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<p>Performance measurement is of paramount importance in the context of sports analytics. A great variety of data analysis methods has been exploited to this aim. All these proposals almost never include resorting to survival analysis techniques, although time-to-event data are suitable for addressing this issue. This work aims to identify the main achievements of a National Basketball Association player that affect the time it takes for him to exceed a given threshold of points. In order to identify nonlinear effects and possible interactions among the predictors, the analysis is carried out with machine learning methods, specifically survival trees and random survival forests.</p>

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Basketball players performance measurement with algorithmic survival data analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月28日

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  • Ambra Macis

摘要

Performance measurement is of paramount importance in the context of sports analytics. A great variety of data analysis methods has been exploited to this aim. All these proposals almost never include resorting to survival analysis techniques, although time-to-event data are suitable for addressing this issue. This work aims to identify the main achievements of a National Basketball Association player that affect the time it takes for him to exceed a given threshold of points. In order to identify nonlinear effects and possible interactions among the predictors, the analysis is carried out with machine learning methods, specifically survival trees and random survival forests.