In This Episode, You Will Learn:

Uncovering bad data as it relates to sports science.

The in’s and out’s of the logic model.

How to simplify complex systems.

Measurement fallacies and biases you should be aware of.

 

Resources + Links:

Connect with Peter on Twitter | @PeterSchryvers

 

Get your copy of Bad Data on Amazon | https://www.amazon.com/Bad-Data-Measure-Things-Metrics/dp/1633885909

 

Check out Anthony’s Masterclass - 

The High Performance Hockey Masterclass

 

Follow Anthony on Instagram | @anthonydonskov

Follow Anthony on Twitter | Anthony Donskov, PhD

Subscribe to our YouTube Channel | The HPH Podcast with Anthony Donskov

 

Follow HPH Podcast on Instagram | @hph_podcast

Follow HPH Podcast on Twitter | @TheHPH_Podcast

 

Learn more on our Website | https://www.donskovsc.com/

 

Check out Anthony’s Books

Physical Preparations for Ice Hockey: Biological Principles and Practical Solutions

The Gain, Go, and Grow Manual: Programming for High Performance Hockey Players

 

Show Notes:

Here’s what you can learn from data - the good and the bad! We’re joined by Peter Schyrvers, urban planner and author of Bad Data: Why We Measure the Wrong Things and Often Miss the Metrics That Matter! We’ll be talking about his book to uncover the chapters as it relates to sports science. Our conversation today is all about data and how we misuse it. Join us as we go over the in’s and out’s of the logic model, simply complex systems, dissect common fallacies & biases, and much more. What is your data really telling you? Discover how to make the measurements that matter the most by mastering the bad data!

00:00 Welcome to the show, Peter Schryvers, the author of Bad Data!

01:45 What was the impetus for writing this book?

04:50 The difference between a measure and a metric.

06:00 Why do we measure?

08:00 What are the unintended consequences of teaching to the test? 

12:05 How do you define a logic model?

14:40 Can we measure outcomes close to the scoreboard?

17:25 Ad break.

18:15 What is the problem with only counting part of the whole?

21:25 How do we look at the forest while accounting for the trees?

25:50 What skills are invaluable in team sports?

28:50 What is the gambler’s fallacy?

32:55 Unpacking the lamp post problem.

35:30 What is a good measure?

38:10 Does all data matter?

41:05 Why is it equally as important to choose what not to count?

42:00 What did it feel like writing your book about bad data?

45:25 What is your favorite section of the book?

49:25 The biggest lesson of Bad Data.

51:40 How do you measure complex systems?

54:35 What is next for you?