Chris Watkins joined Adam Stringham to discuss some of his new work and Erik Karlsson’s recent comments. Is the NHL entering a new age of superstar transition? Will the leagues best players start jumping around in free agency? Any comments are appreciated, the goal is to produce a podcast that people want to hear. Please subscribe to the podcast on iTunes!
Hockey fans and analysts have always appreciated the importance of passing. But until the passing project led by Ryan Stimson, we couldn’t quantify that importance. His work supported by a team of volunteers and other analysts has established that the passing sequence prior to a shot is a significant predictor of the likelihood of the shot becoming a goal. His work also showed that measuring shots and shot assists combined as shot contributions is a better predictor of future performance for both players and teams than shots alone.
Knowing that, the logical next step is to use passing data in analysis whenever possible. Unfortunately, the NHL does not provide passing data so it must be manually tracked by people like Corey Sznajder. Corey’s work is invaluable and I encourage you to support him but he’s only one person.
This article attempts to estimate a player’s quantity of shot assists in a given sample using publicly available data to help fill in gaps where tracked data doesn’t exist.
By MithrandirMage [CC BY-SA 3.0], via Wikimedia Commons
Every once-in-a-while I will rant on the concepts and ideas behind what numbers suggest in a series called Behind the Numbers, as a tip of the hat to the website that brought me into hockey analytics: Behind the Net.
Hey! Remember me?
I work full-time for (slash help run) HockeyData, a data tracking and analysis company. Because of this conflict of interest, it limits what I can and cannot talk about. The good news is I can still talk generalities, the basics behind analytical thinking in hockey, and other peoples’ good work, which fits my Behind the Numbers series.
Why have there been so few updates then? Been busy (…lazy).
One generality I’d like to rant about is how we look at and evaluate statistics and models: how meaningful different numbers are and why we view them that way.