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Players' selection for basketball teams, through Performance Index Rating, using multiobjective evolutionary

Miguel Ángel Pérez-Toledano1, Francisco J Rodriguez2, Javier García-Rubio3,4

  • 1Quercus Software Engineering Group, University of Extremadura, Cáceres, Spain.

Plos One
|September 5, 2019
PubMed
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This study uses the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to objectively select basketball players, optimizing team composition for financial efficiency and competition constraints.

Area of Science:

  • Sports Science
  • Computational Intelligence
  • Operations Research

Background:

  • Player selection in sports is complex, influenced by numerous variables and often subjective.
  • Effective team composition is crucial for sports performance and financial viability.
  • Existing methods may not fully address the multifaceted constraints of player selection.

Purpose of the Study:

  • To objectively select players for a basketball team using an evolutionary algorithm.
  • To optimize team selection considering financial resources and competition-specific restrictions.
  • To introduce a data-driven approach to a traditionally subjective sports management process.

Main Methods:

  • Application of the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a stochastic search method inspired by natural evolution.

Related Experiment Videos

  • Utilizing player data from the top Spanish basketball league (Association of Basketball Clubs - ACB).
  • Comparative analysis of algorithm-generated team solutions against actual ACB teams.

    • Main Results:

    • The NSGA-II algorithm successfully generated diverse team compositions.
    • Solutions demonstrated efficient utilization of financial resources.
    • Selected teams adhered to competition and sport management constraints.

    Conclusions:

    • Evolutionary algorithms like NSGA-II offer an objective and effective method for player selection in basketball.
    • This approach can lead to financially optimal and competitively viable team structures.
    • The study provides a framework for enhancing decision-making in sports management through computational methods.