A Novel Approach for Performance Analysis and Optimal Selection of Players in Indian Premier League Auction

Khandelwal, Mayank; Prakash, Jayant; Pradhan, Tribikram

doi:10.1007/978-81-322-2553-9_33

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…In case of IPL, a team has the liberty to retain their previously auctioned players, but it lowers the funds for the owner to enter the auction. A decision tree is made use of to compute the "most valuable player" for a team by player's batting and bowling points and experiences [23]. Prediction of match is calculated through various match statistics using TOPSIS in multiple studies based on World Cup 2019 [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of TOPSIS and TODIM for the Performance Evaluation of Foreign Players in Indian Premier League

et al. 2022

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Sports officials, players, and fans are concerned about overseas player rankings for the IPL auction. These rankings are becoming progressively essential to investors when premium leagues are commercialized. The decision-makers of the Indian Premier League choose cricketers based on their own experience in sports and based on performance statistics on several criteria. This paper presents a scientific way to rank the players. Our research examines and contrasts different multicriteria decision-making algorithms for ranking foreign players under various criteria to assess their performance and efficiency. The paper uses three MCDM algorithms, TOPSIS, TODIM, and NR-TOPSIS, for foreign players ranking in the Indian Premier League. Our analysis is limited to the batsmen and bowlers only. We perform the analysis using Python language, a popular high-level programming language. Finally, we perform a sensitivity analysis to determine the stability of each method when the weights of the criterion or the value of a parameter was changed. Our analysis exhibits the superiority of TODIM over TOPSIS and NR-TOPSIS.

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Section: Literature Reviewmentioning

confidence: 99%