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Live Multiattribute Data Mining and Penalty Decision-Making in Basketball Games Based on the Apriori Algorithm.

Jian Zeng1, Bao Jia2

  • 1Mianyang Teacher's College, Sichuan, China.

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Summary

Data mining reveals key basketball actions and player performance metrics influencing game outcomes. This analysis provides valuable insights for training, strategy, and predicting team success.

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Area of Science:

  • Sports Analytics
  • Data Mining
  • Basketball Performance Analysis

Background:

  • Basketball games generate vast amounts of multi-attribute data.
  • Understanding the relationships between technical actions, player performance, and game outcomes is crucial for strategic improvement.

Purpose of the Study:

  • To apply data mining techniques to analyze basketball game data.
  • To identify key technical and tactical actions, scoring/loss patterns, and factors affecting winning.
  • To assess the influence of player performance on game results.

Main Methods:

  • Improved Apriori algorithm for association rule analysis of technical actions.
  • Markov process-based data mining for identifying key scoring and conceding actions.
  • Logistic regression and decision tree algorithms for feature selection and performance prediction.
  • Association rule algorithm to determine player performance impact on game outcomes.

Main Results:

  • The improved Apriori algorithm effectively mines frequent technical actions and their associations.
  • Key scoring and conceding actions were identified, offering practical guidance for training and games.
  • Significant features affecting team winning were determined, enabling performance prediction.
  • Player performance was shown to influence game outcomes through association rule analysis.

Conclusions:

  • Data mining provides valuable, instructive insights into basketball game dynamics.
  • The identified patterns and influential factors can guide strategic decision-making and training regimens.
  • This analytical approach has high practical value for improving team performance and predicting outcomes.