6 Sports Analytics Project Ideas

also my first ever email 😄

Welcome to the very first article I’ve ever sent out!

This has been a long time coming but I’m excited to jump into providing actionable tips about sports, AI, and data topics.

I’ll talk about each idea and then give some skills these projects should help you improve.

Let’s jump into it.

If you’d rather just watch a YouTube video I created about this exact topic, you can access that here.

Project Idea #1 → Data Collection Pipeline

This project should encompass all things data collection.

We’ll want to start with scraping data from a website, transforming and cleaning that data, and then storing it in some sort of database. That could be a file system, a simple Google Cloud drive or if you want big bonus points here, you can store them in a cloud database or file system on AWS.

Skills:

• Data Engineering

• a bonus of cloud development if you go with the cloud storage

Project Idea #2 → Scouting

The second project is going to be a “scouting” project. Scouting is a key component in every team and organization so being able to master scouting unlocks a lot of skills and opportunities.

This can build off of the first project but we want to use data & statistics to analyze different players.

We can analyze their strengths, weaknesses, or how players would fit in certain systems or teams.

Ideally these scouting reports have analysis, recommendations as well as some visualizations on top to make them easily digestible.

Skills:

• Data analysis

• Statistical modeling

Project Idea #3 → Game Analysis

The third and final “beginner” project is going to be focused on game/match analysis.

This analysis could be on the post-game and on providing an analysis of what happened, what went right, what went wrong, where the team can improve, etc.

Or it could be focused on the pre-game of an opponent and strategies to exploit weaknesses, different tactics that could be used, and much more.

To do this analysis you’ll want to:

1. Create reports of a game or previews of a game

2. Include visualizations

3. Includes recommendations of tactics, improvements, etc.

4. Report on where the team or opponent strived and where it failed

You should try to make this more than just some graphs and have actual insights that are packaged up so a coach can digest it and use it to make informed decisions.

Skills:

• communication

• visualization

• analysis

Project Idea #4 → Predicting Game Outcomes

Now let’s jump into the advanced projects

The first advanced project is predicting game outcomes using machine learning.

This also can be integrated with project #1 to get the data you’ll need.

To do this you’ll want to gather historical data and then predict whether a team will win or lose.

I’d stay simple at the start and use simple classification algorithms like decision trees but you can explore different methods and algorithms.

If you want a bonus, deploy the model to an API and have it predict matches each week.

Skills:

• Machine learning

• Model deployment if you end up deploying the API

Project Idea #5 → Player Tracking via Video

The second advanced project is creating a model that can track players in a video and then create data from that video.

Data company in sports use computer vision heavily to gather and create data from videos & cameras.

In this project you’ll want to create a computer vision model using something like openCV if you’re in Python and then extracting data from that.

You can also get a nice introduction to deep learning here if you decide to go down that route.

Skills:

• computer vision

• deep learning

Project Idea #6 → Sentiment Analysis

The last advanced project is to perform sentiment analysis for a team, player, org, club, etc.

Natural language processing is a huge part of data even outside of sports.

What we’ll do here is gather tweets, blog posts, interviews, news articles and then create reports based on the sentiment of those.

You can even involve some AI and go down the route of using the OpenAI library and learning about prompt engineering.

Skills:

• natural language processing

• prompt engineering (if using the OpenAI library)

And there they are!

Remember these aren’t things you must do to improve your skills but they are hopefully inspirational to get you started or help in your journey.

Until next time ✌️

Random things:

1. I’ve been trying to read more. I’m reading the Red Rising series right now and it is becoming one of my favorite sci-fi series I’ve read

2. This language translation deep fake model / company is super cool

3. Football matchup predictions this weekend. No analytics, just vibes:

   1. Real Madrid v Barcelona: 2-2

   2. Man City v Man United: 4-1

   3. Napoli v AC Milan: 1-2

   4. Inter v Roma: 3-1

4. This video on a guy training an AI to play Pokemon I’ve watched maybe 10 times this week