The digital transformation of the sports industry has heightened the need for data-driven managerial practices. Sport managers now work in environments dominated by performance metrics, predictive models, sponsorship analytics, and financial assessments, underscoring the strategic importance of data in organizational success. Consequently, integrating data analytics into sport management education is both a pedagogical priority and a strategic necessity to equip future professionals with essential competencies. However, adoption among practitioners remains uneven, not because of technological barriers but due to gaps in education, skills development, and pedagogy. Insufficient attention to the role of educational ecosystems in shaping acceptance and use of analytics leaves a significant gap in both theory and practice.

This paper addresses the gap by presenting a conceptual investigation into how educational experiences and teaching methodologies can shape the adoption of analytics in sport management. Drawing on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), it proposes a framework to better understand the learning processes that support analytics-driven thinking. Sport organizations increasingly rely on analytics across domains such as athlete monitoring, injury prediction, fan engagement, sponsorship valuation, ticket pricing, and scouting, demanding professionals able to work fluently with complex data ecosystems. However, managerial adoption remains fragmented, raising concerns about the readiness of graduates and practitioners to operate in data-rich environments. Traditional curricula often emphasize management, marketing, or law but lack experiential training in analytics, producing graduates who may hold theoretical knowledge yet lack data literacy and confidence in applying analytics to real-world decisions.

The framework considers educational influences at three levels. At the micro-level, data literacy, analytical mindset, technological confidence, and experiential learning shape individual skills. At the meso-level, curriculum design, interdisciplinary collaboration, and faculty expertise are central, while at the macro-level, partnerships, accreditation policies, and lifelong learning provide systemic support. Pedagogical methods such as experiential, case-based, flipped, and scaffolded learning further strengthen these competencies through authentic problem-solving.

Integrating analytics into sport management education offers clear benefits: students develop critical thinking, evidence-based reasoning, and digital fluency with tools like Tableau, Power BI, R, Python, and CRM platforms, while also improving employability in areas such as performance analysis and sponsorship evaluation. Institutions, in turn, must invest in faculty training, expand industry collaboration, and promote lifelong learning opportunities for professionals.

In conclusion, this study repositions analytics as a pedagogical priority rather than a technological trend. By aligning curricula, teaching methods, and institutional strategies, higher education can prepare a new generation of sport leaders equipped to think critically, act strategically, and lead confidently in a data-driven environment.

Keywords: Data Analytics Adoption, Evidence-Based Decision-Making, Sport Management Education, Technology Acceptance Models.