This module helps you analyze draft prospects for the NHL and explores ways to create sports stories from that analysis.
On this Page
- Learning Objectives
- Module Map
- Module Resources
- Published Story
- Key Takeaways
- Additional Resources
- Up Next
Learning Objectives
After completing this module, you'll be able to:
- Identify good sources for getting NHL prospect data
- Explain how to use an equivalency metric for player evaluation
- Compare and rank a set of incoming prospects
- Create a sports story from your data analysis
Module Map
This module contains the following progressive lessons.
- Lesson 1: The Big Question
- Introduces the big question that this module will analyze.
- Lesson 2: Show Me the Data
- Provides an overview of the data source and dataset for the lesson.
- Lesson 3: Stats and Methodology
- Overview of key hockey statistics and approach to analyze the data.
- Lesson 4: Data Analysis and Visualizations
- Walkthrough of the data analysis and resulting charts.
- Lesson 5: Discovering the Storyline
- Explores the different stories that you can create from the analysis.
Module Resources
Below are key resources you can use to follow along with this module.
- NHL 2026 Prospects Dataset (Curated from Elite Prospects)
- Top Prospects Data Analysis (Microsoft Excel)
- Top Prospects Data Analysis (R Code)
All module resources can be found at our Data Punk Media GitHub repo folder.
Published Story
Below are links out to the story we chose to create and publish using the analysis.
- TBD
- TBD
Check out our Stories page for more deep dives on different data stories.
Key Takeaways
The below summarizes learnings and takeaways that you can apply to your own storytelling projects.
1. You need to be careful with equivalency scores
This is especially so when you're translating across so many leagues to express and represent potential at the NHL level. The NHLe is not perfect; it's directional. And you can find discrepancies in the way each league is scored. So, if you choose to rely on this within your model, be sure to note this as an assumption.
2. There isn't a lot of data when it comes to prospects
This is unfortunate because it's an important part of gauging evolution from the minors into the pros. You saw that we a 'small data' problem with, for example, a low number of Games Played for some of the players. Arguably, you'd want to see 20+ games to get a sense for trend and player performance.
3. Attack the data from different angles
You saw in this module how if we were to approach the data singly from the Adjusted Points per Game angle, this may ignore other important elements (e.g., statistical bias from a lower number of games played, high PIMs indicating potential disciplinary issues, etc.). And when you do use different methods, there are points of conflation that are more qualitative. This is where you expertise and understanding of the game come into play.
Additional Resources
Here are some additional resources we hope you'll find useful.
- SHL vs the NHL
- About the NHLe (And Other Methods of Equivalency)
- What is a Z-Score?
- What is K-Means Clustering?
Up Next
In our next module, we'll be focused on What Makes a Superbowl Team?