How good is Columbus? A Bayesian approach

Columbus has been surprisingly good this year. As of this writing, the Blue Jackets are first in the league in points and goal differential with games in hand. Remember: Columbus, in terms of preseason predictions, was pegged as more like a 5-8 finisher in the Metropolitan division (e.g. see here, here, here, here, and here).

That said, it’s still early. If it might take 70 games for skill to overtake randomness in terms of contribution to the standings, and if teams like the 2013-14 Avalanche and 2013 Maple Leafs (to name two prominent examples) can fool us for so many games, it doesn’t seem so unbelievable that a team could do it over just 32. (And the Blue Jackets aren’t the only example this year, either–Minnesota is under 48% possession and has a 103+ PDO right now.)

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Making Better Hockey Graphs

Visualization seems pretty easy, so it’s often left as an afterthought. But visuals can be an immensely effective—or destructive—form of communication. To that end, many, if not most, people fail to tap into its power because of they make prominent mistakes. (Sorry for the ego blow, homies.).

But that need not be the case. Although visualization is a process, not a result, once you know what to look for, you can easily cut down on those big mistakes and make graphs that—while not perfect—will be consistently good.

Here are a few things to keep in mind for hockey bloggers, adapted from Andy Kirk as well as Dieter Rams’ 10 principles of good design.

For our purposes, they can be summed up as “think about your readers while recognizing your practical limitations.”

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