munotes®
Sign in
Home / Syllabus / B.Sc. (Data Science) / TY BSc Data Science / Semester 6 / Sports Analytics

B.Sc. (Data Science) Sports Analytics Syllabus - Mumbai University

This is the TY BSc Data Science syllabus under NEP 2020, in force from the academic year 2026-27. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.

Sports Analytics Syllabus.pdf
Major · Semester 6 · TY BSc Data Science · 2 credits · 50 marks

Loading syllabus...

Syllabus for Sports Analytics

Major · Semester 6 · TY BSc Data Science · 2 credits · 50 marks

Module I: Foundations and Predictive Performance Analytics

  • Chapter 1: Foundations of Sports Data Analytics and Athlete Management Systems
  • Evolution and scope of sports analytics
  • Data collection methods: manual, optical tracking, wearables, video analytics
  • Data preprocessing: cleaning, normalization, encoding, validation
  • Athlete Management Systems (AMS): design principles and architecture
  • Ethical considerations, data privacy, and governance
  • Integration of performance and health data Chapter 2: Statistical Assessment and Performance Evaluation in Sports
  • Key Performance Indicators (KPIs) in sports
  • Descriptive and inferential statistics in sports science
  • Data interpretation and visualization
  • Performance benchmarking
  • Contextual analysis of player and team performance
  • Evidence-based decision-making in sports Chapter 3: Predictive Modelling and Game Performance Analytics
  • Analytical frameworks for sports performance modelling
  • Regression, classification, and machine learning basics
  • Game outcome prediction models
  • Tactical and strategic analytics
  • Model validation and performance metrics
  • Applications in team selection and match strategy

Module II: Advanced Applications, AI, and Sports Business Analytics

  • Chapter 4: Injury Analytics and Load Management Technologies
  • Athlete workload monitoring concepts
  • Injury risk modelling and mitigation strategies
  • Biomechanical and physiological data integration
  • Wearable technology applications
  • Data-driven rehabilitation and return-to-play decisions Chapter 5: Artificial Intelligence Applications in Sports Performance
  • AI-driven performance analysis (Badminton case study model)
  • Computer vision in sports analytics
  • Machine learning algorithms for opponent analysis
  • Explainable AI in sports
  • Challenges and limitations of ML in sports environments Chapter 6: Fan Engagement Analytics and Future Trends in Sports Data Science
  • Fan behavior analytics and engagement modelling
  • Social media and digital analytics in sports marketing
  • Revenue optimization using data analytics
  • Emerging technologies in sports (AI integration trends)
  • Ethical challenges and future research directions 10 Text Books 1. Sports Data Analytics: Techniques, Applications, and Innovations, edited by Mehul S. Raval, Tolga Kaya, N. Sertac Artan, and Christopher Taber, published by Springer Nature (Studies in Big Data Series), 2026, First Edition. 2. Machine Learning and Data Mining for Sports Analytics: 9th International Workshop, MLSA 2022 Proceedings, edited volume (workshop proceedings), published by Springer, 2022, First Edition. 3. Football Analytics with Python & R: Learning Data Science Through the Lens of Sports, by Eric A. Eager and Richard A. Erickson, published by O’Reilly Media, 2023, First Edition. 4. Game of Edges: The Analytics Revolution and the Future of Professional Sports, by Bruce Schoenfeld, published by Grand Central Publishing, 2023, First Edition. 5. Sports Performance Analytics: Data-Driven Approaches for Athlete Optimization, by Carlos Lago-Peñas and Jaime Sampaio, published by Routledge (Taylor & Francis), 2022, First Edition. 11 Reference Books 1. Handbook of Statistical Methods and Analyses in Sports, by Jim Albert, Mark E. Glickman, Tim B. Swartz, and Ruud H. Koning, published by Chapman & Hall/CRC Press, 2017, First Edition. 2. Analyzing Baseball Data with R, by Max Marchi, Jim Albert, and Benjamin S. Baumer, published by Chapman & Hall/CRC Press, 2019, Second Edition. 3. Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers, by Benjamin C. Alamar, published by Columbia University Press, 2013, Updated Edition. 4. The Data Game: Controversies in Social Science Statistics and Sports Analytics, by Mark P. Taylor, published by Princeton University Press, 2022, First Edition. 5. Data Analytics in Sport: Concepts and Cases, by Craig Williams and Mark Hughes, published by Routledge, 2021, First Edition. 12 Internal Continuous Assessment: Semester End Examination: 60% 40% 13 Continuous Evaluation through: 30 marks Semester End Examination Lecture attendance, Mid Term teste, assignment, class test, presentations on new real life applications and its working 14 Format of Question Paper: (Semester End Examination: 30 Marks. Duration: 1 Hr ) Q1: Attempt any three (out of five/six) from Module 1 (15 Marks) Q2: Attempt any three (out of five/six) from Module 1 (15 Marks)

Text Books

  • 1 Sports Data Analytics: Techniques, Applications, and Innovations, edited by Mehul S. Raval, Tolga Kaya, N. Sertac Artan, and Christopher Taber, published by Springer Nature (Studies in Big Data Series), 2026, First Edition.
  • 2 Machine Learning and Data Mining for Sports Analytics: 9th International Workshop, MLSA 2022 Proceedings, edited volume (workshop proceedings), published by Springer, 2022, First Edition.
  • 3 Football Analytics with Python & R: Learning Data Science Through the Lens of Sports, by Eric A. Eager and Richard A. Erickson, published by O’Reilly Media, 2023, First Edition.
  • 4 Game of Edges: The Analytics Revolution and the Future of Professional Sports, by Bruce Schoenfeld, published by Grand Central Publishing, 2023, First Edition.
  • 5 Sports Performance Analytics: Data-Driven Approaches for Athlete Optimization, by Carlos Lago-Peñas and Jaime Sampaio, published by Routledge (Taylor & Francis), 2022, First Edition.
  • 1 Handbook of Statistical Methods and Analyses in Sports, by Jim Albert, Mark E. Glickman, Tim B. Swartz, and Ruud H. Koning, published by Chapman & Hall/CRC Press, 2017, First Edition.
  • 2 Analyzing Baseball Data with R, by Max Marchi, Jim Albert, and Benjamin S. Baumer, published by Chapman & Hall/CRC Press, 2019, Second Edition.
  • 3 Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers, by Benjamin C. Alamar, published by Columbia University Press, 2013, Updated Edition.
  • 4 The Data Game: Controversies in Social Science Statistics and Sports Analytics, by Mark P. Taylor, published by Princeton University Press, 2022, First Edition.
  • 5 Data Analytics in Sport: Concepts and Cases, by Craig Williams and Mark Hughes, published by Routledge, 2021, First Edition.

Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020, in force from the academic year 2026-27. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 2024 25 DS SEM I & II NEP NEP 2020 syllabus, in force from 2024-25 Read full PDF Read
PDF 2023 24 BSc Data Science Sem V & VI Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read
PDF 2021 22 BSc Data Science Sem III & IV Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read
Report or request
Done!