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This study introduces new methods for basketball spatial performance analysis, creating scoring probability maps using advanced algorithms like Random Forest. These tools enhance player and team evaluation with robust, interpretable visualizations.

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Area of Science:

  • Sports Analytics
  • Algorithmic Modeling
  • Basketball Performance Analysis

Background:

  • Traditional basketball analytics often lack detailed spatial insights.
  • Existing methods may not fully capture the nuances of court geometry and player movement.
  • There is a need for advanced tools to visualize and interpret scoring probabilities.

Purpose of the Study:

  • To develop novel algorithmic tools for spatial performance analysis in basketball.
  • To create visualizations of court areas indicating scoring probabilities for players or teams.
  • To enhance the interpretability and robustness of basketball analytics models.

Main Methods:

  • Examination of Classification and Regression Trees (CART) and their limitations.
  • Proposal and application of polar coordinates for improved court geometry representation.
  • Implementation of CART-based ensemble learning algorithms: Random Forest and Extremely Randomized Trees.
  • Definition of a graphical goodness index for algorithm tuning.

Main Results:

  • Random Forest and Extremely Randomized Trees demonstrate excellent interpretability and robustness in spatial analysis.
  • Polar coordinates offer better alignment with basketball court geometry compared to traditional methods.
  • The proposed graphical goodness index aids in parameter tuning for enhanced model performance.
  • Successful application of the methods to NBA 2020/2021 regular season data.

Conclusions:

  • Algorithmic modeling, particularly ensemble methods, significantly advances spatial performance analysis in basketball.
  • The proposed polar coordinate system and ensemble techniques provide a robust framework for scoring probability mapping.
  • These tools offer valuable insights for player evaluation, strategy development, and performance optimization in basketball.