<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This review explores the avenues for the application of meta-heuristics in sports. The necessity of sophisticated algorithms to investigate different NP hard problems encountered in sports analytics was established in the recent past. Meta-heuristics have been applied as a promising approach to such problems. We identified team selection, optimal lineups, sports equipment optimization, scheduling and ranking, performance analysis, predictions in sports, and player tracking as seven major categories where meta-heuristics were implemented in research in sports. Some of our findings include (a) genetic algorithm and particle swarm optimization have been extensively used in the literature, (b) meta-heuristics have been widely applied in the sports of cricket and soccer, (c) the limitations and challenges of using meta-heuristics in sports. Through awareness and discussion on implementation of meta-heuristics, sports analytics research can be rich in the future.</p>
</abstract>

This review explores the avenues for the application of meta-heuristics in sports. The necessity of sophisticated algorithms to investigate different NP hard problems encountered in sports analytics was established in the recent past. Meta-heuristics have been applied as a promising approach to such problems. We identified team selection, optimal lineups, sports equipment optimization, scheduling and ranking, performance analysis, predictions in sports, and player tracking as seven major categories where meta-heuristics were implemented in research in sports. Some of our findings include (a) genetic algorithm and particle swarm optimization have been extensively used in the literature, (b) meta-heuristics have been widely applied in the sports of cricket and soccer, (c) the limitations and challenges of using meta-heuristics in sports. Through awareness and discussion on implementation of meta-heuristics, sports analytics research can be rich in the future.

Meta-heuristics meet sports: a systematic review from the viewpoint of nature inspired algorithms

Department of Computer Science, Faculty of Applied Sciences

Department of Statistics, Faculty of Applied Sciences

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This review explores the avenues for the application of meta-heuristics in sports. The necessity of sophisticated algorithms to investigate different NP hard...

Meta-heuristics meet sports: a systematic review from the viewpoint of nature inspired algorithms

Online veröffentlicht: 15. Juni 2022

Seitenbereich: 49 - 92

DOI: https://doi.org/10.2478/ijcss-2022-0003

Schlüsselwörter
SPORTS-ANALYTICS, EVOLUTIONARY COMPUTING, SWARM INTELLIGENCE, SIMULATED ANNEALING, REVIEW

© 2022 M.K.A. Ariyaratne et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Meta-heuristics meet sports: a systematic review from the viewpoint of nature inspired algorithms

Online veröffentlicht: 15. Juni 2022

Seitenbereich: 49 - 92

DOI: https://doi.org/10.2478/ijcss-2022-0003

SchlüsselwörterSPORTS-ANALYTICS, EVOLUTIONARY COMPUTING, SWARM INTELLIGENCE, SIMULATED ANNEALING, REVIEW

© 2022 M.K.A. Ariyaratne et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
SPORTS-ANALYTICS, EVOLUTIONARY COMPUTING, SWARM INTELLIGENCE, SIMULATED ANNEALING, REVIEW