1  Introduction to the Module

1.1 Overview

This module is designed to be an entry-point to the field of sport data analytics. As well as gaining an overview of the field of sport data analytics, you’ll learn some of the fundamental tools and concepts necessary for the discipline including R and RStudio, data cleaning and preparation, and statistical modelling.

1.2 How is the module organised?

The module is divided into two elements, which run concurrently from weeks 1 - 10.

The final week of the module (Week 11) will involve an assessment briefing and preparation session.

Element One: Post-Class Reading (Self-Study)

In this element, you’ll be asked to read a selection of journal articles that cover various aspects of contemporary sport data analytics.

Each week will have a clearly-defined topic, and two papers that align with that topic. You are expected to complete the readings on Thursday/Friday each week.

You’ll be given some key concepts or themes that emerge from the papers, and a short set of reflective questions that will help you start thinking critically about the application of sport data analytics within modern sport.

For the current academic year, the topics will be:

All readings are freely available to read and/or download online via the University library. The reading list can be accessed on the B1700 myplace page, using the ‘Module Reading List’ link, or here.

In addition to the essential readings each week, each topic has a number of articles or other items for ‘further reading’ which you’ll find helpful in developing your knowledge of the field.

Please note that your engagement with these readings is a central element of one of the assessed components for the B1700 module.

Element Two: Pre-Class Reading and In-Class Practicals

In the second element, you’ll be introduced to a range of technical skills and processes that are essential for work in sport data analytics.

This will include the R programming language and RStudio as our primary analytical environment for the MSc, explaining the syntax and data types within R and the interface components of RStudio. You’ll gain an understanding of data structures, control structures, and functions in R, providing a solid groundwork for later data manipulation and analysis.

We’ll explore more advanced topics in R such as data importing, manipulation using tidyverse, and basic data visualisation using ggplot2. You’ll be introduced to a comprehensive and repeatable process for data analysis, from data collection to ensuring data quality, data pre-processing, exploratory data analysis, and predictive analytics.

You’ll develop the skill of converting raw data into informative visualisations and meaningful insights, contributing to efficient and informed decision-making processes. You’ll be introduced to prescriptive analytics and the handling of big data, and develop an understanding of data-driven decision-making processes.

1.3 Staff information

The module will be led and delivered by Dr Allan Hewitt (allan.hewitt@strath.ac.uk), who is based in Graham Hills 538.

Individual appointments with Allan can be arranged using the doodle link on the module myplace page.

1.4 Learning Objectives

By the end of the module you should:

  • understand the diverse ways sport data is employed in professional sports settings. You’ll gain exposure to a variety of scenarios in which data is utilised, from strategising and training enhancements to injury prevention and player performance tracking.

  • demonstrate your ability to analyse various data types and forms to gain insights into sporting outcomes and trends. This understanding of the practical applications of sports data will provide you with an enriched perspective on its significance within the sports industry.

  • demonstrate your ability to evaluate the importance and impact of sport data analytics within professional sports settings. This involves understanding how data-driven insights can optimise performance, inform strategy, enhance scouting, and improve player health, amongst other applications.

  • demonstrate your ability to create responses to practical issues in professional sports settings using available data. You will articulate how to harness data to make informed decisions and implement effective solutions. This includes using data to form strategies, address performance issues, optimise training plans, and improve player wellbeing.

  • have confidence in manipulating and organising commonly-found data in professional sports settings. You’ll be expected to demonstrate your ability to apply common statistical techniques to analyse this data, transforming raw figures into actionable insights.

  • be able to engage critically with the subject matter, comparing arguments for and against the use of data analytics within professional sports settings. This involves analysing the ethical implications, accuracy, and potential biases of data analytics, as well as its potential to revolutionise the sports industry.

  • be able to demonstrate a rounded understanding of the role of data analytics in sport, equipped with both the practical skills and the critical thinking abilities necessary for success in the field.

1.5 Module Structure

The module is delivered over ten weeks from September to November. There is a final week that involves assessment preparation.

Overview

As noted above, the module is divided into two elements.

In the first element (running Weeks 1 to 10), you’ll be introduced to a range of literature from the broad field of sport data analytics. This literature has been selected to allow you to develop a basic understanding of how sport data analytics has been conceptualised and researched within the academic literature.

The element is based around self-directed study (‘Post-reading’). Each week you’ll have a short, introductory section to read, and then two academic papers to review. There will be follow-up questions that encourage you to reflect on what you have read.

This element should be completed on Thursday/Friday each week.

In the second element (running Weeks 1 to 10), you’ll develop your practical skills in data handling, R programming, and basic analysis. This will be based around:

  1. Pre-class reading, which you are expected to complete on Monday/Tuesday each week.
  2. In-class practical each Wednesday, where I’ll demonstrate the various techniques and concepts. You’ll then be required to replicate the demonstration to build confidence and competence in working with the various techniques and concepts.

This element will cover:

  • Using R and RStudio (Week 1): Here, you’ll be introduced to the R programming language and the RStudio development environment. We’ll cover basic syntax in R, and how to work with the different interface components of RStudio.

  • Working with Sport Data in R (Weeks 2- 6): Here, you’ll develop your confidence in using R and learn more about coding principles, data structures in R, preparing data for analysis, and basic techniques for data exploration.

  • Data Analysis and Interpretation (Weeks 7 - 10): In this final section, you’ll review and apply a range of techniques for data analysis and interpretation, and build confidence in the creation and application of regression models and techniques for multivariate statistical analysis.

1.6 Module Assessment

Assessment One

The first assessment for the module is due at the end of University Week 14/Module Week 7 (8th November 2024). This assessment is worth 40% of your overall grade for the module.

For this assessment, you’ll be provided with a data set and a list of operations that should be performed on the data set. Working in a supervised environment, you’ll complete these operations and submit your output in a format set out in the assessment briefing.

Assessment Two

The second assessment for the module is due at the end of University Week 20 (20th December 2024). This assessment is worth 60% of your overall grade for the module.

For this assessment, you’ll be asked to submit a 3,000 word critical review of how sport data analytics are currently employed within professional sports settings, focusing on the strengths and weaknesses of current approaches and the challenges involved in data collection, analysis, and preparation.

You are expected to show evidence of reading in the academic literature, including (but not restricted to) the recommended readings for each week.