University of Technology Sydney

96309 Performance Analysis and Data Science

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Subject handbook information prior to 2024 is available in the Archives.

UTS: Health
Credit points: 6 cp
Result type: Grade and marks

There are course requisites for this subject. See access conditions.


This subject examines the role of the performance analyst in a sporting context. Students explore data-led frameworks and performance analysis tools used to investigate a variety of problems faced by sports practitioners. The strengths and limitations of common analytical methodologies are appraised and students examine the typical metrics that are collected on a daily, weekly, monthly and yearly basis. Data visualisation and presentation methods are also considered in order to optimise communication of empirical findings to a range of stakeholders. This includes aspects of acute decision making along with decisions that relate to player development and athlete acquisition. To ensure relevance with current best-practice, this subject utilises a plethora of contemporary research, and exposes students to a range of industry experts, who facilitate learning by presenting information obtained from the professional sport industry. This approach ensures the contemporary relevance of the content. A key aspect of this subject is the use of a comprehensive assessment that requires students to use a dataset from professional sport and systematically analyse the information to provide an integrated solution for enhancing athlete preparation.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:
A. Investigate the complex nature of sport and evaluate common performance analysis frameworks
B. Evaluate the physical, technical and tactical components of a variety of sporting contexts
C. Apply authentic database solutions and implement effective data handling procedures
D. Create effective visualisations to optimise data communication strategies in high performance sport
E. Critically evaluate contemporary evidence related to performance analysis and translate into practical recommendations for use within high performance sport
F. Sensitively and effectively communicate with a range of stakeholders in diverse social, cultural and multidisciplinary high performance sports settings

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the following graduate attributes:

  • Adapt to an extensive array of stakeholder circumstances and integrate evidence to empower optimal and sustainable sport, exercise and health outcomes for service users. (1.2)
  • Critically appraise, assess and synthesise evidence relevant to high performance sport practice from an extensive range of sources to develop creative, innovative and effective evidence-based solutions. (2.1)
  • Expertly solve problems by applying advanced methodologies of sport, exercise and health interventions in a logical and systematic manner; and the ability to document decisions appropriately. (2.2)
  • Expertly integrate expertise and synthesise evidence to determine the validity and reliability of a range of advanced methodologies, tools and techniques and employ those relevant to high performance sport practice. (4.2)
  • Expertly communicate with key stakeholders and adapt to challenging, complex and diverse industry contexts to create positive and professional relationships in a high performance sport environment. (5.2)

Teaching and learning strategies

Blended learning strategies used in the Canvas learning platform provide students with an array of foundational material in the area of performance analysis and data science. Conceptual aspects of performance analysis are detailed throughout the subject and learning material is designed to instill an understanding of each component with a view to expanding on this information in live zoom sessions. The primary sources of information are in the form of interactive written material, short videos, short lectures, case studies, interviews with industry experts, websites, and journal manuscripts. The variety of delivery mediums available via the Canvas learning platform ensures that students can interact with the subject content each week at a time that is convenient to them, in a location in which they are comfortable, and at their own speed. Moreover, expert staff are available at regular intervals to provide feedback about all aspects of the subject content.

The learning material available online for each aspect of performance analysis and data science is partitioned into three modules. These modules provide a sequential approach to understanding the essential components of performance analysis and include a background of the concept, discussion of testing and implementation methods, identification of commonly reported shortcomings of the concept, case studies involving the relevant content, and an overview of the real-world applicability of the concept. To complement the online material, regular live discussion sessions have been scheduled throughout semester, with students able to converse with industry experts who will be involved in Zoom sessions. These activities provide opportunities to liaise with professional industry experts and will inspire students to immerse themselves in the available material while enhancing their understanding of detailed concepts.

A major component of the assessment for this subject is an assignment that requires students to identify a relevant aspect of performance to analyse via the use of a dataset generated from a professional sporting organisation. Students will be required to compile a framework that suitably addresses key research problems, present the information in a clear manner using appropriate visualisation strategies, and integrate the outcomes into the design of short- and long-term training programs. Further, students will be required to compile a presentation pitched at a senior staff member at a professional sporting organisation to convey key aspects of their analytical process. Such an activity is ecologically valid and specifically replicates the real-world work of a performance analyst.

Content (topics)

Module 1 – Understanding Performance Analysis

  • Fundamental concepts of Performance Analysis and Data Science
  • Approaches to data science and conceptualising research questions
  • “Fast” vs “slow” approaches to analysing performance
  • Introduction to physical, technical and tactical elements of performance analysis
  • Frameworks for approaching performance analysis, eg. Problem, Plan, Data, Analysis, Conclusion (PPDAC)
  • Contextual factors of performance analysis
  • Overview of relevant stakeholders in performance analysis and data science in sport

Module 2 – Tools and Methodologies for Data Science in Sport

  • Overview of key tools for analysis in sport:
    • Video coding
    • Statistical information for technical data
    • Global Navigation Satellite Systems/local positioning systems
    • Inertial measurement units
    • Introduction to R programming language and software
  • Introduction to data visualisation and dashboards
  • Structuring data for optimal storage and analysis procedures
  • Foundations of databasing

Module 3 – Next Level Performance Analysis

  • Overview of analytics in management and the “front office” of sport
  • Use of data analytics in player management, player value and recruitment strategies
  • Methods of obtaining large datasets
  • Advanced data handling methods


Assessment task 1: Conceptualisation of performance analysis (brief report)


This assessment examines student understandings of contemporary performance analysis methods. The report assesses the concepts explored in Module 1 and aims to provide feedback to students regarding their level of understanding of foundational performance analysis.


This assessment task addresses subject learning objective(s):

A and B

This assessment task contributes to the development of graduate attribute(s):

2.1 and 2.2

Type: Report
Groupwork: Individual
Weight: 10%

600 words

Assessment task 2: Performance Analysis Framework (Part 1): Data Management and Visualisation


This assessment examines data management and visualisation concepts that are central to performance analysis and data analytics. Students are required to critically approach a high performance sport problem by evaluating a real-world dataset to clearly present and communicate tangible outcomes that could be implemented to enhance team performance. Further, students are required to express their processes and provide justifications for their course of action in terms of the database properties and the presentation methods selected.


This assessment task addresses subject learning objective(s):

A, B, C, D, E and F

This assessment task contributes to the development of graduate attribute(s):

2.1, 2.2, 4.2 and 5.2

Type: Report
Groupwork: Individual
Weight: 50%

Microsoft Excel File (or other appropriate software) plus 750 word written report

Assessment task 3: Performance Analysis Framework (Part 2): Verbal Presentation


This assessment enables students to showcase their communication skills by presenting their database and data visualisations from Part 1. The task requires students to clearly outline the processes they used to develop the relevant outputs presented in their output file (could be Excel or other appropriate software) and ensure they have a deep understanding of the data to make a case to the audience regarding the importance of regular testing in the weekly, monthly or yearly training cycles. This part of the assessment enables students to demonstrate a thorough understanding and implementation of data presentation.


This assessment task addresses subject learning objective(s):

A, B, C, D, E and F

This assessment task contributes to the development of graduate attribute(s):

1.2, 2.1, 2.2, 4.2 and 5.2

Type: Presentation
Groupwork: Individual
Weight: 30%

10 minute presentation (delivered live via Zoom)

Assessment task 4: Participation in online discussions


This assessment encourages regular participation in class discussion boards and online forums throughout the semester. Marks will be awarded for regularity of posts along with their content, depth and relevance.


This assessment task addresses subject learning objective(s):

A, B and E

This assessment task contributes to the development of graduate attribute(s):

2.1 and 4.2

Type: Exercises
Groupwork: Individual
Weight: 10%

Short responses

Other resources

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HELPS (Higher Education Language & Presentation Support)
HELPS provides assistance with English language proficiency and academic language. Students who need to develop their written and/or spoken English should make use of the free services offered by HELPS, including academic language workshops, vacation intensive courses, drop-in consultations, individual appointments and Conversations@UTS ( HELPS staff are also available for drop-in consultations at the UTS Library. Phone (02) 9514 9733.

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