I count only 8 matches not correctly predicted:
64
65
70
72
73
76
78
84
… what’s the 9th one?
*While we’re waiting for Ed to update his superb scouting spreadsheet…
OPR & CCWM World Rankings based on Weeks 1 thru 6 Qual Match data
Weeks1thru6 World OPR&CCWM revA.xls (259 KB)
Weeks1thru6 World OPR&CCWM revA.xls (259 KB)
71 was a tie.
OK, let’s call it 16 out of 24 then 
When I did counts using OPR data and match outcome “predictions”, I always counted ties as wrong.
Unless there’s a confidence interval, I’m not exactly sure how to treat a tie statistically. And labeling a match “too close to call” isn’t any fun. 
Ties are outliers in binomial situations. Because you cant have 3 options for two choices. Which is why ether just excused it and lowered the sample size.
Right, but hypothetically if OPR predictions said the match would be 100-50, and there was a tie 50-50, the OPR prediction is wrong and shouldn’t be excused as a tie.
hypothetically if OPR prediction said the match would be 50.001-49.999, and there was a tie 50-50, should the OPR prediction be considered wrong and not excused as a tie?
Maybe we should start publishing the residual vector (or the covariance matrix?) along with the OPR
*
*
One metric I’ve used to avoid this problem is the mean and standard deviation of the distribution of alliance score residuals. I also considered using winning margin residuals, but decided against.
I’m sure that integer rounding can be excused. 
*FWIW, I calculated OPR using all qual data for weeks 1 thru 5 PLUS week6 Friday, and used that to predict Saturday Qual matches at Crossroads.
It got Matches 65 and 84 right, but got Match 80 wrong.
64
70
72
73
76
78
80
I’m wondering if OPR predictions at the Championship event will be similar; Crossroads had a fairly deep field with an average OPR of about 27.6.
*Max Event OPR achieved by each of the 2,490 teams
Max EventOPR.xls (146 KB)
Max EventOPR.xls (146 KB)
Wait a minute, Ether. How can average CCWM not be zero? For each event, the sum of CCWM should be zero. In most cases, they are. But there are some events that has a small positive or negative number. I think it must be due to round off error. Is that correct? Then how can the average CCWM be -2.11?
What is the match with an OPR residual of 280+?
Take the average of cells Cells R4 thru R2493 on sheet “World Rank” in your spreadsheet.
Back to the drawing board. Thanks for keeping me honest.
I found an equals sign where there should have been a minus sign in one of my AWK scripts.
Here’s a corrected version.
*
Weeks 1-6 OPR&CCWM residuals.xls (733 KB)
Weeks 1-6 OPR&CCWM residuals.xls (733 KB)
It’s not due to rounding error. It’s due to the match schedule.
If you look carefully, you’ll see that the average CCWM is zero only for those events where the schedule had each team playing the same number of times.
When you do World CCWM, this effect is exaggerated.
The CCWM residuals, however, will always sum to zero, regardless of schedule.
Although this data sheet is now slightly old news…what is this black magic that puts my team’s OPR above 341, 365, 233, and 379? :ahh: Last time I checked, 3941 was ranked 262…quite frankly, I am astonished by this considering our current regional ranking. What factors go into OPR that could produce such a result?
The computation of OPR is explained here:
and here:
and here:
and here:
http://www.chiefdelphi.com/media/papers/download/3321
Aha. Makes better sense now. Thanks Ether.