Research shows that lateral/”east-west” puck movement in the offensive zone is beneficial to increasing one’s odds of scoring. But I have now heard from people in various positions within the hockey industry on why it might also be useful to generate east-west puck movement in the neutral zone. The theories – focused on lateral passing, lane changes and stretch passes, respectively – all boiled down to one point: When you rush the puck up ice, the defending team will focus on that side, leaving the other side of the ice somewhat more open, so there might be open ice to exploit.Continue reading
Month: October 2019
Passing clusters: A Framework to Evaluate a Team’s Breakout
Quick breakouts – trying to move the puck out of your zone right after gaining possession – make up roughly 38% of possessions and account for 22% of all shots and 22.4% of Expected Goals (at least according to my possession and xG definitions). Therefore, understanding what does and does not work when breaking out the puck against present forecheckers is important. There is evidence that passes from the defensive half boards by wingers inside produce more offense than those straight up ice. But the puck is more often recovered elsewhere, so these passes by wingers aren’t the first pass in a possession and are therefore presumably influenced by the previous play. It should be interesting to find out how the inclusion of the pass(es) that came before affects this conclusion.Continue reading
A crowdfunding initiative to promote diversity at the Columbus Analytics Conference
I’ve been fortunate enough to be able to attend the last three years of the RIT Sports Analytics Conference. The first year I went, I was nervous to meet people whose work I admired. I was afraid that nobody would want to talk to this new person that few people knew and who was just starting to learn about the field.
I could not have been more wrong.
Exploratory Data Analysis Using Tidyverse
This post assumes beginner knowledge of R.
Welcome to the second article in our series on basic data cleaning and data manipulation! In this article, we’re going to use play-by-play data from two NHL games and answer two questions:
- which power play unit generated the best shot rate in each game?
- which defenseman played the most 5v5 minutes in each game?
In the process of doing so, we’ll cover several topics of basic data manipulation in the tidyverse, including using functions, creating joins, grouping and summarizing data, and working with string data.Continue reading