How I would learn sports analytics in 2024

2024 is right around the corner and just one year you can change your life.

5 years ago I was seeing an uptick on Twitter of analytics content. I was still in college trying to learn how to code, taking stats classes, and trying to decide what I wanted to do with my life.

After a couple of months of seeing all of this content, I decided I wanted to learn how a lot of people were making these incredible visualizations and doing advanced analytics in sports.

After tinkering around for a while I finally figured out how to do a lot of things but my learning could have been sped up a ton if I had had some sort of guidance, advice, or even just a simple roadmap of what I should be learning and focusing on rather than bouncing from idea to topic to project.

This article will provide just that. I’ve been learning about sports analytics and trying to hone my craft for 5 years so I’ll give you a couple of things you can start working on and how you can evolve your sports analytics journey over the next year!

Coding

The first thing you are going to need to learn is how to code.

I’ll keep it simple. Learn a combination of either Python or R and then SQL on top. These languages are the most commonly used in sports analytics and in organizations, teams, and clubs that are implementing analytics.

You can learn all three, but pick one (I recommend Python) and get good at the basics. Then you can move on to SQL and the other language. If your goal is to work in sports analytics, coding is a non-negotiable. It’s a basic requirement so you’ll want to learn it as soon as possible.

Coding is something that you won’t learn in a week or a month, or even be able to perfect in a year. You’ll never be able to perfect it in reality. Technology is always evolving and changing and if you are trying to perfect that skill you’ll be wasting time. But it is something that is going to be critical for your sports analytics journey so learning as early as you can is one of the most important steps you can take.

Coding also opens up a ton of opportunities outside of analytics so it’s a skill you should learn regardless of what career you want to end up in.

Start as soon as possible and be consistent by practicing and learning every day and you’ll be surprised where you are at in a year.

Here are a couple of my favorite resources for learning to code:

For Python:

• My YouTube Channel (sorry self plug haha)

• FC Python

• Python crash course book

For R:

• Michigan Analytics Youtube

• Dom Samangy YouTube

For SQL:

• Code Academy beginner course

Math

Honestly, I overlooked how important understanding basic math and statistical concepts was when I first started. I thought I was going to be able to rely on just using premade packages in code and not grasping the core concepts of how things were working and the underlying that powered those models.

Statistics and algebra are probably the most important you’ll need to know as you get started so I would recommend you go with statistics at the beginning of the year and once you feel comfortable with that you can move on to algebra.

You should understand key statistical concepts such as probability, hypothesis testing, sampling, distributions, and regression to start with.

You don’t need to become Einstein, but you should spend a good chunk of time learning about these core concepts and understanding them.

It will make a huge difference in being able to apply analytical concepts to sports. A big differentiator I see between top-level analysts and people who aren’t quite at the same level is usually the understanding of math. Those who understand the math powering these models can innovate, create new ideas and concepts, and advance the field of sports analytics.

My go-to resource for learning math is Khan Academy.

Honestly, sometimes I go back and rewatch some of the basic materials because it’s so good. It has a great stats and probability course you can work through which is a great intro to all the concepts talked about above.

Getting your hands dirty

Now probably the most important part, getting your hands dirty with some projects.

The only way you are going to progress is if you start and keep going. Keep learning and keep improving. Working on projects is a great way to do this and is what I tell everyone who asks me how they can improve their sports analytics journey.

You will learn faster and gain more traction by getting your hands dirty with different projects that you are passionate about than you will by doing an endless amount of tutorials.

These projects could be simple things like building a radar plot to building a full-on app or website. By working on projects you’ll learn how to problem-solve, create, and think. These are all important processes in sports analytics.

In 2024 you should be trying to publish and work on projects as much as you can. By doing this you build up a portfolio and have something to show future employers or others in the field. This also allows you to get feedback and progress your learning even further.

Consistency

As I’ve mentioned, you have to be consistent. Learning sports analytics takes time. Like any skill, you’ll have to learn, fail, improve, and find ways to progress. This only comes from starting and continuing to progress.

By learning to code, math fundamentals, and building projects in 2024 you’ll be able to learn and be competent in sports analytics.