Sports Analytics

The details
Sport, Rehabilitation and Exercise Sciences (School of)
Colchester Campus
Undergraduate: Level 6
Thursday 03 October 2024
Friday 13 December 2024
19 October 2023


Requisites for this module



Key module for


Module description

This module examines the application of analytics within applied sport environments, to facilitate data-informed decision-making within the coaching process and beyond (e.g. performance anlaysis, talent development and identification, sports medicine).

Students will be introduced to a range of statistical modelling and machine learning techniques which are prevalent in applied sport environments. Furthermore, students will gain experience analysing real datasets from different sports.

Students will gain a knowledge and understanding of effective data visualisation for different sport performance data, and apply key concepts and principles to produce engaging data visualisation. In addition, students will develop their knowledge, understanding and mastery of industry standard software for sports analytics, including R-Studio, Microsoft Power BI and Tableau.

Students will be tasked with identifying and applying appropriate analytical techniques, and interpreting the results to generate actionable insights for coaches and other decision makers within applied sport environments. In addition, students will enhance their knowledge and understanding of sport science and coaching processes, through exploring the opportunities and challenges for the integration of sports analytics within various aspects of applied practice.

Module aims

The aim of this module is:

• To provide students with an understanding of sports analytics, by exploring prevalent statistical modelling, machine learning and data visualisation techniques which inform decision-making within applied sport environments.

Module learning outcomes

By the end of this module, students will be expected to be able to:

1. Demonstrate coherent and detailed knowledge and understanding of analytical methods and their potential applications within applied sport environments.
2. Select and apply appropriate statistical modelling and machine learning techniques to analyse sports performance data.
3. Critically evaluate and interpret the results of such techniques to derive practical implications for coaches and other decision makers within applied sport environments.
4. Demostrate knowledge, understanding and application of relevant data analysis and visualisation software to solve sports performance problems.
5. Demonstrate effective communication skills to convey information in written, oral, and visual formats to key stakeholders within applied sport environments.

Module information

This syllabus is an initial draft and is subject to change and amendments.

Week 2:
- Lecture: Module overview & introduction to sports analytics
- IT-Lab: Programming R-ecap

Week 3:
- Lecture: Technical actions (Part 1)
- IT-Lab: Analysing and visualising event data (Part 1)

Week 4:
- Lecture: Technical actions (Part 2)
- IT-Lab: Analysing and visualising event data (Part 2)

Week 5:
- Lecture: Finding groups in data
- IT-Lab: Cluster analysis

Week 6:
- Lecture: Modelling relationships in data
- IT-Lab: Regression models

Week 7:
- Lecture: Categorising data
- IT-Lab: Classification models

Week 8:
- Lecture: Quantifying physical performance
- IT-Lab: Analysing tracking data

Week 9:
- Lecture: Recruitment analysis
- IT-Lab: Player evaluation
- Voluntary support class

Week 10:
- Lecture: Let's get visual!
- IT-Lab: Dashboard creation (Part 1)

Week 11:
- Lecture: Module recap
- IT-Lab: Dashboard creation (Part 2)
Voluntary support class

Learning and teaching methods

The module will be delivered in-person only and will include a mixture of lectures and practical (IT-lab) sessions, as well as voluntary support classes. More specifically: - 10 x 1 hour lectures - 10 x 3 hour practical (IT-lab) sessions - 2 x 1 hour voluntary support sessions There will also be weekly independent study tasks to consolidate learning in taught sessions.


This module does not appear to have a published bibliography for this year.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Oral Presentation - FASER submission      

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Alice Harkness-Armstrong, email:
Dr Alice Harkness-Armstrong
School Undergraduate Office, email: sres (Non essex users should add to create the full email address)



External examiner

Prof Paul Potrac
Northumbria University
Available via Moodle
Of 12 hours, 12 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.


Further information

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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