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Home / Syllabus / B.Sc. (Data Science) / Fourth Year BSc Data Science Honours / Semester 7 / Sports Data Analytics Practical

B.Sc. (Data Science) Sports Data Analytics Practical Syllabus - Mumbai University 2026

This is the Fourth Year BSc Data Science Honours syllabus under NEP 2020, phased in one year at a time, from the academic year 2024-25. 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 Data Analytics Practical Syllabus.pdf
Major elective · Semester 7 · Fourth Year BSc Data Science Honours · 2 credits · 50 marks

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Syllabus for Sports Data Analytics Practical

Major elective · Semester 7 · Fourth Year BSc Data Science Honours · 2 credits · 50 marks

Module I

  • 1. Exploratory Data Analysis
  • Perform exploratory data analysis on a cricket dataset, analyzing variables such as number of matches, runs, not outs, wickets, etc.
  • Visualize the distribution of player performance metrics using histograms, box plots, or scatter plots.
  • Investigate the relationship between player age and performance metrics using correlation analysis. 2. Batting Performance Analysis
  • Analyze batting performance in a Cricket dataset, calculating metrics such as batting average, strike rate, and runs scored by players.
  • Identify top-performing batsmen based on performance metrics and compare their performance against different opponents or in specific conditions. 3. Bowling Performance Analysis
  • Analyze bowling performance in the Cricket dataset, calculating metrics such as bowling average, economy rate, and wickets taken by players.
  • Identify top-performing bowlers based on performance metrics and analyze their performance against different teams or in various match situations 4. Performance Comparison
  • Compare the scoring averages of top-performing batsman in different seasons.
  • Analyze the runs scoring (strike rate of Batting) of players from various teams in a specific league 5. Player Position Analysis
  • Calculate position-specific performance metrics and compare players within each position.

Module II

  • 1. Injury Analysis
  • Investigate the relationship between player injuries and their subsequent performance using historical injury and performance data.
  • Identify patterns and trends in the data to determine the impact of injuries on player performance and team success. 2. Team Analysis
  • Analyze the impact of toss on a team's overall scoring and winning percentage.
  • Study the relationship between batting averages of players and their team's win-loss record 3. Sports Revenue Analysis
  • Analyze revenue generation in sports organizations by examining factors such as ticket sales, merchandise sales, and sponsorship deals.
  • Identify key drivers of revenue and provide recommendations for maximizing financial performance. 4. Predictive Modeling
  • Build a regression model to predict the number of runs scored by players based on their historical performance data.
  • Develop a classification model to predict the outcome of match based on team’s statistics. 6. Visualization and Reporting: (Mini-Project)
  • Prepare a comprehensive report summarizing the findings of the analysis and providing actionable insights for sports teams or organizations. 10 Text Books 1. Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers by Benjamin C. Alamar, Columbia university press, 2013 2. Sports Analytics and Data Science: Winning the Game with Methods and Models by Thomas Miller, 1st edition, Pearson FT Press, 2015 3. Sports Analytics: Analysis, Visualisation and Decision Making in Sports Performance by Daniel Memmert, Tim McGarry, and Tony Reilly, 2018 11 Reference Books 1. Cricket Analytics: Analytics and Data Science in Cricket by Tapan Bagchi and S. Raghunathan 2. Machine Learning using Python by Manaranjan Pradhan and U. Dinesh Kumar, Weily , 2020 12 Semester End Examination: 100% Sem – VII

Text Books

  • 1 Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers by Benjamin C. Alamar, Columbia university press, 2013
  • 2 Sports Analytics and Data Science: Winning the Game with Methods and Models by Thomas Miller, 1st edition, Pearson FT Press, 2015
  • 3 Sports Analytics: Analysis, Visualisation and Decision Making in Sports Performance by Daniel Memmert, Tim McGarry, and Tony Reilly, 2018
  • 1 Cricket Analytics: Analytics and Data Science in Cricket by Tapan Bagchi and S. Raghunathan
  • 2 Machine Learning using Python by Manaranjan Pradhan and U. Dinesh Kumar, Weily , 2020

Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020. 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
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