Florida Panthers

Friday Quick Graphs: Total Player Charts, Revived

Benjamin Wendorf Friday Quick Graphs, NHL League-Wide Analysis, Total Player Chart Tags: , , , , , , , , , , , , , Leave a comment

Bringing back an older concept…a few years ago, I was spurred by Tom Awad’s “Good Player” series to put together these radar charts of player ice-time. I’d always felt, for fantasy hockey purposes, it is important to know the boxcars (goals, assists, points) come from the ice-time as much as anything, and so the initial creation of what I called “Total Player Charts,” or TPCs, was to portray precisely that. It ended up that they gave intriguing portrayals of players that we felt had strong seasons. See Jamie Benn’s above; an Art Ross Trophy, sure, and much of it came from near the top share of playing time at evens and on the powerplay, league-wide. You can also get a sense of just how valuable a defenseman like T.J. Brodie is:

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2014-15 Season Preview: The Atlantic Division

Jared NHL League-Wide Analysis Tags: , , , , , , , , , , , , , , , , , , , , , , Leave a comment

Image from Sarah Connors via Wikimedia Commons

Finishing last season with an average of 87.6 points per team, the Atlantic/Flortheast Division was the worst in the NHL. I see that gap widening, not narrowing, in 2014-15.

The battle at the top of the division will, in my eyes, come down to two teams: the Boston Bruins and the Tampa Bay Lightning. The Bruins have placed either first or second in their division (the Atlantic or the former Northeast) in each of the past four seasons. The 2nd place Lightning finished a full 16 points behind the Bruins in 2013-14, but a strong off-season combined with a full season of Steven Stamkos and rookie Jonathan Drouin potentially making an impact has them near even money with the Bruins.

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Revisiting the NHL Regression Predictions from January 1st

Benjamin Wendorf NHL League-Wide Analysis Tags: , , , , , , , , , , Leave a comment

Photo by “User:Zucc63” via Wikimedia Commons, modified by author

If you’ll remember, one of the inaugural posts here was a regression prediction piece, using a combination of PDO and Fenwick Close to see who might improve or decline over the latter half of the season. I decided to put together a table of the teams I predicted would negatively or positively regress, just using the aforementioned data:

If you’ll remember, I pegged Anaheim, Colorado, Montreal, Phoenix, Toronto, and Washington for negative regression, and Florida and New Jersey for positive regression. So, even with really rudimentary predictors, this season I was able to be fairly successful building predictions from a half-season sample for the remaining season. In previous years, the fancy stats folks usually picked the much more obvious targets (Toronto being the big one this year), but it’s very possible to go further if you wanted.

Outperforming PDO: Mirages and Oases in the NHL

Benjamin Wendorf NHL League-Wide Analysis, Theory Tags: , , , , , , , , , , , 3 Comments

Above is the progressive stabilization (game-by-game, cumulatively) of all-situations PDO over time for the 30 NHL teams. It’s a demonstration of the pull of PDO towards the average (1000, or the addition of team SV% and shooting percentage with decimals removed), and it gives you a sense of the end game: an actual spread of PDO, from roughly 975 to roughly 1025. In other words, if you were just to use this data, you could probably conclude that it’s not outside expectations for a team to outperform 1000 by about 25 (or 2.5%) on either side.

That’s all well and good, but PDO is a breakdown of two very different things, a team’s shooting and goaltending, two variables that understandably have very little to do with each other (they are slightly related because rink counting bias usually affects both). Shooting percentage can hinge on a number of contextual variables, though its reliance on a team’s player population usually can bring it a bit in-line with league averages. Save percentage, on the other hand, hinges on one player, and what’s more past performances suggest that a single goaltender can quite significantly outperform expectations. In this piece, I want to jump into the sliding variables of PDO, and what we can expect from teams, but first I want to begin with why I’m working with all-situations PDO.

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Crystal Blue Regression: Leafs, Avalanche, Ducks, Among the Most Likely to Regress in 2014

Benjamin Wendorf NHL League-Wide Analysis, NHL Topical Analysis Tags: , , , , , , , , , 3 Comments

Image

Picture taken by Sarah Connors, posted to Flickr – via Wikimedia Commons

With the Winter Classic coming up, or should I say the Winter Classics since the NHL handles marketing success like the kid who found the cookie jar, we also ring in the rough middle of the season. It’s a time for reflection, maybe a chance to re-assess your decisions, lifestyles; and if you’re analyzing the NHL, it’s the perfect time to recognize trends that may or may not continue. Also known as “regression,” here I’m dealing with a concept everyone understands to a degree; you invoke it when you see a friend sink a half-court shot in basketball and say, “Yeah, bet you can’t do that again.” The trend, supported by a history of not making half-court shots, suggests that it is unlikely for your friend to sink the half-court shot, even if they recently made one. In the NHL, possession stats like Corsi are considered better predictors of future success than stats that can be influenced more greatly by luck, like goals (and, consequently, wins), shooting percentage, or save percentage. Much like your friend and their half-court shot, there are teams that are defying their odds (established by possession measures) to succeed, which can easily happen with less than a half-year of performance.

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