Measuring Playoff Excitement

Most of us are by now familiar with the concept of win probability. The current state of the game has many implications on the way the game is played and I’ve been a proponent of using it to adjust statistics as an alternative to using just the score, since win probability itself is simply a function of score and time remaining.

In the spirit of the playoffs today I want to use win probability and corresponding statistic leverage to measure ‘excitement’. Leverage is the total win probability added (and for the opposing team, lost) on account of a particular goal. If a team scores a goal in the last second of the third period, the win probability added would be about 0.5: they went from essentially 0% to 50% chance of winning the game.

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A New Look at Aging Curves for NHL Skaters (part 2)

In part 1 of this series, I looked at how NHL skaters age using the delta method with Dawson Sprigings’ WAR model. As mentioned in my previous article, there is still one major problem with the delta method that needs to be addressed: survivorship bias. The “raw” charts presented in part 1 are quite informative, but they’re missing a correction for this bias. Before we can draw conclusions about what this new WAR metric tells us about NHL skater aging, we need to figure out how to correct for survivorship bias.

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Friday Quick Graphs: Marginal Gains for Defenders

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Last Friday we asked how many goals is improving a team’s first line worth versus their fourth line? What about defenders?

The above graph shows the number of goals over a season a team should expect in improving their player’s shot differential talent, here described in percentiles of talent.

The blue line is first pair with 2nd, 3rd, pairs falling next with red and yellow.

The blue line is the steepest, suggesting that moving from a 55th percentile player to 60th percentile player on the top pair will improve a team’s goal differential more so than a second or third pairing player. (This is not to be confused with improving from a 55% Corsi player to a 60% Corsi player)

Notice how the difference between the top and middle pair is pretty negligible. Improving from an average (median, 50th percentile) to the absolute best in both top and middle pair defenders is only about half a goal difference in improvement. This effect may be due to the fact that teams often place their second best defender on the second pair, whether that may be due to strategy and design or due to handedness “forcing” the team’s hand.

A reminder that the coefficients we found for forwards were 0.24, 0.12, 0.12, and 0.06. This may seem to suggest improvement should be concentrated for top forward line, followed by the top-four defenders, and then middle-six forwards with the bottom pair. However, our method is agnostic of usage and who drives shot differentials more, forwards or defenders.

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

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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How good is Columbus? A Bayesian approach

Columbus has been surprisingly good this year. As of this writing, the Blue Jackets are first in the league in points and goal differential with games in hand. Remember: Columbus, in terms of preseason predictions, was pegged as more like a 5-8 finisher in the Metropolitan division (e.g. see here, here, here, here, and here).

That said, it’s still early. If it might take 70 games for skill to overtake randomness in terms of contribution to the standings, and if teams like the 2013-14 Avalanche and 2013 Maple Leafs (to name two prominent examples) can fool us for so many games, it doesn’t seem so unbelievable that a team could do it over just 32. (And the Blue Jackets aren’t the only example this year, either–Minnesota is under 48% possession and has a 103+ PDO right now.)

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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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#1MinuteTactics – Studying The World Cup Of Hockey

When you bring the best players and coaches together, entertaining things happen. Not only that, but many of the tactical habits employed by elite hockey team are actually not so hard to grasp.

Here are five teaching points brought to us by The World Cup Of Hockey 2016, broken down and served up in just over one minute apiece:

1) Transition Play: Team North America’s Neutral Zone Mastery

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#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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