2025-12-09 –, Ernst-Curie
How can data science help young athletes navigate their careers? In this talk, I’ll share my experience building a career path planner for aspiring ice hockey players. The project combines player performance data, career path patterns, and predictive modeling to suggest possible development paths and milestones. Along the way, I’ll discuss the challenges of messy sports data and communicating insights in a way that resonates with non-technical users like coaches, parents, and players.
Objective
To share the process of building a data-driven career path planner for young hockey players, highlighting both the technical challenges and the broader impact of applying data science in real-world decision-making.
Outline
- Why hockey (and why career planning matters in sports)
- Data collection & wrangling: turning messy, incomplete performance records into usable data
- Modeling progression: techniques to identify likely career trajectories and milestones
- Communicating results: designing tools that resonate with non-technical stakeholders (players, coaches, parents)
- Lessons learned: challenges, surprises, and what translates to other domains beyond sports
Central Thesis
Data science can provide valuable guidance in high-stakes, personal decision-making — but building tools for non-technical users requires more than just models. It demands thoughtful data cleaning, careful feature selection, and communication strategies that make insights accessible and actionable.
Audience
This talk is for data scientists, analysts, and practitioners who are curious about applying machine learning to sports. It will appeal both to those interested in the technical process (data wrangling, predictive modeling) and those interested in building data products that impact real people.
Tone / Type of Talk
Informative and light-hearted — grounded in practical data challenges, with a touch of storytelling from the hockey world. This is not a heavily mathematical talk at all, suitable for broader audience as well.
Key Takeaways
- How to wrangle and model messy sports data for career planning use cases.
- How to design predictive models that communicate uncertainty and possible paths.
- Lessons on building data tools for non-technical stakeholders.
- Inspiration for applying data science to creative and impactful real-world problems.
Background Knowledge Expected
No prior knowledge of hockey is required — we’ll keep the sports side light and fun.
I graduated in statistics in 2018 and have been working in data analytics ever since, across a variety of roles. I am currently the Head of Sport Sciences at GRAET, a company building a platform to help aspiring ice hockey players develop their potential. Alongside this, I work as a part-time data analyst for one of the major Czech ice hockey clubs. My work combines a passion for sports with a love of data. Outside of work, I enjoy playing the drums, riding my bike, and debating whether pineapple belongs on pizza — though as a fan of pizza Hawaii, I’m firmly in the “yes” camp.