ArticlePloS one2019
Players' selection for basketball teams, through Performance Index Rating, using multiobjective evolutionary algorithms.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Decision support system based on AHP and PROMETHEE under rough pythagorean fuzzy set information for selection of basketball team.Scientific reports · 2025Article
- Differences in Physical Demands and Player's Individual Performance Between Winning and Losing Quarters on U-18 Basketball Players During Competition.Journal of functional morphology and kinesiology · 2024Article
- Physical Fitness as a Predictor of Performance during Competition in Professional Women's Basketball Players.International journal of environmental research and public health · 2023Article
- Some metaheuristic algorithms for solving multiple cross-functional team selection problems.PeerJ. Computer science · 2022Article
- The balancing act: Identifying multivariate sports performance using Pareto frontiers.Frontiers in sports and active living · 2022Article
- In search of diverse and connected teams: A computational approach to assemble diverse teams based on members' social networks.PloS one · 2022Article
- Performance Differences in Male Youth Basketball Players According to Selection Status and Playing Position: An Evaluation of the Basketball Learning and Performance Assessment Instrument.Frontiers in psychology · 2022Article
- Citius, Altius, Fortius; Is It Enough to Achieve Success in Basketball?International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In any sport the selection of players for a team is fundamental for its subsequent performance. Many factors condition the selection process from the characteristics of the sport discipline to financial limitations, including a long list of restrictions associated with the environment of the competitions in which the team takes part. All of this makes the process of selecting a roster of players very complex, as it is affected by multiple variables and in many cases marked by a great deal of subjectivity. The purpose of this article was to objectively select the players for a basketball team using an evolutionary algorithm, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) that uses stochastic search methods based on the imitation of natural biological evolution. The sample was composed of the players from the teams competing in the top Spanish basketball league, the Association of Basketball Clubs (ACB). To assess the quality of the solutions obtained, the results were compared with the teams in the ACB playing in the same competition as the players used in the study. The results make it possible to obtain different solutions for composing teams rendering financial resources profitable and taking into account the restrictions of the competition and of each sport management.
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