Analytics in Hockey

Number crunching, algorithms, and the future of statistics in Hockey.

I've taken draft class data from 2003 to 2016, with career games played and points, and cup winners from 2004 to 2018 to give a visual representation how each Draft Class compares to each other as well as give a breakdown of which teams have accumulated the most amount of First Liners/Top 4 D/Starters in the last decade of drafting. Feel free to offer thoughts/feedback! Link to the dataset is here if you want to filter to your team specifically or see the names (hover your mouse over the...
As an alternative metric to points-per-game, I've been working on calculating the amount of points a player puts up in each game they play relative to their teammates and adjusting that based off of the player's age. I'm not familiar with any similar metrics in the NHL, but I wouldn't be surprised if one exists.
I thought it would be fun to measure the "value" of a Stanley Cup, year-by-year, as determined by the number of teams competing for it. This idea came about when I was thinking about how the 1950s Habs 5-straight Cups weren't as impressive to me as say; the Hawks 3-in-6. The reason is that I don't personally find it "impressive" that 1 team wins a cup in a small field of 6 teams. I might be underestimating the difficulty in that, but it's moot, really. So with that in mind, I set about...
When "corsi" became a buzz word in hockey, there was definitely a revolution in the types of players teams would employ. Gone were the lumbering defensive defensemen who couldn't move the puck - it was adapt or get out. Gone were many floating goal scorers who let their linemates do all the work. But it seems that we've hit a point where the league has "homogenized" in those aspects. In 2008, the best possession team in the league had a CF% of 58.84% and the worst had 42.85%. This was...
I would like to share with you team rankings based on the ELO rating system. This is purely mathematical based on wins and losses. Each team has a score. That score increases or decreases based on wins and losses. The amount of change is based on the score of the opponent. Eg. If a strong team beats a weak team then they don't gain a lot of points because it is expected. If a weak team beats a strong team then they gain a lot of points and the strong team loses a bunch. It balances out....
I find a problem with basically all studies here (including my own) in that they require a lot of work and time. There are usually many hours of boring research to have to be done, in order to learn and know about many factors leading up to the end results. There also seem to basically always be factors "biasing" things, including (of course) "randomness" or "circumstances".
I have put together a spreadsheet which compares all teams league wide.
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