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

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.

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

“…In addition, previous studies [5,9,[17][18][19][20][21][22] collectively addressed various aspects of cricket analytics, encompassing the learning strength and weakness rules of players [17], extracting regions of batsmen through text-commentary analysis [18], machine learningbased team selection [19], and understanding the impact of contextual factors on team performance in T20 cricket through an interpretable machine learning approach [20]. Furthermore, studies have explored the utilization of big data for cricket match outcome prediction [9], sports analytics for cricket games [21], offering a unique approach to ranking cricket teams [23], and conducting a comparative analysis of pitch ratings for all formats of cricket [22].…”

“…In addition, previous studies [5,9,[17][18][19][20][21][22] collectively addressed various aspects of cricket analytics, encompassing the learning strength and weakness rules of players [17], extracting regions of batsmen through text-commentary analysis [18], machine learningbased team selection [19], and understanding the impact of contextual factors on team performance in T20 cricket through an interpretable machine learning approach [20]. Furthermore, studies have explored the utilization of big data for cricket match outcome prediction [9], sports analytics for cricket games [21], offering a unique approach to ranking cricket teams [23], and conducting a comparative analysis of pitch ratings for all formats of cricket [22]. Finally, studies [24][25][26][27], collectively explored strategic decision-making and performance evaluation in cricket, covering optimal playing strategies against specific bowling types using game theory [24], player-aware resource compensation in interrupted matches [25], investigating the influence of the T20 cricket on Test cricket performance and team quality [26], and estimating a batsman's shot selection in T20 cricket to guide strategic decisions related to fielder placement [27].…”

“…Over the past 140 years, cricket has embraced innovation, including technology integration, which benefits coaches and players in game preparation [8]. The increasing popularity of cricket and growing availability of ball-by-ball big data demand more powerful tools and research in cricket analytics [9]. Effective game analysis and planning, including well-planned fielding and bowling strategies, can significantly impact game outcomes by increasing wicket-taking opportunities, restricting runs, and exploiting player dynamics, strengths, and weaknesses, as well as pitch conditions and match contexts [10].…”

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

“…These datasets are inherently complex and multi-modal [ [12] , [13] , [14] ]. Consequently, the accurate identification of patterns and distinctive features within such multidimensional data poses a ubiquitous challenge in the realms of sports and data science [ [15] , [16] , [17] ].…”

Digital Twin in Sport: From an Idea to Realization

“…Besides measuring the load indicators during the implementation of training sessions, these also offer advice on how to train (i.e., training plans), archiving of already implemented training sessions, and miscellaneous analysis of an athlete's progress in training [6]. Artificial Sport Trainer (AST) [7] presents complete help for an athlete in individual sport disciplines in all phases of sport training, where all these phases are covered using computational intelligence (CI) algorithms [8,9].…”