Behind the Numbers: Scoring first and conditional probability

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.

Not long ago Jason Gregor tweeted about the value of scoring first.

It may be a bit controversial and difficult to get right away, but the value of scoring first is not special. Long ago, Mr. Eric Tulsky, now of the Carolina Hurricanes, showed that the value of scoring first equals the value of any other goal.

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Have you even used a calculator, jock?

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There has been quite a lot of talk over the last couple of days about how unhappy NHL players are with the escrow hit they take on every paycheque.

Unfortunately, when you have a collective bargaining agreement that specifies how hockey related revenues are to be split between owners and players, an escrow account is a necessary evil. Because the players’ share is paid out under the provisions of 700 or so individual player contracts throughout the season, there needs to be a mechanism to reconcile those payments with the total share following completion of the season, when all hockey related revenues have been accounted for.

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Predicting Shot Differentials for NCAA Players

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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League Wide Report for Weeks 1-4

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As a person who learns best by looking at visual representations of data, I have a couple of statistics that I check on a regular basis to hep me calibrate my interpretation of what’s happening around the NHL. I’m going to start sharing them in a regular series that I hope will give a quick overview of how teams are performing around the league. The goal is for this to be a high level view of the basic trends for all 30 teams that will draw attention to specific areas that might need to be explored further. The charts in this article are up to date through last night’s games. All data is via corsica.hockey.

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Behind the Numbers: Why Plus/Minus is the worst statistic in hockey and should be abolished

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.

Hockey’s plus/minus may be the worst statistic in hockey, although there is some debate with goalie statistics not based off of save percentage (like GAA or Win% that just adds a team component to a goalie’s save percentage). It could even be in contention for just the worst statistic in sport.

Now, some people may read that and think I’m simply saying this because I value shot metrics over goal metrics in player evaluations. While I do feel that way, it is only one of a few reasons that that plus/minus fails in being useful.

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