The Powering Podiums event from March 13, 2019, hosted at Richmond Olympic Oval.


To investigate strategies to gather and use data effectively to make decisions to guide athlete programming.

  • Is what I am measuring telling me what I need to know?
  • What can I start, stop of continue to measure in my sport?
  • What are some strategies to effectively analyze the data so that it is meaningful for coaches and athletes?
  • How do I know that monitoring interventions are having an impact on athlete performance?

Why Data?

  • Evidence-based decision making
  • Comparing ranking informing or influencing
  • Augmented feedback
  • What’s measured matters

Athlete Conversion
In Drew’s presentation, he built toward a theme of connecting data best practices to optimal athlete targeting within a provincial program. Beginning with an introduction to the sport analytics movement, he then discussed how all PSO staff can play a part in data analysis and informed decision-making through their annual athlete nomination lists. In the second half of his talk, he discussed examples of how athlete data can be used within high performance programs and support efficient targeting within the system. Specifically, he keyed in on analysis that CSI Pacific is currently conducting such as athlete conversion rates, average age targeted, and how those two statistics intersect for data-informed decision-making.

Data vs. Information (Dalcher, 2015)

  • Data is the distinct facts, numbers, words and images; the raw input of organizational life.
  • Once, they are processed in some meaningful way, they become information that is interpreted and understood for a particular purpose.

Athlete Targeting Criteria

  • Outside of competition, what are you doing to identify high potential athletes?
  • Event-based vs. Performance-based
  • Without the resources (budget, capacity, etc.) to monitor every athlete in the province, can data analysis help to identify those “diamonds in the rough”?
  • National program vs. Provincial program


  • While days of the “eye test” are over, data doesn’t tell the whole story
  • Good data collection and cleaning practices are essential to informed decision-making
  • Nomination criteria / lists are informing much of our sport and system analyses
  • The critical question is “why”
  • CSI Pacific is doing a lot of this work already, and we can share and/or work with you on your current system

In Ming-Chang Tsai’s presentation, he described the data solutions process detailed the management (raw data and data processing), analytics (validation and modelling) and visualization steps (results and reporting).

Types of Data

  • GPS/IMU tracking device (ie Catapult, Metawear, Garmin, Suunto, Polar, …)
    • Accelerometer, gyroscope, magnetometer, GPS, HR, time, …
      • Acceleration, degrees of rotation, heading, altitude, position (longitude/latitude)
      • Speed, distance, spin/turn rate, cadence, player load, …
    • Performance testing
      • Race results, strength, speed, endurance, neuromuscular, physiological,…
    • Technical/tactical
      • Video coding, game box scores
    • Medical
      • Injury, sickness
    • Nutrition
      • Caloric intake
    • Mental / Psychological
    • Wellness
      • Sleep (quality & duration), muscle pain, muscle soreness, motivation, ….


Data Processing

  • Data wrangling
    • Cleaning
      • Formatting
    • Connecting different datasets
    • Calculating sport-specific metrics
      • Training Load
      • Performance
        • Physiological markers (VO2max, lactate threshold,…)
        • Force Velocity profile (strength, speed, power, force,…)
        • Race results
      • Wellness