Research Article
Statistical Analysis of IPL Player Performance Using Advanced Computational Methods
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 186 - Issue 30 |
| Published: July 2024 |
| Authors: Nirat J. Patel |
10.5120/ijca2024923882
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Nirat J. Patel . Statistical Analysis of IPL Player Performance Using Advanced Computational Methods. International Journal of Computer Applications. 186, 30 (July 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 },
year = { 2024 },
volume = { 186 },
number = { 30 },
pages = { 48-52 },
doi = { 10.5120/ijca2024923882 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2024
%A Nirat J. Patel
%T Statistical Analysis of IPL Player Performance Using Advanced Computational Methods%T
%J International Journal of Computer Applications
%V 186
%N 30
%P 48-52
%R 10.5120/ijca2024923882
%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
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- KENNEY, J.F. & KEEPING, E.S. (1954). Mathematical Statistics (3rd ed.). New York, NJ: D. van Nostrand.
- IPL matches dataset. https://www.kaggle.com/datasets/patrickb1912/ipl-complete-dataset-20082020?select=matches.csv
- IPL ball-by-ball dataset. https://www.kaggle.com/datasets/patrickb1912/ipl-complete-dataset-20082020?select=deliveries.csv.
- 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
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