About¶
ScraperNHL is an open-source project dedicated to scraping, processing, and analyzing NHL data. It provides tools to collect raw league data, transform it into usable datasets, and support modeling and analytical workflows for hockey research.
This is roughly the tenth NHL scraper I’ve built since I started coding in 2017. Over time, I wanted something more efficient, more maintainable, and designed as a long-term project rather than a collection of one-off scripts. While this could have remained a personal tool, I chose to make it public. The hockey analytics community has helped me immensely over the years, and this project is my way of giving back.
ScraperNHL is a community project. Contributions of all kinds are welcome—bug fixes, new features, documentation improvements, or ideas. This is not my project; it is our project. Fork it, use it, improve it, and make it your own. If you have questions, feedback, or suggestions, feel free to reach out.
Author¶
| Name | Role | X / Twitter | Bluesky | Website |
|---|---|---|---|---|
| Max Tixador | Hockey Analytics Enthusiast | @woumaxx | @HabsBrain.com | HabsBrain.com |
Hello, I’m Max Tixador, a hockey analytics enthusiast and data analyst with a strong interest in using data to better understand and appreciate the game of hockey.
My path into hockey analytics is unconventional. I come from countries where hockey is either nonexistent or a tertiary sport, no one in my family follows sports closely, and I never played competitive hockey. As a kid, I memorized sports statistics by reading fantasy hockey magazines, which sparked my interest in hockey journalism. In my teens, I discovered hockey analytics and became fascinated by how data could explain the game beyond traditional statistics.
I later explored hockey graphic design and content creation, teaching myself visual design and social media growth. I originally learned to code to automate content creation, but quickly realized that programming could also unlock far more powerful ways to scrape and analyze hockey data.
A turning point came when I met Mikhail Nahabedian, then with the McGill Redbirds and now Director of Hockey Analytics for La Victoire de Montréal. Through his free online hockey analytics seminar series, he mentored me and introduced me to structured analytical thinking in hockey. Under his guidance, I spent countless nights learning to code, scrape data, build models, and create visualizations. Despite having no formal background in data science or programming, he helped me land my first professional opportunities in hockey analytics.
Since then, I’ve continued to grow independently through self-study and practice. My work includes building an Expected Goals model, developing a Regularized Adjusted Plus-Minus (RAPM) model, contributing to The Draft Digest (a data-driven draft prospect evaluation project), submitting work to hockey analytics competitions, and helping introduce several aspiring analysts to the field.
I strongly believe in end-to-end understanding—from data collection, to modeling, to visualization. I also believe growth does not happen in isolation. Collaboration, knowledge sharing, and community feedback are essential, and I am always open to discussion, critique, and new ideas.
Outside of hockey analytics, I am learning Mandarin Chinese (currently at a beginner level) and play soccer.
I operate under a simple belief: I will never know enough, and there will always be more to learn. That mindset drives both this project and my broader work.
I am open to collaborations, new projects, and opportunities related to hockey analytics. I am also interested in eventually formalizing my self-taught background through a degree or professional certification in data science or a related field.
Most importantly, I want to thank the hockey analytics community for its support and openness. This project exists because of that community, and I look forward to building more tools for it.
Contact: maxtixador@gmail.com