I tracked 5 years of my own body data to answer one question: "Does tennis make you lose weight?" The answer was r = 0.00. Then it got weirder. [OC] Statistics

What This Visualization Shows

This data visualization displays "I tracked 5 years of my own body data to answer one question: "Does tennis make you lose weight?" The answer was r = 0.00. Then it got weirder. [OC]" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on [https://x.com/kakapark/status/2026835540809801899?s=20](https://x.com/kakapark/status/2026835540809801899?s=20)

Full analysis as a Twitter thread with charts:

TL;DR of what I found:

\- Tennis hours vs weight change: r = 0.00 (literally zero)

\- But tennis hours vs body fat % next month: r = −0.21

→ The lag is one full calendar month

\- HRV by day of week: Friday is my weekly low, every single week

\- Optimal rest days after workout: exactly 2 (48 hrs)

\- 10k+ steps → lower HRV than <5k steps day

Every insight only appeared after combining two

variables that seemed unrelated.

Dataset: 1,200 workouts, 483 weight logs,

1,800+ days of HR/HRV/RHR. All in Google Sheets.

Happy to share methodology or answer questions., which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

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Data Analysis and Insights

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