ULMS Electronic Module Catalogue

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Sports Business Analytics
Code MGTK709
Coordinator Dr DC Cockayne
Marketing (ULMS)
Year CATS Level Semester CATS Value
Session 2023-24 Level 7 FHEQ Whole Session 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):


Modules for which this module is a pre-requisite:


Programme(s) (including Year of Study) to which this module is available on a required basis:


Programme(s) (including Year of Study) to which this module is available on an optional basis:


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 2


Timetable (if known)              
Private Study 124


EXAM Duration Timing
% of
Penalty for late
CONTINUOUS Duration Timing
% of
Penalty for late
Executive dashboard Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: Yes    60       
Essay Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: Yes    40       


The module aims to:

Nurture an understanding of the importance of data-driven decision making and its subsequent impact on strategy formulation;

Enable students to understand how statistical analysis and data visualisation assist in identifying sport business trends and solutions.

Learning Outcomes

(LO1) Demonstrate knowledge and a broad understanding of the emerging data driven sport business landscape.

(LO2) Evidence an understanding of emerging technologies and the potential for disruption and development in the use of data analysis in sports business.

(LO3) Apply quantitative analysis to understand and present complex data to support evidenced-based decision making.

(LO4) Discuss critically how data visualisation contributes to evidence-based decision making and organisational communications.

(LO5) Reflect creatively on how analytical techniques can address sport business problems.

(LO6) Ideate and prototype applications of data visualisation techniques in the construction of organisational and executive dashboards and reports.

(LO7) Appraise the value of analytics across a range of other business contexts.

(LO8) Assess the limitations of data analytics in the decision-making process.

(S1) Flexible and adaptable.
Through the collection and evaluation of a variety of data sets and sources of data relative to a multitude of organisational objectives, students will develop skills designed to encourage and promote their versatility and ability to adapt to complex situations.

(S2) A problem solver.
The data alone will not provide solutions. Students will need to work with the data collected to provide an evidence base relative to given problems/objectives designed to mirror those faced in the sports business environment.

(S3) Numerate.
There is no requirement to perform the complex mathematics involved in quantitative analysis of data, however an understanding of how to visualise and (re)present numeric data is a fundamental skill developed through this module.

(S4) An excellent verbal and written communicator.
Through the creation and presentation of data visualisations and inherent data narratives students will develop skills in the verbal and written communication of data.

(S5) IT literate.
Students will engage directly with software packages in the searching, collection, processing and analysis of data; and in the visualisation and communication of data.

(S6) Ethically aware.
The collection and storage of user data represents an important ethical consideration for all business types, and particularly in sport. Students will be encouraged to reflect on ethical considerations regarding data collection, storage and use.

Teaching and Learning Strategies

The module will primarily be delivered through eight e-lectures/seminars. These will consist of podcasts covering key concepts, theories, and practical applications of quantitative data analysis and visualisation communication techniques. Contemporary software packages will be used to support practical activities linked to lecture content. Individual online tasks and discussion boards will be used to develop and apply learning within the sport industry and the students’ own work contexts. These will be moderated by the module tutor to ensure feedback, and to support the development of the virtual cohort. Readings and contemporary debates surrounding this area will be used to punctuate vocational development, ensuring balance between ‘know-how’ and ‘know-why’.

Unscheduled Directed Student Hours: 24 hours

Description: The e-Lectures/seminars will equate to 3 hours/week over 8 weeks, undertaken asynchronously.

Attendance Recorded: Ye s – tracked via the learning platform.

Additionally, one scheduled synchronous seminar will be delivered (if there are issues with time zones another seminar will be provided).
Scheduled Directed Student Hours: 2 hours

Description: The scheduled seminar will equate to 2 hours undertaken synchronously. The date and time of the seminar will be confirmed at the start of the module.

Attendance Recorded: Yes – tracked via the learning platform.

Self-Directed Learning Hours: 124 hours

Description: This will involve directed and independent reading, independent research and assessment preparation.



Data, Big Data, and Disruptive Technologies;
Performance versus Business Analytics in Sport;
Quantitative Analysis in Sports Business;
Techniques, Technology and Challenges for data-driven quantitative analysis in Sports Business;
Data Visualisation A: Organising the Data;
Data Visualisation B: Creating the Data Narrative;
Data Visualisation C: The Executive Dashboard;
Ethics and Data Governance.

Recommended Texts

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.