Are Teams getting Lucky on Rushes?

Introduction

This game was played on January 25th, 2017 between the Vancouver Canucks and the Colorado Avalance at Pepsi Center. In this 2-on-1 rush, Loui Eriksson, #21, carries the puck up the ice. Nikita Tryamkin, #88, outskates Mikko Rantanen, #96, to get to the net and create an dangerous opportunity while the lone defender, Cody Goloubef, #18, sprawled to futilely prevent the pass.

When you look at this offensive rush, do you ever wonder about the numbers behind it? For example, is the number of shots that were preceded by passes repeatable over an entire season? What about shooting percentages? If they are repeatable, do zones of the primary pass (the pass preceding a shot) influence this repeatability? What about rebounds and rebound shooting percentages (the goals scored from rebounds)?

Terminology

In hockey, “odd-man rushes” is a term frequently used to refer to offensive attacks such as the above where the attacking team has more players than the defending team. In my analysis, I will be slightly deviating from this jargon and instead use “odd-player rushes”, which consist of shots that were preceded by passes and taken on breakaways, 2-on-1, 3-on-2, etc. Any shots that are not rush shots with a player advantage are categorized as “all_other_shots”.

In the later parts of this analysis, I will be using the terms, “rebound shot” and “rebound shooting percentage”. The first indicates a shot on goal following a rebound and the second is calculated as rebound goals (goals that follow rebounds) divided by rebound shots.

 

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Public Ballots May Be Changing Award Voting Behavior

My office was recently planning an offsite social event. During a team meeting, we brainstormed what activity to do together. Along with ideas like mini golf, hiking, and wine tasting, someone suggested karaoke. The team initially responded positively, so when everyone turned to me, I said “sure, that sounds fun”. Then someone put the options in a Google Form for us to all vote on privately. I opened it at my desk and immediately voted for karaoke dead last. I didn’t want to be a downer in public, but there was no way I was doing karaoke.

Being in public changes our behavior. It’s a natural trait and totally understandable. What’s interesting is understanding when and how it changes, and the NHL awards voting may have given us an opportunity to do just that. For the 2017-2018 season, the Professional Hockey Writers Association (PHWA) made their individual voter ballots public for the first time, and it appears that this may have affected how some writers voted.

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Comparing Scoring Talent with Empirical Bayes

Note: In this piece, I will use the phrases “scoring rate” and “5on5 Primary Points per Hour” interchangeably.

Introduction

Nico Hischier and Nikita Kucherov are both exceptional talents but suppose, for whatever reason, we want to identify the superior scorer of the two forwards. A good start would involve examining their 5on5 scoring rates in recent seasons…

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Evidently, Hischier has managed the higher scoring rate but that doesn’t convey much information without any notion of uncertainty – a significant issue considering that the chunks of evidence are of unequal sizes. It turns out that Kucherov has a sample that is about three times larger (3359 minutes vs. 1077 minutes) and so it is reasonable to expect that his observed scoring rate is likely more indicative of his true scoring talent. However, the degree to which we should feel more comfortable with the data being in Nikita’s favor and how that factors into our comparison of the two players is unclear.

The relationship between evidence accumulation and uncertainty is important to understand when conducting analysis in any sport. David Robinson (2015) encounters a similar dilemma but instead with regards to batting averages in baseball. He presented an interesting solution which involved a Bayesian approach and more specifically, empirical Bayes estimation. This framework is built upon the prior belief that an MLB batter likely possesses near-league-average batting talent and that the odds of him belonging to one of the extreme ends of the talent spectrum are less likely in comparison. As evidence is collected in the form of at-bats, the batter’s talent estimate can then stray from the prior belief if that initial assumption stands in contrast with the evidence.  The more data available for a specific batter, the less weight placed on the prior belief (the prior in this case being that a batter possesses league-average batting talent). Therefore, when data is plentiful, the evidence dominates. The final updated beliefs are summarized by a posterior distribution which can be used to both estimate a player’s true talent level and provide a sense of the level of uncertainty implicit in such an estimate. In the following sections we will walk through the steps involved in applying the same empirical Bayes method employed by Robinson to devise a solution to our Hischier vs. Kucherov problem.

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Linking penalties and game minute in the NHL

By Ingrid Rolland and Michael Lopez

At the 10-minute mark of the first period during Game 2 of Tampa Bay’s 2nd-round series with Boston, Torey Krug was sent to the box for two minutes after committing this slashing violation against Brayden Point.

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The Lightning cashed in on the ensuing power play, with Point scoring the game’s opening goal.

Fast forward to later in this same game, with Tampa Bay clinging to a 3-2 advantage and less than four minutes remaining in regulation. Brad Marchand skated past Anton Stralman for a scoring chance, and the Lightning defender reached around to commit what looked to be a similar violation to the one deemed a penalty on Krug above.

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No penalty on Stralman was called, however, and Tampa Bay held on for a 4-2 win. It was the first of the team’s four consecutive triumphs over the Bruins that earned the Lightning a spot in the Eastern Conference Final.

“We hate to harp on the ref’s, but tonight they deserved to get harped on,” opined NBC’s Jeremy Roenick after the game. “How can you call [Krug’s] penalty early in the game, in such a big playoff game?”

 

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NHL Scoring Trends, 2007-08 to 2017-18: Is the League Getting More Competitive?

Photo by Bobby Schultz, via Wikimedia Commons

Though it was completely tangential to @SteveBurtch’s line of thinking, his brief comments pondering the competitiveness between the middle of NHL lineups yesterday (which I can’t locate now, natch) got me thinking about whether the NHL and team management has gotten any more efficient or competitive overall the last decade. With 10 years in the books for complex Corsi data, and hockey’s seeming “Moneyball moment” fully here regardless of the quibbling on social and mainstream media, is the league getting any tighter?

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The Launch of the Tape to Tape Project

Earlier this year, Rushil Ram, Mike Gallimore, and Prashanth Iyer launched Tape to Tape, an online tracking system that can be used to record locations of shot assists, zone exits, and zone entries. Rushil and I will be running the Tape to Tape Project in order to compile a database of these statistics with the application Rushil created. We have already had close to 30 trackers sign up from an announcement on Twitter last week.

 

 

 

Each individual will track zone exits, zone entries, and shot assists for games they sign up for. Once the games are complete, the data will be exported to a public Dropbox folder. The goal with this project is to enhance our understanding of these microstats as they pertain to coaching decisions, player performance, and wins. What follows next is a description of what we will be tracking, a brief summary of the research that describes why these specific microstats are important, and how we will be tracking these events.

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An Introduction to NWHL Game Score

In July of 2016, Dom Luszczyszyn released a metric called Game Score.  Based on the baseball stat created by Bill James (and ported to basketball by John Hollinger) the objective of game score is to measure single game player productivity.

While it’s often easy to compare players across larger sample sizes, comparing two different players’ performance on a given night can be difficult. If player A has a goal, two shots, and took a penalty, did that player outperform player B who had two assists and one shot? Game score attempts to answer that question by weighting each of the actions of each player to give us a single number representing their overall performance in that game.

Unlike Dom, whose main goal was to create a better way to evaluate single game performance, mine was to create a better statistic to evaluate the total contributions of players. There are no advanced metrics, like Corsi For percentage, or even Goals For percentage, available at this time in the NWHL. Because of this, points are the best way to evaluate players, even though other box score stats are available.

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How Much Do NHL Players Really Make?

Under the provisions of the collective bargaining agreement between the NHL and the NHLPA, a player’s cap hit and the salary they are paid can be two very distinct values in any given year. But even when you understand those differences, how much do NHL player actually take home?

Players’ actual earnings are diminished by a number of factors including escrow, agent fees, and taxes. Agent fees can range from 2-6% depending on representation agreements and services rendered. Tax rates vary throughout the NHL depending on the country, state, and city a team and player reside and play in. But of all the deductions from their income, escrow might be considered the greatest annoyance, as it’s a mechanism to ensure that the owners collect a greater share of hockey-related revenues (HRR) than they have in previous collective bargaining agreements (CBA).

So what is escrow, how much does it actually deplete a player’s salary, and why has it contributed to the tensions between players and owners?

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