Playoff Primer Part 2/2 (Regular Season Success)

Happy NHL Playoffs Eve!

If you haven’t checked out part 1 of the primer, you’re missing out on an exploration of NHL home ice advantage in the regular season v. playoffs. In part 2, I’m going to be exploring the relationship between regular season success and playoff success across the NHL, NBA and NFL. In other words, how much “parity” do we see between the playoffs and regular season? In this case, what I mean by “parity” is the relationship between regular season and playoff success. I capture this phenomenon by looking at the average regular season winning% of playoff teams in the NFL, NBA and NHL (data courtesy of hockey, baseball and football reference):

Average Regular Season Winning% of Playoff Teams

What does this mean? Since the NFL only has 12 total playoff teams compared to the 16 of the NBA and NHL, it is to be expected that the average NFL playoff team is well above average. However, when we compare the NHL and NBA over the past 18 years, the average NBA playoff team tended to be better than the average NHL playoff team – until 2012. This trend shifted quite drastically in 2012, with the average NHL playoff team outperforming the average NBA playoff team in the regular season.

What if we limit our scope from all playoff teams to the regular season winning% of conference finalists (the final-4):

Regular Season Winning% of Final-4

While the NFL still reigns supreme with the highest average regular season winning% of their Conference Championship participants, they did fall below average twice in the past 18 years (2008 and 2010). Just to refresh your memory, the 2008 NFL NFC Championship featured the 9-6-1 Philadelphia Eagles and 9-7 Arizona Cardinals. That year, the final-4 boasted a combined 65% winning percentage, lowest in the league’s past 18 years, but still more than the average NHL Conference Finals team. Speaking of which, the NHL was below average (and at the bottom of the list) every season except 2012. This featured the (somewhat) anti-climatic Conference Finals that included two #1 seeds (Pittsburgh and Chicago), the 4th best team in the league (Boston) and Los Angeles. In other words, the NHL appears to have the most unpredictable outcomes in the playoffs of these three leagues. But wait there’s more…It’s time for the main event: The difference between regular season winning% of the average final-4 team relative to the average playoff team.

Difference Between Regular Season Winning% of Final-4 and Average Playoff Team

The impact of this chart is to identify the “parity” within each of the leagues by calculating the difference in regular season winning% of final-4 teams to their respective leagues’ average winning% of all playoff teams. There’s a few conclusions that we can draw from this chart: (1) The NBA is the most predictable sport in this regard. This should come as no surprise to anyone that follows basketball – the best teams seem to almost always advance to the conference finals, with 2 notable exceptions: 2006 and 2009. In 2006, while Detroit and Cleveland occupied the top-2 seeds in the East, there was a major shocker out west with the 8th seeded Warriors upsetting the Mavs in the first round. Something similar happened in 2009, with the top-seeded Cavs upset in the second round by Boston and the 2nd-seeded Mav’s getting dunked on (pun intended) by the Spurs in the first round. While a couple of 1/2/3 seeds being upset isn’t really that exciting for the NHL or NFL, for the NBA it’s the only evidence of relative unpredictability that we have. (2) The NHL has the most unpredictability in the playoffs of any sport. It is rare (like 2012) that successful regular season teams advance to the conference championship. On four different occasions (2009, 2011, 2013 and 2016), the average final-4 team was actually worse than the average NHL playoff team. The only other league to accomplish this feat was the NFL, who holds the record for most chaotic playoff of the past 18 years in 2008. (3) Speaking of the NFL, there’s a lot more variation than other sports, , which is a product of a single-elimination playoff format.

In sum: The NHL and NBA are more predictable (for very different reasons). The regular season is less predictive of a deep playoff run in the NHL and more so for the NBA. Whereas, the NFL is just a total crap shoot year-to-year. With 24 teams in the NHL Playoffs this year, we should actually expect some chaos based on what we’ve seen in previous seasons.

Playoff Primer Part 1/2 (Home Ice Advantage)

Welcome to Stanley Cup Playoffs Week!

Beginning on Saturday, August 1 at 12:00 PM EST, the New York Rangers and Carolina Hurricanes kick off the insanity of this year’s COVID Olympics, also known as the Stanley Cup Playoffs. All games are being played at Rogers Place (Edmonton) and Scotiabank Place (Toronto), with Western teams playing at the former and Eastern teams at the latter. That means that only two teams will actually enjoy all the luxuries of “home ice advantage” – Toronto and Edmonton. The rules regarding home ice advantage will remain the same as any other season, with the higher seed afforded the benefits of “home ice advantage” in 4 games of the best-of-7 series (3 of the 5 in the first round).

These benefits include:
(1) Last change – The home team makes player substitutions after the visiting team between stoppages of play
(2) Faceoffs – The centre from the visiting team must put their stick on the ice first
(3) Rink familiarity

Continue reading “Playoff Primer Part 1/2 (Home Ice Advantage)”

Annual Rate Charts (Introduction)

I debuted the Annual Rate Charts (ARCs) on twitter over a month ago and since I’m unleashing the Tableau to the public, I wanted to ensure proper documentation was available.

You can find the link to the interactive Tableau Charts HERE!

What are Annual Rates Charts?

Annual Rate Charts (ARCs) are a way to measure the production and expected production of a player relative to their time on the ice. Production is quantified using the Goals Above Replacement (GAR) and Expected Goals Above Replacement (xGAR) models from Evolving-Hockey. If you want an in-depth analysis of how GAR is calculated, documentation is available here. In short, GAR is a single number that captures the contribution of that player in different game situations. GAR is subdivided into several categories, including even strength offense & defense, power play, penalty kill, takeaways and faceoffs. The number for each player represents the number of goals more (positive) or less (negative) that a player contributions relative to a replacement-level player (by position).

Expected GAR (xGAR) is also a single number assigned to each player, which is calculated based on the on-ice performance of a player (including rates, quality, shooting and goaltending). This number is the expected number of goals more or less above replacement-level that the player should contribute, based on their on-ice actions. In other words, xGAR represents the performance of a player, while GAR represents the results of that performance. When I tested the relationship between these values, I find that xGAR captures approximately 86% of the variation in GAR, which is quite substantial for a model including human subjects.

Annual Rate Charts (ARCs) can be differentiated from many other publicly available visualizations using GAR and xGAR data as it is relative to the amount of time a player is on the ice. These rates can be differentiated from many other popular visualizations that  display GAR data in aggregate form. There are strengths and weaknesses to both rate and aggregate data, but both are useful in their own way.

Continue reading “Annual Rate Charts (Introduction)”

New Podcast Coming Soon!

I was blessed with an opportunity to join The Hockey Podcast Network to do an original content podcast on NHL Analytics. Episodes of the Ice Analytics Podcast will be released every Friday beginning on December 27. This podcast will posit one NHL-related question each week and explore the answer using the available data. I will also be joined by a guest from the hockey or statistics community to get an insider prospective on these topics. Show notes, including data sources and visualizations, will be available on StatsEnforcer.com

I hope you find these topics to be as informative as I do!

Follow me on twitter @IceAnalytics.

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GAR and Cap (2018-2019): Defensemen (cont.)

Following up from the previous post here, which examined the best (and worst) GAR performers in each game situation, this article will examine defensemen value relative to cap hit. For more information on how GAR is calculated, please check out the introduction. Before delving into the nuances of player value, I present an illustration of the cap hit and GAR of all defensemen:

GAR per Dollar (D)

Continue reading “GAR and Cap (2018-2019): Defensemen (cont.)”

GAR and Cap (2018-2019): Defensemen

The first positional group of interest is defensemen, who will be presented in the three different game situations (Even Strength, Power Play and Short Handed). Before diving into the GAR values for individual defensemen, be sure to check out the introduction, which outlines the process for data collection and team-aggregate values. The following charts illustrate the GAR of defensemen in different game situations and time-on-ice:

Continue reading “GAR and Cap (2018-2019): Defensemen”

Objective Thoughts Concerning Nylander

I apologize in advance that I am even covering this whole spectacle. Anyone living in/near the GTA is probably exhausted from all the speculation surrounding William Nylander. All that being said, I wanted to present the situation in an objective fashion with certain conditions that can be logically understood. It should be noted that I am not arguing that the Leafs trade Nylander, but merely presenting a thought experiment based on two factors: (1) age and (2) AAV.

Continue reading “Objective Thoughts Concerning Nylander”