1989–2005. Journal of Sports Science and Medicine, 5, 503–508.
  3. Lemmer, H. H. (2002). The combined bowling rate as a measure of bowling performance in cricket. South African Journal for Research in Sport, Physical Education and Recreation, 24(2), 37–44.
  4. KENNEY, J.F. & KEEPING, E.S. (1954). Mathematical Statistics (3rd ed.). New York, NJ: D. van Nostrand.
  5. IPL matches dataset. https://www.kaggle.com/datasets/patrickb1912/ipl-complete-dataset-20082020?select=matches.csv
  6. IPL ball-by-ball dataset. https://www.kaggle.com/datasets/patrickb1912/ipl-complete-dataset-20082020?select=deliveries.csv.
  7. Fernández Ponce, J. M., Rodríguez Griñolo, M. del R., & Troncoso Molina, M. A. (2022). An Application of Stochastic Dominances in Sports Analytics. Economía Aplicada, 40(1). https://doi.org/10.25115/eea.v40i1.7002
Index Terms

Computer Science
Information Sciences

Keywords

Data Analysis Indian Premier League Player Performance Cricket Analysis

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