Mastering Football Data Analytics: A Step-by-Step Guide to Self-Learning
Your roadmap to mastering the essential tools and skills needed to thrive in football data analytics.
Introduction
You’ve immersed yourself in the world of football data analytics—you’ve read the books, watched the videos, and you’re convinced this path is for you. But now, the technical side seems a bit daunting and overwhelming. Don’t worry! In this post, I’ll break things down for you and guide you through the essential tools and skills you’ll need to master, making your self-learning journey much smoother and more manageable.
Mathematics
As much as some might try to avoid it, mathematics is crucial in football data analytics. Many people mistakenly focus solely on creating impressive visuals or building recruitment models, but in reality, about 80% of your time will be spent making sense of the data—and mathematics is the key to doing this effectively. I learned this lesson the hard way, so I want to help you avoid the challenges I faced. While the fun often lies in modeling or visualizing, most football data is scattered, and you need a solid mathematical foundation to bring order to it.
Now, you don’t need to be a mathematical genius, but having a good grasp of the basics—particularly in Statistics, Calculus, and Linear Algebra—is essential.
Statistics: This playlist of 8 videos covers everything you need. If you watch with full focus and take notes, it will be more than enough. I’ve already mentioned that Krish Naik is one of the best creators for technical content in the data world.
Calculus & Linear Algebra: For these subjects, I’m a huge fan of the 3Blue1Brown channel. The way concepts are broken down is outstanding, so following along with some of the algebra videos should give you the foundation you need.
If you’re short on time or just want to focus on specific Statistics concepts, StatQuest on YouTube is another great resource. You can search for individual topics as needed.
If you’re more of a visual learner and love a good story, "The Cartoon Guide to Statistics" is an underrated source of learning. This book turns complex concepts into engaging comics, making learning stats not just informative but also enjoyable. It's perfect for easing into the world of numbers without the intimidation factor.
By covering these areas, you’ll be well-equipped to tackle the mathematical side of football data analytics and excel in your learning journey.
Coding
In today's world, coding is an essential skill to master, regardless of your discipline. Before diving into coding, it’s important to develop logical thinking—something that often comes from mathematics and problem-solving. If you’re already well-equipped with these skills, then you're ready to start coding. In football data analytics, there are two key programming languages to consider: Python and R.
I recommend starting with Python due to its popularity and ease of use compared to R. However, learning R can still be very beneficial, particularly for data visualizations—an area where R truly shines, and which is crucial in football data analytics.
While I typically prefer learning from detailed documentation, I understand that time constraints can be a barrier. Here’s a more practical approach:
Python: The excellent Krish Naik has a playlist that helped me tremendously, and I highly recommend following it at least until the 21st video ("Tutorial 19 - Python Iterables vs Iterators"). While Object-Oriented Programming (OOP) is important, at the start, it’s better to prioritize the basics that will allow you to apply Python quickly. To avoid the hassle of local installations, I recommend practicing on Google Colab alongside the tutorials.
R: I haven’t delved as deeply into R, but if you already have a good grasp of Python, picking up R should be relatively straightforward. I followed a quick tutorial that gave me the foundation I needed, and now, whenever I need something specific, I just search for it.
After you’ve tackled these steps, you’ll have made significant progress toward your goal. But remember, don’t get stuck in tutorial mode—apply what you’ve learned and start working with actual football data. The best learning comes through hands-on experience!
Visualizations
I’ve already emphasized the importance of building powerful dashboards, and although visualizations aren’t my personal strength, I’ve come to realize how essential they are in football analytics. Effective visualizations allow you to communicate insights clearly, making them a valuable tool for any data scientist or analyst who wants to play a bigger role in their organization. In this field, being able to explain data is just as important as analyzing it.
For me, two tools stand out when it comes to creating impactful visualizations:
Tableau: I followed a straightforward tutorial by Tableau Tim, which made my first interaction with Tableau easy. The great thing is, you don’t need heavy installations—you can work directly online using Tableau Public, as I did. For inspiration, check out Edd Webster’s Tableau profile to see what’s possible. However, keep in mind that the free version (Tableau Public) has some limitations and doesn’t include all features.
Power BI: When it comes to Power BI, Alex the Analyst is by far the best resource. I’ve been a fan of his content for a while, and his guide is comprehensive and user-friendly. Power BI can be a great tool to learn, especially if you’re working with smaller teams or organizations that might not have the budget for more expensive software.
You don’t need to learn both. I chose to focus on Tableau because it’s more widely used by larger teams. However, Power BI might offer better local opportunities if your team or organization has a smaller budget. It really depends on where you want to focus your efforts.
Data Analytics
Modeling data is an exciting step where you bring together everything you’ve learned, but it’s crucial to approach it from a data analytics perspective.
To start, you’ll need to apply your knowledge of statistics to real-world data. For a clearer understanding of why statistics is essential in data analytics, I recommend checking out Krish Naik's Statistics for Data Science playlist. It provides a great foundation on how statistical concepts are used in data-driven projects.
Before diving into modeling or making predictions, there are a few important steps you need to take:
Exploratory Data Analysis (EDA): This helps you get familiar with the data, uncover patterns, and detect any outliers or anomalies.
Feature Engineering: This step involves preparing and transforming the data to make it suitable for modeling by selecting the right features that impact the outcome.
By watching Krish Naik's video playlist tutorial, you'll become familiar with the two crucial steps—Exploratory Data Analysis (EDA) and Feature Engineering—giving you a deeper understanding of the dataset.
Lastly, it's important to understand the various methods and models used in data analytics. Once again, Krish Naik’s playlist on Machine Learning is an excellent resource for this, with his playlists offering a detailed look at different modeling techniques.
Conclusion
With these tools in your arsenal, you're now well-equipped to take your football data analytics journey to the next level. The key is to consistently apply what you've learned, particularly in football-specific contexts. Consider working on a substantial project—whether it's analyzing a team's performance, creating predictive models for player recruitment, or exploring tactical trends. Once completed, showcase your work by sharing it publicly, whether through platforms like GitHub, LinkedIn, or X.
This will not only help you receive valuable feedback but also allow you to refine your skills and stand out in the competitive field of football analytics.
Additionally, starting an online blog can be a game-changer. Sharing your insights and work regularly not only builds your personal brand but also positions you as a thought leader in the space. Over time, this can lead to networking opportunities, collaborations, and even job offers. The more visible and active you are in the football data analytics community, the more doors you’ll open for yourself.
Remember, it’s all about continuous learning, applying your skills, and making your work visible. By doing so, you’ll stay ahead of the competition and steadily move closer to achieving your career goals.
Call to Action
As you work through the material, you'll notice yourself leveling up and becoming a stronger version of yourself in football data analytics. Once you feel confident in your coding or visualization skills, don’t hesitate to share your progress. Post your findings online, document your journey, and be proud of the milestones you achieve. You can even tag me on X (Twitter) or LinkedIn when you share your work, or feel free to send it to me privately for feedback.
Remember, learning by doing is the best approach—don’t get stuck in endless tutorials. Start coding, experimenting, and applying your skills to real-world problems.
Identify an interesting challenge within football data analytics, and transform it into a project that can set you apart. This could be the stepping stone that catapults your career forward.
Enjoy the content, and I’ll see you on the dugout.