Quote:
Originally Posted by Bingo
That's where you lose me.
The stats work in a large sample size.
But they also work in a one game sample size if that's the extent of the conclusion.
You shouldn't take 11 minutes of five on five ice time, and a terrible xGF% split and say the player sucks.
But if you want to say he had a tough night that's valid. If the opposition generates more of league average bad events against you than you generate for you it's likely a tough night. There are nuances for sure, but the objective assignment of expected goals in both directions is from a model. And that model would suggest a tough night if the other team had more instances than you did by a large margin; even in just one game.
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And we'll just have to agree to disagree on the small sample size issue - hockey is much more subject to random bounces than most sports - weird shaped ball, ice, skates, sticks... it all adds up to lots of bounces/luck. And that equates to there being far too much noise in small sample sizes for them to be useful.
You take a sport like baseball... every play is isolated, and started fresh. We can measure a pitcher's velocity, spin rate, release angle, etc. And all of those stats are essentially as valid in small sample sizes as they are in large samples. Same for the hitter - how did they fare against fast balls, sliders. inside, outside? All measurable, and all essentially equally measurable from game to game.
Hockey isn't like that at all. In order to compare shot statistics, you need large samples. Any random player has a one-timer - it could be 60 MPH, or it could be 95. It all depends on how the puck laid down for his stick. Then it might hit the net, it might not, again, largely depends on the positioning of the puck on the ice at contact. So much randomness.
The other factor is the one-game samples are too small. Sometimes we see a player at 25% (or whatever) for expected goals for and against. Sounds like he got caved in. But in reality, he was probably on the ice for only a very few chances either way. And maybe one of his linemates should have shot once, or had a puck bounce over their stick. That one play would have completely changed the player's xGF/A number. One turnover by another player, and the xGF/A gets hammered. Larger sample sizes, these things get averaged out. But in a single game - especially for a low-event team, and even more so for a very low event fourth line, the total scoring chances might be 2-1 for the game. That's a 33% result, where a single bounce would have made it 50%. A single bounce each way, and it's 66%.
All this would be fine (is what it is), if everyone understood that. But they don't. Every game we see "this person had a great game, that person got caved in"