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The Role of Big Data Analytics (BDA) in Sport: Sports Data Mining

Journal of Coaching and Sports Science · Vol. 4 No. 2 (2025) · hlm. 101–117

DOI
10.58524/jcss.v4i2.717
ISSN
2963-1483, 2963-1459
Terbit
2025-10-30
Penerbit
CV. FOUNDAE
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Background: Big Data Analytics (BDA) is urgently needed in Indonesian sports to improve evidence-based decision-making, athlete development, and organizational management. While BDA has transformed global sports through data-driven insights, its application in Indonesia remains limited and fragmented. The absence of integrated analytics between athlete performance and organizational quality management hinders the creation of sustainable, long-term development systems. Aims: This study investigates how BDA can enhance athlete development and organizational management by analyzing multidimensional data from athletes, coaches, referees, and sports infrastructures. It also aims to identify dominant predictors of athlete performance across various sports and to evaluate the quality management practices of the National Sports Committee of Indonesia (NSCI). Methods: A mixed-methods sequential explanatory design was applied. The quantitative phase involved 67 athletes from six sports: football, table tennis, weightlifting, pencak silat, basketball, and karate at the Sport Training Center. Data on anthropometry, fitness, and achievements were analyzed using descriptive statistics, ANOVA, Chi-Square, and regression tests. The qualitative phase involved interviews and observations with 8–12 stakeholders, while organizational quality was assessed using the Wilcoxon Signed Rank test. Result: Results revealed significant performance differences among sports (F = 4.927, p = 0.001). Each sport had unique dominant predictors: VO₂ max and anthropometry (soccer), agility (table tennis), muscle strength (weightlifting), endurance and height (basketball), and speed (karate). NSCI’s organizational analysis showed substantial deficiencies in management, facilities, and procedures (p < 0.001). Conclusion: This study confirms that BDA is crucial in advancing sustainable sports development. By identifying sport-specific performance predictors and systemic weaknesses, BDA provides a scientific foundation for designing targeted training, improving organizational quality, and building adaptive, data-driven sports ecosystems in Indonesia. The findings highlight the urgent need for national sports bodies to institutionalize BDA as part of long-term strategic planning.

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22 penulis · 40 tautan kepenulisan bersama · 8 lintas organisasi

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  • State University of Semarang
  • Yogyakarta State University
  • Universitas Gadjah Mada
  • SeaDataNet AISBL
  • Tanjungpura University
  • Universitas Negeri Surabaya
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Rekan penulisOrganisasiArtikel bersama
Tandiyo RahayuState University of Semarang2
Harry PramonoAfiliasi tidak diketahui1
SemionAfiliasi tidak diketahui1
HadiAfiliasi tidak diketahui1
Adi MaryonoAfiliasi tidak diketahui1
SulaimanAfiliasi tidak diketahui1
Imron Nugroho SaputroAfiliasi tidak diketahui1
Mugiyo HartonoAfiliasi tidak diketahui1
HAFNITA HAFNITAState University of Semarang1
Nur YasinState University of Semarang1
Hermawan Pamot RaharjoState University of Semarang1
Malik FahadUniversitas Gadjah Mada1
Dewangga YudhistiraUniversitas Negeri Surabaya1
Ayu Tri AgustinState University of Semarang1
Irvan AbdillahYogyakarta State University1
Rifky Riyandi PrastyawanYogyakarta State University1
Wawan Sundawan SuhermanYogyakarta State University1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.foundae.com/index.php/jcss/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">The Role of Big Data Analytics (BDA) in Sport: Sports Data Mining</dc:title>
	<dc:creator xml:lang="en">Soedjatmiko</dc:creator>
	<dc:creator xml:lang="en">Nurcahyani, Atip </dc:creator>
	<dc:creator xml:lang="en">Sastaman B, Putra </dc:creator>
	<dc:creator xml:lang="en">Mashud</dc:creator>
	<dc:creator xml:lang="en">Nasrulloh, Ahmad </dc:creator>
	<dc:subject xml:lang="en">Big data analysis</dc:subject>
	<dc:subject xml:lang="en">Sports coaching</dc:subject>
	<dc:subject xml:lang="en">Sports technology</dc:subject>
	<dc:subject xml:lang="en">Performance analysis</dc:subject>
	<dc:description xml:lang="en">Background: Big Data Analytics (BDA) is urgently needed in Indonesian sports to improve evidence-based decision-making, athlete development, and organizational management. While BDA has transformed global sports through data-driven insights, its application in Indonesia remains limited and fragmented. The absence of integrated analytics between athlete performance and organizational quality management hinders the creation of sustainable, long-term development systems.
Aims: This study investigates how BDA can enhance athlete development and organizational management by analyzing multidimensional data from athletes, coaches, referees, and sports infrastructures. It also aims to identify dominant predictors of athlete performance across various sports and to evaluate the quality management practices of the National Sports Committee of Indonesia (NSCI).
Methods: A mixed-methods sequential explanatory design was applied. The quantitative phase involved 67 athletes from six sports: football, table tennis, weightlifting, pencak silat, basketball, and karate at the Sport Training Center. Data on anthropometry, fitness, and achievements were analyzed using descriptive statistics, ANOVA, Chi-Square, and regression tests. The qualitative phase involved interviews and observations with 8–12 stakeholders, while organizational quality was assessed using the Wilcoxon Signed Rank test.
Result: Results revealed significant performance differences among sports (F = 4.927, p = 0.001). Each sport had unique dominant predictors: VO₂ max and anthropometry (soccer), agility (table tennis), muscle strength (weightlifting), endurance and height (basketball), and speed (karate). NSCI’s organizational analysis showed substantial deficiencies in management, facilities, and procedures (p &amp;lt; 0.001).
Conclusion: This study confirms that BDA is crucial in advancing sustainable sports development. By identifying sport-specific performance predictors and systemic weaknesses, BDA provides a scientific foundation for designing targeted training, improving organizational quality, and building adaptive, data-driven sports ecosystems in Indonesia. The findings highlight the urgent need for national sports bodies to institutionalize BDA as part of long-term strategic planning.</dc:description>
	<dc:publisher xml:lang="en">CV. FOUNDAE</dc:publisher>
	<dc:date>2025-10-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.foundae.com/index.php/jcss/article/view/717</dc:identifier>
	<dc:identifier>10.58524/jcss.v4i2.717</dc:identifier>
	<dc:source xml:lang="en">Journal of Coaching and Sports Science; Vol. 4 No. 2 (2025): Journal of Coaching and Sports Science; 101-117</dc:source>
	<dc:source>2963-1483</dc:source>
	<dc:source>2963-1459</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.foundae.com/index.php/jcss/article/view/717/448</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Soedjatmiko, Atip Nurcahyani, Putra Sastaman B, Mashud, Ahmad Nasrulloh</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>