Optimization of team selection in fantasy cricket: a hybrid approach using recursive feature elimination and genetic algorithm

Jha, Apurva; Kar, Arpan Kumar; Gupta, Akash

doi:10.1007/s10479-022-04726-z

Cited by 2 publications

(2 citation statements)

References 49 publications

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“…This aligns with the findings of Zhao & Zhang (2023) [ 32 ], who introduced a dynamic tunable clustering strategy into a network-aware data access algorithm. Jha et al (2023) [ 29 ] introduced a two-step method for player evaluation and team selection in fantasy cricket, achieving commendable results.…”

Section: Discussionmentioning

confidence: 99%

A clustering mining method for sports behavior characteristics of athletes based on the ant colony optimization

Yang,

Wang,

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

A clustering mining method for sports behavior characteristics of athletes based on the ant colony optimization

Yang,

Wang,

et al. 2024

Heliyon

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“…For defenders, they suggest tailored actions based on pitch location. The study [40] introduced a unique method for choosing fantasy cricket teams. It uses data analysis and a genetic algorithm to select better teams than the traditional methods.…”

Section: Context-aware Strategiesmentioning

confidence: 99%

RunsGuard Framework: Context Aware Cricket Game Strategy for Field Placement and Score Containment

Hussain,

Arshad,

Hassan

2024

Applied Sciences

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Sports analytics utilizes data analysis techniques and computational methods to gain insights, make informed decisions, and facilitate improvements in the performance of individuals and teams. Cricket is one of the most popular games and continues to evolve worldwide. The availability of ball-by-ball data demands in-depth investigation of player strategies, team dynamics, and the impact of contextual variables. Existing studies explored various aspects of cricket analytics, including detecting key events, predicting outcomes, and ranking teams. However, the literature lacks a comprehensive integrated framework that processes unstructured sports commentary, extracts actionable insights, conducts a thorough player analysis, and develops strategic plans while considering contextual factors. This work aims to propose a bowling and fielding strategy to contain a batsman. For this purpose, we developed a comprehensive context-aware framework that collects data, extracts insights from commentary, identifies player strengths and weaknesses, and proposes cricket bowling and fielding strategies according to the given context. To evaluate this work, we implemented a case study that simulated different scenarios, and our framework suggested bowling and fielding strategies. In these simulations, the proposed strategies consistently demonstrated a substantial reduction in the number of runs that were scored. On average, the framework reduces the batsman’s score rate by 33%. These findings underscore the practical effectiveness of research in optimizing field placement and effectively reducing scoring opportunities. Finally, by bridging the gap between data analytics and cricket game strategy, this methodology provides a competitive advantage to coaches, captains, and players. In the future, we aim to involve temporal patterns to understand the evolving behavior of players.

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Optimization of team selection in fantasy cricket: a hybrid approach using recursive feature elimination and genetic algorithm

Cited by 2 publications

References 49 publications

A clustering mining method for sports behavior characteristics of athletes based on the ant colony optimization

A clustering mining method for sports behavior characteristics of athletes based on the ant colony optimization

RunsGuard Framework: Context Aware Cricket Game Strategy for Field Placement and Score Containment

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