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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