Sporting organisations, analysts and sport scientists work in environments that contain complex problems. For example, what type of drills to add in a training session, how much training an athlete should complete in a week, what athlete to select in a draft and if an athlete can progress quickly from an injury, in order to return to play. 

Recent improvements in sports technologies have increased the accessibility and volume of data to capture information that may help humans make decisions, yet solely, it is not possible for humans to make sense of these sources without assistance. To assist decision makers in environments whereby the data available is large and beyond the information processing capabilities of a human, decision support systems may be of use. Decision support systems assist with organisational decision making by using objective data to generate a recommendation or assessment. 

In this unit, students will learn how a decision support development framework can be utilised in high-performance sport. Students will conceptually map a sport performance problem, by evaluating the context, output and process of a decision support system. Students will then synthesise how various machine learning can be applied to form different outputs, decisions and recommendations. Students will translate findings from decision support systems and critique how they may be able to fit into a high-performance sport setting.

Unit details

Location:
Study level:
Postgraduate
Credit points:
12
Unit code:
SES7006

Learning Outcomes

On successful completion of this unit, students will be able to:
  1. Review how sports technology can be used to maximise value for decision-makers in sport;  
  2. Contextualise how decision support systems are useful in high-performance sport;  
  3. Evaluate a decision support system, for different decisions typically faced by analysts/ sport scientists; and,  
  4. Exhibit how machine learning approaches can be used to aid decision support in sporting organisations.  

Assessment

Assessment type Description Grade
Test Two online quizzes 20%
Report A 2000 word self-reflection on how students will evaluate the performance of their conceptually mapped decision support system 50%
Presentation Students will be present their results and insights, from the self-reflection, via video 30%

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