{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T03:47:08Z","timestamp":1776311228148,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>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\u2019 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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/make6030102","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T03:03:37Z","timestamp":1726196617000},"page":"2074-2095","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Data Science and Sports Analytics Approach to Decode Clutch Dynamics in the Last Minutes of NBA Games"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8757-8969","authenticated-orcid":false,"given":"Vangelis","family":"Sarlis","sequence":"first","affiliation":[{"name":"School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6371-3137","authenticated-orcid":false,"given":"Dimitrios","family":"Gerakas","sequence":"additional","affiliation":[{"name":"School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8263-9024","authenticated-orcid":false,"given":"Christos","family":"Tjortjis","sequence":"additional","affiliation":[{"name":"School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"ref_1","first-page":"19","article-title":"Sports Analytics\u2014Evaluation of Basketball Players and Team Performance","volume":"19","author":"Sarlis","year":"2020","journal-title":"Inf. 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