92526 Chap.2 Where Data Comes From Across Sport
Where Data Comes From Across Sport
Data in sport is everywhere, and this chapter makes that claim specific.
Following the Week 1 slides, it walks through the areas that collect data, from biomechanics, strength and conditioning and physiology to performance analysis, sports medicine, physiotherapy, psychology and nutrition, and on to talent identification, coaching, business and marketing, naming the measurements each one relies on.
A matrix sorts those sources by capture mode, separating body-worn sensors, video, self-report, laboratory or clinical tests and digital records, because the mode is the first clue to a dataset's quality risks.
The chapter ends with the three questions the slides ask before anything counts as data, about the unit, the item and the observation, and with the purpose question that sits above all three.
What this chapter covers
- 01
Areas of sport that collect data
- 02
Biomechanics, physiology and strength testing sources
- 03
Performance analysis, tracking and video
- 04
Medicine, psychology and nutrition data
- 05
Business, talent identification and marketing data
- 06
Capture modes and their quality risks
- 07
Three tests for whether something is data
Worked example · free
Match four club data sources to their area and capture mode
- 1GPS distance belongs to sports physiology, where GPS units monitor training, and it is captured by a wearable sensor.
- 1Ticket scans are marketing data on attendance, generated as digital records by the ticketing system.
- 1Food diaries are nutrition data on dietary intake, captured by self-report.
- 1Force-plate jumps are biomechanics or strength testing, captured as a laboratory test.
Key terms
- Performance analysis
- The area of sport that studies match and game data, player tracking and technical or tactical coding over time.
- Ground reaction force
- The force a surface exerts back on an athlete, measured by force plates in three directions.
- Self-report data
- Information athletes provide about themselves through questionnaires, surveys, diaries or ratings of perceived exertion.
- Talent identification
- The use of performance history, maturation, socio-demographic and support data to judge an athlete's long-term potential.
- Engagement metrics
- Marketing measures of how audiences interact with a club, such as social media activity and website traffic.
Where Data Comes From Across Sport FAQ
Where is data found in sport?
Almost everywhere. The Week 1 slides name strength and conditioning, biomechanics, physiology, performance analysis, sports medicine, physiotherapy, psychology, nutrition, talent identification, coaching, business and marketing, and list the measurements each collects, from force plates and GPS to questionnaires, imaging, ticket sales and social media analytics.
What kinds of data does biomechanics collect?
Biomechanics relies on video, three-dimensional motion capture of joint and segment displacements, velocities and accelerations, and force measurement, including ground reaction forces in three directions. Several of these, such as motion capture and force plates, are laboratory tools, which the Week 5 material describes as accurate but often impractical for regular use because of cost and complexity.
How is marketing data different from sport science data?
Marketing data is mostly generated by systems rather than by measuring bodies: audience data, engagement metrics, social media analytics, website traffic, ticket sales, attendance and merchandise figures. Sport science data usually comes from sensors, laboratory tests or athletes' own reports, which raise different quality and privacy questions.
Can everything be treated as data?
Not automatically. The slides suggest three checks: whether the thing belongs to a particular person or entity such as a team, whether what is collected is something real, and whether it can reflect that person's or team's experience. Above all three sits the question of purpose, because data collected without one still has to be stored and protected.
Why does the capture mode matter?
Each mode brings its own weaknesses. Sensors can drift or be worn badly, video needs time-consuming coding, self-report depends on honesty and recall, and laboratory tests demand equipment and time. Knowing how a dataset was captured tells you which quality dimensions to check first before using it for a decision.
Exam move
Pick one sport you know well and list two data sources it uses from each area on the Week 1 slides, then label each with its capture mode. Check your list against the slides so that every example is one they would recognise, such as heart rate in physiology or merchandise data in marketing.
Practise the three tests for whether something counts as data on a borderline case, such as a coach's notes or a fan forum, and always finish by stating the purpose. This builds the specific, sport-anchored vocabulary that Assessment 1 rewards when it asks you to identify the data a fictional organisation collects.
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