The Hockey-Graphs Podcast Episode 5: Mock Off-Season Part 1

Adam Stringham was joined by: Chris Watkins, Namita Nandakumar, Garik16, Shayna Goldman and Carolyn Wilke to redo the 2017 NHL off-season!

Here are the rules that we used for the mock off-season:

  • The rosters were rolled back to the start of free agency.
  • The Salary Cap has gone up 10% to 82.5M
  • Max contract length is 5 years
  • There are player and team options (so Connor McDavid could sign a 4 year contract with a player option for the 5th year)
  • All no trade clauses are void, and teams can go over the cap to sign their own players (up to $90M)
  • There is no compensation for offer sheets. the team can either match or the player walks for free.

Any comments are appreciated, the goal is to produce a podcast that people want to hear. Please subscribe to the podcast on iTunes!

The Hockey-Graphs Podcast Episode 3: Expansion Draft and GM Rankings

Welcome back to The Hockey-Graphs Podcast! Our third episode showcases Chris Watkins and Carolyn Wilke‘s recent articles; The 2017 NHL GM Report Cards (Parts 1, 2 and 3. Given the leaks that came out prior to the expansion draft; what is Las Vegas’ strategy? Are good general managers always good or do they excel in some areas and struggle in others? We discuss those questions and more in this episode of The Hockey-Graphs Podcast. Any comments are appreciated, the goal is to produce a podcast that people want to hear. Please subscribe to the podcast on iTunes!

The Hockey-Graphs Podcast Episode 2: Risk Aversion and the Draft

Welcome back to The Hockey-Graphs Podcast! Our second episode showcases Namita Nandakumar‘s recent articles; Exploiting Variance in the NHL Draft and Who’s Getting Drafted This Year?. Namita and Adam discuss: draft theory, risk aversion, player potential, central scouting rankings and how to best apply her research moving forward. Any comments are appreciated, the goal is to produce a podcast that people want to hear. Please subscribe to the podcast on iTunes!

FQG: Cumulative Hits in the Conference Final

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Throughout the playoffs (quarterfinals, semifinals), I have analyzed whether a team’s hits for and against were indicative of their success. Studying a team’s Corsi for percentage per game and expected goals for per game alongside their cumulative hits can help us spot high-level trends.

We’re seeking to determine the accuracy of the narrative that many hockey traditionalists love – that a team must increase their hitting to succeed in their quest for the Stanley Cup. This has been studied in recent seasons, including 2014-15 season, 2015 playoffs, and 2016 playoffs, yet no decisive correlation was found between a team’s increased hitting and success. So far in the first two rounds of the playoffs, this seems to hold true.

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How Indicative are hits in the 2017 Stanley Cup Playoffs: Semifinals

After the conclusion of the 2017 Stanley Cup Quarterfinals, I looked at whether a team’s hits for and against were indicative on their play. By looking at a team’s Corsi for percentage per game and expected goals for per game, against their cumulative hits as their first round progressed, it could be observed whether a team’s production dropped due to being outhit.

As it was explained in the first part of this series, many hockey traditionalists point to an increased number of hits as a necessity to compete for the Stanley Cup. There is a preconceived notion by some hockey minds that a team will become worn out if they are consistently outhit in the playoffs and subsequently will not be able maintain their production. However, in the 2014-15 season, 2015 playoffs, and 2016 playoffs, no decisive correlation was found between success and hits.

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Identifying Team Playing Styles with Clustering

Last time, I looked at individual playing styles by clustering players together based on various passing metrics. Today, I’m going to use a similar approach to identify team playing styles and what we can learn about them. I got the idea watching this video on NBA offensive styles (stick tap to @dtmaboutheart for the link). It’s been sitting in my unfinished pile for a while, but I was spurred on to finish it by some comments made about the Washington Capitals and Pittsburgh Penguins series, which I will delve into tomorrow. Today’s piece is to going provide examples of how passing metrics can provide more detailed and actionable scouting reports for a team’s offensive and defensive tendencies.

All data is form 5v5 situations and is either from the Passing Project or Corsica.

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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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