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#1
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Re: pic: Top 10% OPRs compared via standard deviation distribution - 2008-2015
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#2
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Re: pic: Top 10% OPRs compared via standard deviation distribution - 2008-2015
Here are the two histograms again, but this time I've clipped the top of the scale so we can focus on the "tails" (the 50 or so teams at the top and bottom of the distribution). I also screwed up the last plot and didn't show 2056 with an OPR at ~6 SD above the mean. 2056 and 1114 look lonely out there
![]() https://drive.google.com/file/d/0B16...ew?usp=sharing |
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#3
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Re: pic: Top 10% OPRs compared via standard deviation distribution - 2008-2015
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#4
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Re: pic: Top 10% OPRs compared via standard deviation distribution - 2008-2015
Not really, it is easier to accidentally hurt your own alliance this year than it was last year.
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#5
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Re: pic: Top 10% OPRs compared via standard deviation distribution - 2008-2015
G40 would like a word with you.
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#6
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Re: pic: Top 10% OPRs compared via standard deviation distribution - 2008-2015
You're absolutely right, I had forgotten about penalties. I wonder how these graphs would compare if the OPRs from last year incorporated penalties.
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#7
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Re: pic: Top 10% OPRs compared via standard deviation distribution - 2008-2015
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So I go back to my original comment that the low end spread is quite interesting. I think it might be reflective of how difficult it is for a newer or less experienced team to contribute to the game, but I didn't think they could detract so much. BTW, there are special statistical properties to include when running regressions with a continuous dependent variable (score) and 0-1 dummy variables (i.e., whether a team is present on the alliance). I haven't looked at that issue for quite a while so I don't remember much beyond that but it is a consideration in the OPR estimation. |
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#8
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Re: pic: Top 10% OPRs compared via standard deviation distribution - 2008-2015
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What I am curious about is an alternate OPR calculation for last year which would use the following formula in the match score matrix instead of the nominal score: adjusted score = (nominal score) - (penalty points incurred by opposing alliance) + (penalty points incurred by this alliance) Actually, I do remember uOPR (unpenalized OPR) being calculated last year. Was this the method of obtaining that? Or did it just subtract out all penalty points incurred without adding any back in? |
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#9
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Re: pic: Top 10% OPRs compared via standard deviation distribution - 2008-2015
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http://www.chiefdelphi.com/media/papers/download/4037 Column headings are as follows: Code:
L2s Final Score OPR L2f Foul OPR L2h Hybrid OPR L2t TeleOp OPR L2os Opponent's Final Score OPR L2of Opponent's Foul OPR L2oh Opponent's Hybrid OPR L2ot Opponent's TeleOp OPR As Average Final Score Af Average Foul Ah Average Hybrid At Average TeleOp Aos Opponents Average Final Score Aof Opponents Average Foul Aoh Opponents Average Hybrid Aot Opponents Average TeleOp uOPR unpenalized OPR = L2s - L2f uDPR unpenalized DPR = L2os - L2of uCCWM unpenalized CCWM = uOPR - uDPR EPA uOPR - (As - Af) M Matches Played |
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#10
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Re: pic: Top 10% OPRs compared via standard deviation distribution - 2008-2015
There are more teams this year than ever before. The 1% is getting bigger and more elite ever year.
More than anything I think this statistic is showing the complete absence of defense from this years game. |
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#11
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Re: pic: Top 10% OPRs compared via standard deviation distribution - 2008-2015
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See my points above: It's not only lack of defense but the particular difficulty of gaining the highest point activities. There's been posts on that issue in other threads. |
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#12
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Re: pic: Top 10% OPRs compared via standard deviation distribution - 2008-2015
Thank you, that is indeed very helpful. I will play around more with this after my team competes this weekend.
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#13
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Re: pic: Top 10% OPRs compared via standard deviation distribution - 2008-2015
G40 didn't reduce your OPR though. It gave points to your opponents instead of taking away your own hard-earned points. You also were unable to de-score yourself by knocking over stacks, since it was pretty hard to get the ball to go backwards back through the goal after your scored.
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