#116 Greater Gary Goblins X (12-13)

avg: 741.16  •  sd: 65.12  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
164 Fifty-Fifty** Win 13-4 737.74 Ignored Aug 4th Heavyweights 2018
120 KC SmokeStack Win 13-9 1130.31 Aug 4th Heavyweights 2018
129 Prime Win 13-6 1244.69 Aug 4th Heavyweights 2018
29 Big Wrench Loss 8-13 881.69 Aug 5th Heavyweights 2018
61 Tanasi Loss 7-13 538.39 Aug 5th Heavyweights 2018
117 THE BODY Win 13-8 1227.1 Aug 5th Heavyweights 2018
124 Wisconsin Hops Loss 10-13 368.21 Aug 18th Cooler Classic 30
136 Pipeline Win 13-9 999.38 Aug 18th Cooler Classic 30
60 DeMo Loss 11-13 867.74 Aug 18th Cooler Classic 30
47 MKE Loss 7-13 666.85 Aug 18th Cooler Classic 30
95 Scythe Win 17-16 1004.07 Aug 19th Cooler Classic 30
121 BlackER Market Win 15-8 1271.27 Aug 19th Cooler Classic 30
73 Greater Gary Goblins Y Win 7-6 1140.8 Aug 19th Cooler Classic 30
170 Bird Patrol Win 13-6 625.47 Sep 8th Central Plains Mens Sectional Championship 2018
31 Black Market** Loss 4-13 746.73 Ignored Sep 8th Central Plains Mens Sectional Championship 2018
121 BlackER Market Loss 6-13 106.46 Sep 8th Central Plains Mens Sectional Championship 2018
73 Greater Gary Goblins Y Loss 8-13 519.64 Sep 8th Central Plains Mens Sectional Championship 2018
123 Satellite Win 12-11 826.15 Sep 9th Central Plains Mens Sectional Championship 2018
- BlackEST Market Win 15-6 799.92 Sep 9th Central Plains Mens Sectional Championship 2018
119 MomINtuM Loss 7-15 112.34 Sep 9th Central Plains Mens Sectional Championship 2018
7 Chicago Machine** Loss 0-13 1244.93 Ignored Sep 22nd Great Lakes Mens Regional Championship 2018
51 BroCats Loss 1-13 589.85 Sep 22nd Great Lakes Mens Regional Championship 2018
65 Mango Tree Loss 6-13 444.17 Sep 22nd Great Lakes Mens Regional Championship 2018
104 Black Lung Loss 10-15 355.12 Sep 23rd Great Lakes Mens Regional Championship 2018
119 MomINtuM Win 15-10 1165.94 Sep 23rd Great Lakes Mens Regional Championship 2018
**Blowout Eligible

FAQ

The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)