#RITHAC Slides, Video, & Recap

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

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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Behind the Numbers: Why statistic-folks are sometimes assholes, UNjustifiably

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.

Here we go. Here is part two, to what really should have been part one in hindsight prior to this piece, which would have saved me some of the backlash on Twitter, as the point was frequently misunderstood. (And while we’re dealing with hindsight, the title was part of the misunderstanding as well.)

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Behind the Numbers: Why statistic-folks are sometimes assholes, justifiably

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.

It does not take long for Hockey Twitter to complete one full rotation on its typical life-cycle of subjects. The same debates come up on shot quality, grit and leadership, eye-test versus numbers, and how statistics should be used in player evaluation again and again.

These debates often come to an impasse. Sometimes they even deviate into ad hominem and red herrings. There are parties guilty on both sides, as one would (and should) expect there to be “assholes” in every demographic.

But why is the prerogative for being nice always on the “stats guys”? Why are the “analytics guys” the only ones needing to change their ways to make things better? Why is it that only one side is discussed to be less cordial than the other?

Why does this hypocrisy exist?

I have a theory on this.

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Weighted Shot Rates Based on the Passing Project

The Passing Project headed by fellow Hockey Graphs contributor Ryan Stimson (@RK_Stimp) is one of the most exciting things happening in hockey analytics. The project consists of dozens of volunteers manually tracking the passes that lead to shot attempts in games across the NHL. Thus far, the project has compiled data for nearly five hundred games. Ryan laid the groundwork for analyzing the project’s data in his piece here. That pieces discusses primary shot contributions (PSCs), which is a counting stat comprised of shots and shot assists. Shots in this article is synonymous with corsi meaning all shots and not just shots on goal. Ryan has built on that original work with a piece on offensive zone play here and a piece on neutral zone play here. And following his line of thought, I wrote a piece for NHL Numbers that expanded on his approach to offensive zone analysis.

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The Distribution of QoC/QoT at the NCAA Level

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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Behind the Numbers: Results matter in the end

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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.

Player tracking, what is it good for? Absolutely nothing.

Okay, that’s a lie. A catchy lie though, especially if you are into late-60s / early-70s soul. It would also be pretty disingenuous of myself, since helping run the technical side of a tracking company is my day job.

However, whether you call it individual player tracking, microstatistics, or whatever, there seems to be some misunderstanding to some on what the numbers being measured represent and what they should do with this information.

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Quick Post: Do Past Sv% Variables Predict Future Sv% Variables?

The usefulness of on-ice save percentage (and derivative metrics such as Sv% Rel and Sv% RelTM) has been the source of many, many heated debates in the analytics blogosphere. While many analysts point to the lack of year-over-year repeatability that these metrics tend to show (past performance doesn’t predict future performance very well) as evidence of their limitations, others (primarily David Johnson of HockeyAnalysis.com) have argued that there are structural factors that haven’t been accounted for in past analyses that artificially deflate the year-to-year correlations that we see.

David’s point is a fair one – a lot can change about how a player is used between two samples, it’s not unreasonable to think that those changes could impact the results a player records. But we don’t just have to speculate about the impact those factors have – we can test the impact, by building a model that includes measures of how these factors have changed and seeing how it changes our predictions.

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10 Rules Of Thumb For Hockey Analysts

  1. The point of hockey is to create goal differential. The point of hockey analysis is to find ways to improve it.

  2. Shot differentials today is goal differentials tomorrow.

  3. 100-10-1. 100 minutes of your time to create data, 10 minutes of the coach’s time to digest the data, 1 minute of the player’s time to absorb the data.

  4. Optimise workload, reduce uncertainty.

  5. If your findings are either always or never surprising, then review your methodology. 80/20 (in favor of confirming existing beliefs) is a good place to start.

  6. Your priority is to help the coach get a better night’s sleep and to help players maximise their experience.

  7. Know your place, but stand your ground.

  8. We are all on the same team.

  9. Who you compete against influences your results; who you work with dictates your destiny.

  10. The job is only done when you’ve trained someone to make you expendable.

 

Jack Han is the Video & Analytics Coordinator for the McGill Martlet Hockey team (not his full-time job). He also writes occasionally about the NHL for Habs Eyes on the Prize. You can find him on Twitter or on the ice at McConnell Arena.

Tactalytics: Using Data to Inform Tactical Neutral Zone Decisions

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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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