Why R is One of My Tools—and a Secret to Better Decisions in Sports Science

What’s the difference between a good sports organization and a great one?

It’s not just talent or budget.

It’s how they make use of the data they have.

The Problem with How Data is Seen in Sports

Most sports teams think they need to adopt complex models or hire the best tech experts to stay competitive.

But that’s not where the edge is.

The real edge comes from using the data you already have—effectively and consistently.

One Tool That’s Helped Me Elevate My Skills

Early on, I found myself relying too much on gut feel. I knew I needed a way to cut through the noise and find patterns hidden in plain sight.

That’s where R comes in. It’s not just a programming language for data experts. It’s a tool that helps you see what matters, fast.

I’m grateful to Daniel Yu Yu for helping me get started with R. His guidance has been invaluable in teaching me how to dig deeper into the data that I’ve been working with for years.

Applying What I Learned: How R is Shaping My Data Science Journey

Before diving into R, I struggled to turn raw data into meaningful projects. I knew there were insights buried in the numbers, but I didn’t have the right approach to bring them to the surface.

Now, I can confidently apply what I’ve learned to create projects that go beyond the basics—projects that not only analyze data but tell a story. R has become my tool of choice for turning complex sports data into clear, actionable insights, and it’s a critical part of my journey in bridging sports science and data science.

Each new project I take on builds on what I’ve learned, allowing me to dig deeper into player performance, game trends, and strategic improvements that I can apply to real-world scenarios.

Three Ways R Has Changed My Approach to Analysis

1. Cutting Down Time Spent on Data Prep: R’s libraries like dplyr and tidyverse make it easy to clean up and organize datasets. What used to take me hours, now takes just a few lines of code. No more manual sorting—just clean data, ready for analysis.
2. Making Numbers Easier to Understand: Using ggplot2, I can turn rows of stats into clear visualizations. Coaches don’t have to sift through spreadsheets—they get to see what’s working and what’s not, right away.
3. Generating Reports Instantly: Automating reports in R means I’m not spending time creating slides or word docs. Now, it’s just one command and I’ve got all the performance summaries needed for our weekly reviews.

The Bottom Line

Better tools don’t make better decisions.

Better use of the right tools does.

R isn’t just for people who love data—it’s for anyone who wants to get to the truth faster and more clearly.

Reading The Score Takes Care of Itself changed the way I think about success in sports. It’s not about the score. It’s about getting the little things right, over and over again.

R helps me do just that.

If you’re in sports science or analytics, I’d love to know: what tools have helped you find your edge? Let’s connect and share ideas!