Introduction to the Module
Overview
This module is designed to be an entry-point to the field of sport data analytics. Starting with an introduction to sport data analytics, you’ll familiarise yourself with some of the fundamental tools and concepts necessary for the discipline, specifically R and RStudio.
You’ll then encounter a range of practical, real-world situations in sport where data analytics can play a key role, and you’ll put into practice the skills learned earlier in the module.
What will we cover?
In weeks 1-5, we introduce 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.
In the second part of the module (weeks 6-11), we move on to a series of invited lectures on various relevant topics such as squad selection, strategy and tactics, training, team training and coaching, and business decisions. These sections include group discussions, challenges, and practical group activities designed to enhance your understanding and foster practical application of the theoretical concepts and programming ideas introduced in the first part of the module.
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. There will also be input from a range of external speakers.
Learning Objectives
This module is designed to help you 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.
You’ll 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.
Further, you’ll demonstrate an 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.
As the module progresses, you’ll 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.
As a key component of the module, you’ll gain hands-on experience 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.
Additionally, you should 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.
By the end of the module, you should 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.
Module Structure
Overview
As noted above, the module is divided into two parts.
In the first part (Weeks One to Five), you’ll learn how to use the software package R (and RStudio) in the context of sport data analytics. You’ll also be introduced to various statistical and data-management techniques and processes that are fundamental to data analytics within sport.
In the second part of the module (Weeks Six to Eleven), you’ll encounter a wide range of concepts and data drawn from a number of different professional sporting organisations and disciplines. Alongside guest lectures, you’ll be given the opportunity to work on ‘real world’ data problems and questions that flow from the guest lectures. This will allow you to put the skills acquired in the first part of the module to practical use, and futher enhance your understanding and confidence in data handling and analysis.
Weekly Syllabus
The module is delivered over 11 weeks. Each week contains three or four learning units, each of which will last around one hour. In addition to these learning units, there will be required reading and suggested additional learning activities.
Some of these units will be delivered in a lecture-tutorial format, while others will require you to work independently, or to work as part of a group.
Week One - An Introduction to R
Introduction to sport data analytics (1.1)
Introduction to R and RStudio (1.2)
RStudio interface components (1.3)
Basic R syntax and data types (1.4)
Week Two - Working with R and RStudio
Data structures in R (2.1)
Control structures and functions (2.2)
Importing data into R (2.3)
Data manipulation using tidyverse (2.4)
Week Three - Working with R/Processes for Data Analysis
Basic data visualisation using ggplot2 (3.1)
Processes for data analysis in R - Introduction (3.2)
Data collection (3.3)
Ensuring data quality (3.4)
Week Four - Processes for Data Analysis
Data pre-processing (4.1)
Exploratory data analysis (4.2)
Predictive analytics (4.3)
Prescriptive analytics (4.4)
Week Five - Processes for Data Analysis
Working with big data (5.1)
Data-driven decision making (5.2)
Review/consolidation (5.3)
Review/consolidation (if required) (5.4)
Week Six - Squad Selection
Guest lecture - data analysis for squad selection (6.1)
Group discussion/challenge (6.2)
Group activity (6.3)
Week Seven - Strategy and Tactics
Guest lecture - data analysis for strategy and tactics (7.1)
Group discussion/challenge (7.2)
Group activity (7.3)
Week Eight - Strength and Conditioning
Guest lecture - data analysis for training (8.1)
Group discussion/challenge (8.2)
Group activity (8.3)
Week Nine - Team Training and Coaching
Guest lecture - data analysis for team training and coaching (9.1)
Group discussion/challenge (9.2)
Group activity (9.3)
Week Ten - Business Decision-Making
Guest lecture - data analysis for better business decisions (10.1)
Group discussion/challenge (10.2)
Group activity (10.3)
Week Eleven - Module Recap
Recap (11.1)
Assessment briefing (11.2)
Module Assessment
Assessment One
The first assessment for the module is due at the end of University Week 12/Module Week 5 (20th October 2023). This assessment is worth 20% 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 (15th December 2023). This assessment is worth 80 per cent 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, as well as drawing evidence from the practical case-studies and guest lectures included within the teaching programme.
A briefing for this assessment will be held during Module Week 11.
Reading List
An important part of this module is developing your awareness and understanding of the research literature in the field. Each week, a set of readings have been identified which should be completed prior to the tutorial.
There are direct links to these papers via the library reading list here.