- Dec 2, 2007
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Why is Stamkos never going to play more than 73 games in a season again when he's never missed a game with an injury until this year?
I will bump this thread in 16 years. Setting an alarm on my phone for 2030.
Why is Stamkos never going to play more than 73 games in a season again when he's never missed a game with an injury until this year?
I don't get the point of this when it is so obviously, wildly wrong.
I gave plenty of constructive criticism in my previous post, thank you very much. Including numerous variables that were ignored, and numerous assumptions made that I don't think can be made in any proper model.Perfect is the enemy of good. What's wrong with someone liking the numbers side of things, and wanting to give something a try?
We prefer constructive criticism in this subforum if you have any to give.
I wasn't aware this was the "you must love and agree with all forms of extremely flawed or worthless statistics and models" forum. If you're making a model, the BASE requirement should be that it actually makes sense in reality.
If you're talking about the special rules, I did read them, and I did follow them.Read the two forum-specific rules here:
http://hfboards.mandatory.com/showthread.php?p=78276171
Like I said, feel free to offer constructive feedback.
Not do all the things I pointed out earlier, and then some?So how would you improve upon it, with actionable items?
I suggested scrapping THIS model, not all models. I'm not aware of the method that you speak of, but I'm sure there are models that would have (not necessarily good, but still) SOME predictive power that people could find a use for.Thank you.
You had me 100% until the "scrap the whole thing" idea.
I disagree with the notion that "no matter what you do", you won't have any useful predictive power. Hockey Prospectus' VUKOTA method has pretty good predictive power, for one.
I suggested scrapping THIS model, not all models. I'm not aware of the method that you speak of, but I'm sure there are models that would have (not necessarily good, but still) SOME predictive power that people could find a use for.
It's just not this one, and that's the point I was making. Unless you change this one so much that it's essentially an entirely different model, it has no use.
He was wrong right from the beginning, since he took an average of 50 random players to try and project out for elite players, which would bring different variables and trends. He was also wrong right from the start because he tried to "average" out GP and injuries.
Now as for how I'd do it (and roughly how I think that VUKOTA works)...
For a given player at age X, I would develop a similarity score that describes how well that player's data matches up against other players through age X. There would be an art to this, of course - for projecting the future, what "matters" in terms of similarity? Ignoring the fact that many of the things that would be important are likely not measured (or not measured well).
Suppose that the player matches up well against Player A (92% similar), Player B (89% similar) and Player C (85% similar).
I'd then take the composite of Player A, B, and C's age X+1 bodies of work (weighted by those percentages) and that would be my middle of the road projection. With enough players in the sample (and there would be more than 3 "similar" players for most comparisons), one could also estimate a variance and a confidence interval.
I've been working on something like this for goaltenders for awhile now, and it's actually what led me to develop my "opponent strength" and "game-to-game variability" metrics (described in other threads in this forum) as potential inputs for the similarity scores.
I think you would want way more than 3 "similar" players, if possible.