October 21, 2018

Finally got the opportunity to upload some of the draft tools I used this year to Github. I used a combination of a PostgreSQL database full of player stats, and a Jupyter Notebook for data wrangling and visualization.

Two plots which I found quite helpful in my drafting:

  • This shows the 2017 vs 2018 relative performance of the top 10% of players in the league. Very helpful in identifying potential bargains, or overvalued players.

fhgraph1

  • For a given goalie this shows the past three years performance (W, GAA, SV%). This came in very helpful during the actual draft when I had to quickly compare multiple goalies.

fhgraph2

In the past I largely used Excel to do this type of analysis. What I realized this year is that for any non-trivial type of data analysis like this, Python is much, much, much, easier to use.