Sport Analytics

• Sport Analytics: studying data from sports performances to try and improve performance
• Basic Functions of Sports Analytics:

  • monitor athletes fitness for performance
  • used to develop skill + technique
  • helps prevent injury - vibration, electro stimulation
  • enables coaches/teams/players to analyse games
  • can be used to talent ID/scout
• Quantitative Data: data that can be written down or measured precisely + numerically
• Qualitative Data: data that is descriptive + looks at the way people think or feel
• Examples of Quantitative Data in Sports Tech:

  • VO2 fitness tests
  • most other fitness tests
  • stat analysis from matches/competitions
• Positives of Quantitative Data:

  • can be used to provide formal, objective data
  • used to gather quantitative data (factual info + numerical data)
• Examples of Qualitative Data in Sports Tech:

  • coach expressing an opinion when watching a performance
• Positives of Qualitative Data:

  • word focussed, not numbers
  • looks at feelings, emotions and opinions only
  • used to gain a better understanding of participants experiences
• Negative of Qualitative Data:

  • subjective
  • can be less precise an meaningful than quantitative data
  • can also be time consuming
• Collecting Quantitative Data:

  • questionnaires + surveys
  • (in sport) stopwatches/HR monitors/tape measures
• Collecting Qualitative Data:

  • interviews
  • observations
• Objective: fact-based info which is measurable + usable
• Subjective: based on personal opinion which is less measurable and often less usable
• Validity: an indication of whether the data collected actually measures what it claims
• Reliable: refers to the degree to which data collection is consistent and stable over time