Course Title: Sports Analytics

Part A: Course Overview

Course Title: Sports Analytics

Credit Points: 12.00


Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2223

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 1 2016

MATH2223

City Campus

Postgraduate

171H School of Science

Face-to-Face

Sem 1 2017

Course Coordinator: Dr. Ian Grundy

Course Coordinator Phone: +61 3 9925 3220

Course Coordinator Email: ian.grundy@rmit.edu.au

Course Coordinator Location: 8.9.27


Pre-requisite Courses and Assumed Knowledge and Capabilities

There are no pre-requisites for this course.


Course Description

 

Modern sports science and technology increasingly requires the use of applied statistical and analytical techniques. This course introduces you to the use of statistical analysis in a variety of contexts applicable to sport. The purpose of the course is to provide with the methods of analysis required specific to sports and sports technology.

You will learn how to apply the theory of statistical methods to tasks such as the analysis of game-day (in-play) sports data and pre and post-game sports performance modelling. The course will cover ratings models, prediction, inference, simulation, performance and notational analysis as found in the latest journals in statistical sports science. The course will make heavy use of appropriate and commonly-used technology e.g. MS Excel and statistical packages such as SPSS, Minitab or R. You will also be exposed to performance and video analysis software such as SportsCode.

To develop and test your sports analytic skills you will undertake lab and research-based project work using real world data. You will also gain experience in presenting sports analytic results to a non-technical audience in written and verbal form.
 


Objectives/Learning Outcomes/Capability Development

 

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.

Problem-solving

  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

Communication

  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

Information literacy

  • the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.


 

On completion of this course you should be able to:

  1. Perform and report on the exploratory analysis of data collected using sports technology
  2. Analyse sporting data of various types via astute use of statistical packages
  3. Present a case study supporting a conclusion based upon quantitative evidence gathered in a sports technology environment. This may include prediction, performance and analysis of a sporting team, code, or gaming environment.


Overview of Learning Activities

 

You will learn the techniques used in contemporary sports analysis as you participate in lectorials, tutorials and computer laboratory sessions. You will also develop your ability to critically evaluate the relevant sports literature, analyse sports data, and report to a lay audience, by undertaking an individual research project (case study). The basic theoretical background will be explained in the lectorials and guided reading tasks. Real-world analyses will be integrated into lectorials. Tutorials and laboratory work will consolidate the knowledge gained in the lectorials.The course is supported by the Blackboard learning management system.

The assessment for this course includes the development and presentation (written and verbal) of a research project (case study) due at the end of semester, weekly written assessment tasks, and laboratory work, submitted fortnightly. Feedback on your written and laboratory work will be provided to you during the semester.


Overview of Learning Resources

 

You will typically need to use professional level resources such as lectorial reading materials, laboratory topics and worksheets (all available via myRMIT studies). Specialist books and journals that are accessible in the RMIT library and other major libraries (recommended references as listed in the Part B Course Guide) may be needed. Internet data sources are essential for your sources of data and background to sports and gaming.

Library Subject Guide for Mathematics & Statisticshttp://rmit.libguides.com/mathstats


Overview of Assessment

 

Note that:

This course has no hurdle requirements.

Early Assessment Task 1:  Tutorial  Worksheets 1 - 3

Weighting 6%

Note: The tutorial worksheets are completed and returned weekly.

This assessment task supports CLOs 1 & 2

Early Assessment Task 2:  Laboratory 1

Weighting 10% (approx)

Note: The laboratory reports are completed and returned fortnightly.

This assessment task supports CLOs 1 & 2

Assessment Task 3:  Tutorial  Worksheets 4 - 10

Weighting 14%

Note: The tutorial worksheets are completed and returned weekly.

This assessment task supports CLOs 1 & 2

Assessment Task 4:  Laboratories 2 - 4

Weighting 20% (approx)

Note: The laboratory reports are completed and returned fortnightly.

This assessment task supports CLOs 1 & 2

Assessment Task 5: Case Study/Project

Weighting 50%

Note: This research task consists of several parts: written report (30%), verbal presentation (10%), poster (10%).

This assessment task supports CLO 1, 2 & 3