A Data Science and Sports Analytics Approach to Decode Clutch Dynamics in the Last Minutes of NBA Games

This research investigates clutch performance in the National Basketball Association (NBA) with a focus on the final minutes of contested games. By employing advanced data science techniques, we aim to identify key factors that enhance winning probabilities during these critical moments. The study introduces the Estimation of Clutch Competency (EoCC) metric, which is a novel formula designed to evaluate players’ impact under pressure. Examining player performance statistics over twenty seasons, this research addresses a significant gap in the literature regarding the quantification of clutch moments and challenges conventional wisdom in basketball analytics. Our findings deal valuable insights into player efficiency during the final minutes and its impact on the probabilities of a positive outcome. The EoCC metric’s validation through comparison with the NBA Clutch Player of the Year voting results demonstrates its effectiveness in identifying top performers in high-pressure situations. Leveraging state-of-the-art data science techniques and algorithms, this study analyzes play data to uncover key factors contributing to a team’s success in pivotal moments. This research not only enhances the theoretical understanding of clutch dynamics but also provides practical insights for coaches, analysts, and the broader sports community. It contributes to more informed decision making in high-stakes basketball environments, advancing the field of sports analytics.

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Institutions

Publication Details

Journal
Machine Learning and Knowledge Extraction
Published
2024-09-13
DOI
https://doi.org/10.3390/make6030102
Citations
25
Primary Topic
Sports Analytics and Performance
Type
article
Field-Weighted Citation Impact
28.23
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article

A Data Science and Sports Analytics Approach to Decode Clutch Dynamics in the Last Minutes of NBA Games

Vangelis Sarlis, Christos Tjortjis, Dimitrios Gerakas
25 citations
Machine Learning and Knowledge Extraction
Sports Analytics and Performance
28.23
article

A Data Science and Sports Analytics Approach to Decode Clutch Dynamics in the Last Minutes of NBA Games

Vangelis Sarlis, Christos Tjortjis, Dimitrios Gerakas
article en
25 citations

Abstract

This research investigates clutch performance in the National Basketball Association (NBA) with a focus on the final minutes of contested games. By employing advanced data science techniques, we aim to identify key factors that enhance winning probabilities during these critical moments. The study introduces the Estimation of Clutch Competency (EoCC) metric, which is a novel formula designed to evaluate players’ impact under pressure. Examining player performance statistics over twenty seasons, this research addresses a significant gap in the literature regarding the quantification of clutch moments and challenges conventional wisdom in basketball analytics. Our findings deal valuable insights into player efficiency during the final minutes and its impact on the probabilities of a positive outcome. The EoCC metric’s validation through comparison with the NBA Clutch Player of the Year voting results demonstrates its effectiveness in identifying top performers in high-pressure situations. Leveraging state-of-the-art data science techniques and algorithms, this study analyzes play data to uncover key factors contributing to a team’s success in pivotal moments. This research not only enhances the theoretical understanding of clutch dynamics but also provides practical insights for coaches, analysts, and the broader sports community. It contributes to more informed decision making in high-stakes basketball environments, advancing the field of sports analytics.

Machine Learning and Knowledge ExtractionVol. 6(3)
International Hellenic University (GR)
Peace, Justice and strong institutions
Openalex Percentile: Top 1%
Sports Analytics and Performance
28.23
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