DSC8016 : Data-informed decision-making in Sport
- Offered for Year: 2025/26
- Module Leader(s): Dr Iain Spears
- Lecturer: Dr Daniel Henderson
- Owning School: Biomedical, Nutritional and Sports Scien
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module aims to equip student with the skills required for making data-informed decisions in sport. Students will develop advanced competencies in analysing, visualising, and modelling complex sport datasets. Students will critically engage with analytical frameworks, use cutting-edge data tools, and apply machine learning models to real-world decision-making in sport (performance evaluation, tactical insights, injury risk prediction and rehabilitation pathways).
Outline Of Syllabus
The syllabus will cover topics from:
• Advanced EDA: Multi-variable correlation, outlier detection, time-series comparison
• Data Visualisation Design: Principles of clarity, interactivity, audience tailoring
• Sport Dashboards: Tableau, Power BI for interactive performance reporting
• Time-Series and Spatiotemporal Data Analysis (e.g., tracking, force-time curves)
• Machine Learning in Sport: Supervised and unsupervised learning (e.g., injury clustering)
• Model Evaluation: (e.g. confusion matrix)
• Model Interpretability (e.g. model transparency in sport contexts)
• Football Case Studies: Recruitment models, team performance clustering, tactical visualisation
• Ethical and Professional Considerations: Bias, data privacy, interpretability in elite environments
• Security, privacy, and ethical frameworks: GDPR, data consent, secure storage
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 32:00 | 32:00 | Research essay – preparation and completion |
| Guided Independent Study | Assessment preparation and completion | 1 | 1:00 | 1:00 | Group project presentation (30 mins + 10 mins for questions) |
| Scheduled Learning And Teaching Activities | Lecture | 9 | 2:00 | 18:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 36:00 | 36:00 | Group project preparation and completion |
| Guided Independent Study | Assessment preparation and completion | 9 | 0:30 | 4:30 | Formative quiz sessions |
| Scheduled Learning And Teaching Activities | Practical | 8 | 3:00 | 24:00 | Practical sessions Guest/Specialist lectures |
| Guided Independent Study | Directed research and reading | 40 | 1:00 | 40:00 | Background reading |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 3 | 2:00 | 6:00 | Coursework support |
| Guided Independent Study | Independent study | 1 | 38:30 | 38:30 | Independent study on course content. |
| Total | 200:00 |
Teaching Rationale And Relationship
Lectures explain the underpinning principles for the module and technologies that support data-informed decisions in sports environments. Lectures are complemented by supervised practical sessions to guide the application of these principles using suitable technical tools. The practical work builds up experience working with a computational toolset that is used to complete a substantive project working with data from a real-world context.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Oral Presentation | 2 | M | 50 | Group project analysing a real sport dataset, including visualisation and predictive modelling. This will be presented as an oral presentation 30 mins plus 10 minutes Q and A. |
| Essay | 2 | M | 50 | Critical evaluation of data modelling approaches used in high-performance sport (1500 words) |
Zero Weighted Pass/Fail Assessments
| Description | When Set | Comment |
|---|---|---|
| Digital Examination | M | Set of quizzes on lecture content. 9 quizzes 30 mins each |
Assessment Rationale And Relationship
The module includes three summative assessments:
Applied Project : Group project analysing a real sport dataset, including visualisation and predictive modelling. They will prepare an oral presentation of their findings to a panel of sport practitioners
Research-informed Essay Critical evaluation of data modelling approaches used in high-performance sport.
The digital examination (formative) will provide feedback on their understanding of lecture content and key concepts.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC8016's Timetable