• Mickey Ferri

What Makes a Great Athlete: The Data Under the Hood

Updated: Sep 18, 2018

I was honored to give a presentation at the "Let's Go Data Science" event in San Diego on August 30. In this post, there is:

1. A link to the survey "What Makes a Great Athlete"

2. A summary of the talk

3. Links to the slides I used in the presentation

4. Videos of the talk, split into 16 minute segments

1. The “What Makes a Great Athlete” SURVEY: http://bit.ly/GreatAthleteSurvey


This survey takes just 3-5 minutes, and the answers may surprise you! I recommend filling in the survey before looking at the slides or watching the talk.


During the presentation I presented survey results from the first 38 people who took the survey. We've now had more people answer, and I plan to recompile the results once we hit 100 responses. Every answer sheds more light on this important topic, so if you are interested in "What Makes a Great Athlete," we greatly value your contribution!

2. A summary of the talk

The overall talk was about one hour long. Here's the outline:

1. Introductions (5 min)

2. Data Science (3 min)

3. Pro Sports Examples (7 min)

4. Exercise & Sports (5 min)

5. Great Athletes (5 min)

6. Running

a. Case Study: Bolt and the 100m (5 min)

b. How Running Works (5 min)

c. Running Data (5 min)

d. Running Advice (10 min)

7. Q&A (10 min)


This talk was at the interaction of Data Science and Exercise. Our world has long been fascinated by great athletes. Nearly everyone exercises, and many of us are working toward specific exercise goals. Millions of people use technology to collect data about their exercise. Analyzing and interpreting the expanding pool of exercise data is a tremendous and growing opportunity for data scientists.


I discussed how data scientists collect, store, process, and interpret exercise data to help athletes improve, which included:

  1. Highlight the cutting edge in data science from various professional sports, including examples from his personal experience.

  2. Discuss professional opportunities for data scientists in exercise, sports, and health.

  3. Dissect data behind famous running examples, including Usain Bolt, Roger Bannister, and Eliud Kipchoge.

  4. Share personal data from local runners in San Diego.

  5. Offer practical advice to individuals who are curious to learn how to improve their own personal exercise data analysis.

During and after the slides, a fascinating discussion evolved among the audience about personal data tracking, nutrition, the value of data, great athletes, and some other really interesting topics.

3. Here is a link to download the final presentation, which was 96 slides!

4. Videos of the Talk


Part 1: Intro and Slides 1-20


Part 2: Slides 20-41


Part 3: Slides 41-61


Part 4: Slides 61-88


Part 5: Slides 88-end, and discussion


Part 6: Discussion


Part 7: Discussion



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