This module aims to:

Equip students with the tools and theoretical knowledge needed for data-driven decision-making in sports management.

Enable students to identify key managerial questions in sports organisations and apply the appropriate analytical tools to solve them.

(LO1) Students will be able to demonstrate the ability to use machine learning, forecasting and simulation techniques.

(LO2) Students will be able to identify and critically analyse data-driven questions in the context of sport.

(LO3) Students will be able to demonstrate a critical understanding and ability to apply of state-of-the-art methods for data analysis in the context of sport.

(LO4) Students will be able to critically assess how data analysis can be used in managerial decision making in the context of sport.

(S1) Adaptability
Students will develop an awareness of how data driven questions in sports can be answered in numerous ways.

(S2) Problem-solving skills
Students will be required to apply state of the art methods in data analysis to current issues and questions in a sports context.

(S3) Numeracy
Students will be familiarised with a variety data of analytics tools.

(S4) Commercial awareness
Students will understand how data analytics underpins commercial decisions.

The module will be delivered over twelve weeks, comprising ten teaching weeks plus two enhancement weeks. The approach to teaching and learning will combine the use of large group in-person and asynchronous lectures, small group seminars (or workshops), scheduled seminar preparation sessions, and cross-programme contemporary issues sessions.

Lectures (total of 14 hours) - Each week will include at least a one-hour scheduled lecture, except for four weeks (scheduled at the beginning, mid-points and end of the module), which will be delivered as two-hour, in-person, live lectures. One-hour lectures could also be delivered live and in-person but may alternatively be provided online or asynchronously (including appropriate scaffolding and online supporting material) at the discretion of the module teaching team.

Seminars (total of 12 hours) - Each module will include six two-hour seminars. These seminars will be interactive small-group in-person workshops.

Seminar prep aration (total of 6 hours) - Each seminar will also include a scheduled one-hour preparation session, enabling students to engage in relevant preparation activities, as deemed necessary by the module teaching team.

Contemporary Issues Sessions (total of 4 hours) - The module will also include two two-hour contemporary issues lectures or events that are directly relevant to the module and broader programme of study. These may include a lecture from a member of faculty on their research, an external industry speaker or a member of the advisory board and will be organised by the Director of Studies in coordination with module teams.

Self-directed learning (total of 164 hours) – Students will engage in self-directed learning in a wide variety of ways throughout the programme. This will include engaging in scaffolded independent learning tasks set outside the classroom on the virtual learning platform, independent reading from essential and recommended sources (e.g., jour nal articles, textbooks, industry reports, practitioner publications), assignment development and preparation, formative online quizzes, case study analysis, simulation-based tasks, and self-directed group activities. Staff responsible for the module will also provide weekly office-hours and dedicated assessment and feedback sessions for students to seek individual support and formative feedback on their independent learning and progress.

Introduction to Data-Driven decision making

Machine Learning, forecasting and simulation

Player Acquisition (win maximisation)

Player Acquisition (profit maximisation)

Matchday Lineup Decision Making

Team Health Management

Forecasting