FSHP Academy
Sports Technology — all lessons

Module 1 – Introduction

Evolution and role of technology in sportImpact on training, recovery and competitionCase studies: technology in Olympic and elite sport

Module 2 – Biomechanics & Motion Capture

Biomechanics basics for technology usersMotion analysis tools: cameras, sensors and appsForce plates (e.g. VALD ForceDecks)Reaction-light systems (e.g. BlazePod)DynamometryRunning gait analysis (e.g. GaitON)Timing and jump systems (e.g. Hyperlab)

Module 3 – Wearables & Monitoring

Types of wearables: GPS and heart-rate monitorsInterpreting speed, load, fatigue and HRV dataIntegrating wearable data into training

Module 4 – Performance Testing & Analytics

Performance testing and analytics workflows

Performance testing and analytics workflows

An end-to-end workflow for performance testing and analytics — designing test batteries, data cleaning, analysis, visualisation and decision-making, with spreadsheet-friendly methods.

Advanced Evidence: Strong evidence 3 min read Reviewed 2026-10-31 Status: draft

Learning objectives

  • Design a performance testing battery
  • Clean and organise test data
  • Analyse change using reliability statistics
  • Report results that inform decisions

Before this lesson

Why this matters

A clear workflow turns testing days into better training decisions.

Performance data workflow A cycle of six steps: start with a question about which decision the data will inform; collect data with valid and reliable tests; clean it by checking errors and missing values; analyse it against baseline, typical error and the smallest worthwhile change; communicate it with simple visuals; decide and act by adjusting training, then return to the question. 1. Question Which decision will it inform? 2. Collect Valid, reliable tests 3. Clean Errors, gaps, outliers 4. Analyse vs baseline, TE and SWC 5. Communicate Simple visuals for coach 6. Decide & act Adjust training, review Data is only useful if it changes a decision
Figure 1. A performance data workflow. — Original diagram, FSHP Academy (schematic, not to scale).

Test battery design

QualityExample tests
StrengthIMTP, 3RM
PowerCMJ (force plate) [mcmahon2018]
Speed10/30 m sprint
Agility/COD505 test
EnduranceYo-Yo IR1, 30-15 IFT

Data management

Tidy format: Athlete | Date | Test | Trial | Value | Conditions. Use consistent naming; keep raw data unchanged.

Analysis

  • Change vs typical error and SWC [hopkins2000]
  • Z-scores for squad profiling
  • Trends over time

Reporting

Simple visuals, one page, actions highlighted [thornton2019]. Integrate with monitoring [bourdon2017].

Common mistakes and misconceptions

Case study

An S&C coach builds a spreadsheet with tidy test data, z-score radar charts and automatic SWC flags. Pre-season meetings with coaches become shorter and more focused.

Further learning: video lessons

Video links are only marked “verified” after a human has watched them. Unverified entries open a YouTube search for that channel and topic rather than a guessed link.

Technical needs verification — opens a YouTube search

Performance testing and analytics workflows

sport science practitioners and manufacturers’ education channels (search)

You will understand: performance testing and analytics workflows

Why: Practitioner content; verify the specific video and be aware of commercial bias.
Technical needs verification — opens a YouTube search

Validity and reliability explained

sport science practitioners and manufacturers’ education channels (search)

You will understand: judging measurement quality

Why: Practitioner content; verify the specific video and be aware of commercial bias.

Knowledge check

Knowledge check · 0/5

1. A tidy data structure means…

Answer B. Analysable.

2. A z-score expresses…

Answer B. Profiling.

3. Before trusting test changes, know…

Answer B. Hopkins (2000).

4. The first step in an analytics workflow is…

Answer B. Purpose.

5. Data cleaning includes…

Answer B. Quality.

Short-answer & application questions

  1. Build a testing spreadsheet template.
  2. Calculate z-scores for a squad.
  3. Write a test report for a coach.

Key takeaways

  • Choose tests that are valid, reliable and relevant to performance.
  • Organise data in a tidy format: one row per athlete-test-date.
  • Interpret change against typical error and smallest worthwhile change; use z-scores for profiling.
  • Communicate simply and act on results.

Connected topics

sports technology

Integrating wearable data into training

football

Assessment protocols for footballers

cricket

Assessment protocols for cricketers: strength, power, speed, agility, endurance

References

  1. Hopkins WG (2000). Measures of reliability in sports medicine and science. Sports Medicine, 30(1):1–15. Narrative review check pending
  2. Thornton HR, Delaney JA, Duthie GM, Dascombe BJ (2019). Developing athlete monitoring systems in team sports: data analysis and visualization. International Journal of Sports Physiology and Performance, 14(6):698–705. Narrative review check pending
  3. Bourdon PC, Cardinale M, Murray A, et al. (2017). Monitoring athlete training loads: consensus statement. International Journal of Sports Physiology and Performance, 12(Suppl 2):S2161–S2170. Guideline check pending
  4. McMahon JJ, Suchomel TJ, Lake JP, Comfort P (2018). Understanding the key phases of the countermovement jump force-time curve. Strength and Conditioning Journal, 40(4):96–106. Narrative review check pending

Educational content only — not medical advice. Consult a qualified clinician for diagnosis or treatment.