Author: Ryan Stimson

Expected Primary Points are a better predictor of future scoring than Shots, Points

Ryan Stimson Passing Data, Shot Quality 3 Comments

While I have spent a lot of time over the last several months digging into how we can quantify passages of play and inform better tactical decisions, it’s time to revisit how passing impacts scoring at the player level. We have only been using half of the picture in terms of individual shots and goals for player evaluation. Sure, we have primary and total points, but primary assists aren’t a very useful metric. The rate at which players create shot assists also appeared to have significantly more value than a player’s own shots in some analysis I did last year.

This piece will release individual passing data for the 2014 – 2015, 2015 – 2016, and 2016 – 2017 seasons, the latter of which tracked by Corey Sznajder, the former tracked by myself and many others. However, it is important to provide context and meaning to the numbers rather than simply inundate you with data.

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Analyzing One-Timers: The Most Dangerous Shot in the Bag

Ryan Stimson Uncategorized Leave a comment

Very little has been written about one-timers because, surprise, the NHL doesn’t track it. However, this is something we’ve been tracking for the last couple of seasons and it is worth a short post to investigate the value in this type of shot. Additionally, it is also worthwhile to dig into whether or not it is a skill to set up a one-timer for a teammate, or if it is strictly a shooter shoot. Lastly, is this type of shot more predictive than ordinary slap shots? Deflections? The standard wrist shot?

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Predicting Shot Differentials for NCAA Players

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While there has been an increase in the type of data that’s available to us on prospects, we are still lacking across all developmental leagues. More importantly, and this is particularly true for the NCAA, player-level data still eludes us, even when there is team-level data present. To get at the context with which a player performs and the factors governing his or her environment, we are left with estimates of things like ice time and quality of competition/teammates.

While this hasn’t stopped us from making advances to enhance traditional scouting and prospect analysis, having player-level shot metrics would be a wonderful piece of data to have when evaluating their performance. This article will look at a method to predict those numbers.

Special thanks to DTMAboutHeart and Matt Cane for their feedback and guidance at certain steps in this process.

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Introducing the 2016 – 2017 Forechecking Project

Ryan Stimson Forechecking Data Leave a comment

Passing and Zone Entries are so last year.

When Corey Sznajder decided to track microstats for the upcoming season and began incorporating my passing concepts into his work on last season’s playoffs, I wondered if we really needed to track this season. Instead, Corey and I chatted a bit and decided the best use of everyone’s time would be if myself and the other passing project volunteers continued to work on last season*, with the hope that we can build a solid sample by the time Corey finishes the 2016 – 2017 season. Having two (nearly) full seasons of data would be excellent to have.

However, this also gave another idea to explore something we really haven’t done a lot of: forechecking.

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Just How Important is Quality of Competition? Very. Also, not much. It’s All Relative.

Ryan Stimson Exploring Context, NHL League-Wide Analysis, Practical Concerns 2 Comments

*This post is co-authored by DTMAboutHeart and Ryan Stimson*

Recently, the topic of Quality of Competition has been at the forefront of Hockey Twitter. This post hopes to articulate some of the nuance associated with Quality of Competition, as well as Quality of Teammate, metrics and how impactful they are. To do that, we will revisit methods outlined here by Eric Tulsky, namely splitting the competition and teammate quality by position and measuring the impact of each split. Ryan recently wrote about this at the NCAA level, but it has not been looked at with much rigor at the NHL level.

Both Quality of Competition and Quality of Teammates matter. They also don’t matter. It depends on the position and metric you’re looking at. All TOI data is 5v5 and from Corsica. Ryan had the game files of who was on the ice during each 5v5 shot from Micah Blake McCurdy, so that data was used as well. Also, thanks to Muneeb for feedback during this process. Thanks to all!

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Tactalytics: Defending the Pass

Ryan Stimson Passing Data 2 Comments

half_ice_overload_1

At the 2nd Annual Hockey Analytics Conference at the Rochester Institute of Technology, Matt Cane and I presented on new ways of measuring and evaluating defensive play. We used the passing data from my project to take a look. I believe Matt will write up his half of the presentation, so this post will focus on my half of the presentation. You can access our slides and view our presentation here. I will go into greater detail in this post since I am without time constraints. All of the data we used can be accessed here. Everything in this post deals with 5v5 play.

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#RITHAC Slides, Video, & Recap

Ryan Stimson Uncategorized 1 Comment

On Saturday, September 10th, the 2nd Hockey Analytics Conference at the Rochester Institute of Technology was held. It was a huge success and this post has the slides for each presenter, as well as video for most of the day. We had some technical problems early on, but most of the event was recorded. There is also footage of the #RITHAC Cup that was held immediately following the conference (stick tap to Conor Tompkins for Periscoping the event).

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Why Deterrents Are Irrelevant

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Hits. Long-forgotten when it comes to hockey analytics and for good reason. It’s been established in many places, even on this site by Garret Hohl, that how often you hit your opponent carries little worth when it comes to predicting goals. Sure, there will always be a play here or there that works out, but by and large hits are noise. Yet, that doesn’t stop teams from shelling out big money for players that can score and hit, despite all evidence to the contrary that the latter is noise. So, why bring it up? Well, someone retweeted this into my timeline Wednesday night and I couldn’t get it out of my head.

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The Distribution of QoC/QoT at the NCAA Level

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It is accepted that measures of Quality of Competition and Teammates have varying effects at the NHL level. Over time, the distribution of a player’s QoC will mirror that of many others. His QoT is another matter. This is nothing new and has been looked at on this site here by petbugs, here by Garret, and here by Dom.

However, where these measures likely have greater impact is at the Junior, European, and NCAA levels where the disparity in talent between the best and worst teams is greater than at the NHL level. In this article, I’m going to clearly show  just that, and articulate why this is important when evaluating prospects as it adds significant context to their performance.

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Tactalytics: Using Data to Inform Tactical Neutral Zone Decisions

Ryan Stimson Neutral Zone Analysis, NHL League-Wide Analysis, Passing Data, Theory, Uncategorized 3 Comments

Breakout - Against 2-1-2...2

Last time, I showed how using data and video evidence can be combined to inform tactical offensive zone decisions. Today, I’m going to do the same thing in the neutral zone. Neutral zone play is something that has been a hot topic among analysts for many years, going back to this paper written by Eric Tulsky, Geoffrey Detweiler, Robert Spencer, and Corey Sznajder. Our own garik16 wrote a great piece covering neutral zone tracking. Jen Lute Costella’s work shows that scoring occurs sooner with a controlled entry than an uncontrolled entry.

However, for all the work that goes into zone entries, there have been few efforts to account for how predictable these metrics are. At the end of the day, what matters is how we can better predict future goal-scoring. Also, in looking at our passing data, what can we also learn about how actions are linked when entering the zone? Does simply getting into the offensive zone matter? Does it matter whether it’s controlled or not? Or, does what happen after you enter the zone matter exponentially more? Lastly, what decisions can we make to improve the team’s process using this data?

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