Ypointis I’m a professional data scientist who has developed performance measures.
in hockey points scored over time creates bias relative to the start point of this. For example pittsburgh and Washington and Chicago had multiple top 5 picks who were top 20 in points and ppg. Those high picks skew results giving credit to team scouting when it was f***ing dumb luck they got the high picks when they did.
A strong defensive minded player who might be under 0.5 ppg rate but shuts down opposing top center is just as important.
on the defensive side. A strong shut down Dman us just as important as a o e dimensional off dman. It’s rare you have ones that are both.
which is why I score players based on expectation of what you get when you picked them. Picking top 5 did you get a top line player. Thus us not a redraft of p,Ayers as if they were the best because other factors play out like system fit, injuries, team depth and roles.
with a team with two strong off Dman one likely loses out on PP time where only one on top PP unit which will affect point production. But both are viewed as equally good players but in points by D of which 50% or more come from PP will result in one to clearly have the points edge.
Lets look at Danault, Brodin, McDavid, Carlson, Bergeron, Hedman, Price, Vasilevskiy: The point you are trying to make here is the weighted factors should be tinkered with (like 30 for forwards and 50 for D for example). But what you fail to realize is these don't skew the final team rankings as much as you think they do. Trust me, I played with it for a bit to see how the results would vary and a few teams move up and down but nothing substantial. The better drafting teams we orginally thought always rise to the top of the rankings. This new system pretty much verifies the subjective expert opinions on who the better drafting teams are. What it does not factor in is GM management (Trades and UFA signings).
However, if you want to provide different weighted factors in the formula below, let me know and I'll adjust them. Then I'll repost the new rankings to show you how things move around. Take the two formulas below and show me your weighted factors you would like to see? And what range of years you want to look at. The goalie weights appear to be low after posting this.
Draft Power Formula: #1 OA(x100) + 2-5(x50) + 6-10(x25) + 11-31(x15) + 32-75(x5) + 76-125(x2) + 126-224(x1) |
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Rating Score Formula: Games(x1) + Goalie Wins(x25) + Goalie Saves(x0.25) + Forward Pts(x30) + Defense Pts(x50) / Draft Power |
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Danault (Last 3 seasons):
* 205 games played. 205 x 1 = 205 pts
* 124 pts. 124 x 30 = 3720 pts
* Total value = 3925
Brodin (Last 3 seasons):
* 204 games played. 205 x 1 = 204 pts
* 69 pts. 69 x 50 = 3450 pts
* Total value = 3654
McDavid (Last 3 seasons):
* 198 games played. 205 x 1 = 198 pts
* 318 pts. 318 x 30 = 9540 pts
* Total value = 9738
Carlson (Last 3 seasons):
* 201 games played. 201 x 1 = 201 pts
* 189 pts. 189 x 50 = 9450 pts
* Total value = 9651
Bergeron (Last 3 seasons):
* 180 games played. 180 x 1 = 180 pts
* 183 pts. 183 x 30 = 5490 pts
* Total value = 5670
Hedman (Last 3 seasons):
* 190 games played. 190 x 1 = 190 pts
* 154 pts. 154 x 50 = 7700 pts
* Total value = 7890
*** Looks like the goalie stats weights are a bit low but we have to remember, it's equally applied to all goalies on all teams. Vasi is ahead of Price by a fair amount and that's how it should be
Price (Last 3 seasons):
* 149 games played. 149 x 1 = 149 pts
* 74 wins. 74 x 25 = 1850 pts
* 3980 saves. 3980 x 0.25 = 995 pts
* Total value = 2994
Vasilevskiy (Last 3 seasons):
* 147 games played. 1497x 1 = 147 pts
* 105 wins. 105 x 25 = 2625 pts
* 4201 saves. 4201 x 0.25 = 1050 pts
* Total value = 3822