"They said my flow was lame, they said I had no game
I told 'em all I was finna to blow, they thought I was insane”
- Mike Jones
Law #1: Winning is all that Matters
Everything in hockey boils down to winning; analytics should always tie back to this metric. Regular season performance is the best indicator of postseason success unless you’re the Boston Bruins.
Law #2: Embrace the Randomness
Hockey is inherently chaotic, and goals are largely random events. Outcomes are uncertain, influenced by a myriad of factors including skill, strategy and execution. Even the best teams can't escape the randomness of the game, but embracing it can guide smarter strategies.
Law #3: Expect Mean Reversion
If something is too good or bad to be true, it probably is. A textbook example of this is William Karlsson. Above average player data is more likely to come from lower skill and positive luck than higher skill and negative luck. Smaller data sets have a strong reversion to population mean, while large data sets have a weak reversion to population mean. Still don’t believe me? Take a look at Jordan Binnington.
Law #4: Possession is Power
Teams that control the puck win more games. More shots = more opportunities. Players are deflecting 100 mph slapshots from the point with more accuracy than Ryan Strome has at an empty net. Shot attempts (e.g. Corsi, SAG) predict future success because they show who’s controlling the game, even if they don’t always lead to goals.
Law #5: Context is Critical
Individual stats should always be understood within the team context. Seek out contribution-to-winning team measurements. If individual stats mattered most, Connor McDavid would have a Stanley Cup ring by now. Zone starts matter. Situations matter. Ice time matters. Strength of schedule matters. Hockey stats live within their context. Even in a game where puck possession and goal differential drive success, great goaltending can mask team weaknesses, Nowhere is this more apparent than the New York Rangers. Adjust accordingly.
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