A Balanced Squad for Indian Premier League Using Modified NSGA-II

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10251966" target="_blank" >RIV/61989100:27240/22:10251966 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9878123" target="_blank" >https://ieeexplore.ieee.org/document/9878123</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2022.3204649" target="_blank" >10.1109/ACCESS.2022.3204649</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Balanced Squad for Indian Premier League Using Modified NSGA-II

Popis výsledku v původním jazyce

Selecting team players is a crucial and challenging task demanding a considerable amount of thinking and hard work by the selectors. The present study formulated the selection of an IPL squad as a multi-objective optimization problem with the objectives of maximizing the batting and bowling performance of the squad, in which a player&apos;s performance is estimated using an efficient Batting Performance Factor and Combined Bowling Rate. Also, the proposed model tries to formulate a balanced squad by constraining the number of pure batters, pure bowlers, and all-rounders. Bounds are also considered on star players to enhance the performance of the squad and also from the income prospects of IPL. The problem in itself is treated as a 0/1 knapsack problem for which two combinatorial optimization algorithms, namely, BNSGA-II and INSGA-II, are developed. These algorithms were compared with existing modified NSGA-II for IPL team selection and three other popular multi-objective optimization algorithms, NSGA-II, NSDE, and MOPSO-CD, on the basis of standard performance metrics: hypervolume, inverted generational distance, and number of Pareto optimal solutions. Both algorithms performed well, with BNSGA-II performing better than all the other algorithms considered in this study. The IPL 2020 players&apos; data validated the applicability of the proposed model and algorithms. The trade-off squads contained players of each expertise in appropriate proportions. Further analysis of the trade-off squads demonstrated that many theoretically selected players performed well in IPL 2020 matches.

Název v anglickém jazyce

A Balanced Squad for Indian Premier League Using Modified NSGA-II

Popis výsledku anglicky

Selecting team players is a crucial and challenging task demanding a considerable amount of thinking and hard work by the selectors. The present study formulated the selection of an IPL squad as a multi-objective optimization problem with the objectives of maximizing the batting and bowling performance of the squad, in which a player&apos;s performance is estimated using an efficient Batting Performance Factor and Combined Bowling Rate. Also, the proposed model tries to formulate a balanced squad by constraining the number of pure batters, pure bowlers, and all-rounders. Bounds are also considered on star players to enhance the performance of the squad and also from the income prospects of IPL. The problem in itself is treated as a 0/1 knapsack problem for which two combinatorial optimization algorithms, namely, BNSGA-II and INSGA-II, are developed. These algorithms were compared with existing modified NSGA-II for IPL team selection and three other popular multi-objective optimization algorithms, NSGA-II, NSDE, and MOPSO-CD, on the basis of standard performance metrics: hypervolume, inverted generational distance, and number of Pareto optimal solutions. Both algorithms performed well, with BNSGA-II performing better than all the other algorithms considered in this study. The IPL 2020 players&apos; data validated the applicability of the proposed model and algorithms. The trade-off squads contained players of each expertise in appropriate proportions. Further analysis of the trade-off squads demonstrated that many theoretically selected players performed well in IPL 2020 matches.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

10

Číslo periodika v rámci svazku

září 2022

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

100463-100477

Kód UT WoS článku

000861342600001

EID výsledku v databázi Scopus

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