#191 DCVIII (7-13)

avg: 735.58  •  sd: 55.72  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
130 Diesel Loss 10-13 712.52 Jul 8th Heavyweights 2023
143 STL Moonar Loss 11-12 841.77 Jul 8th Heavyweights 2023
73 Knights of Ni Loss 6-13 785.85 Jul 8th Heavyweights 2023
135 Trident II Loss 11-12 898.67 Jul 8th Heavyweights 2023
253 Scoop Win 13-7 606.02 Jul 9th Heavyweights 2023
156 NOMAD Loss 10-11 764.95 Jul 9th Heavyweights 2023
170 Rubicon Rapids Loss 10-11 705.18 Aug 19th Cooler Classic 34
179 Timber Win 12-7 1331.06 Aug 19th Cooler Classic 34
106 MKE Loss 6-13 582.41 Aug 19th Cooler Classic 34
204 Loaded Panda Loss 8-13 146.13 Aug 19th Cooler Classic 34
179 Timber Loss 9-13 391.99 Aug 20th Cooler Classic 34
148 Minnesota Superior A Win 14-13 1054.49 Aug 20th Cooler Classic 34
241 Middleton High School Win 13-8 745.98 Aug 20th Cooler Classic 34
245 Milwaukee Revival Win 13-3 800.48 Sep 9th 2023 Mens Northwest Plains Sectional Championship
73 Knights of Ni** Loss 5-13 785.85 Ignored Sep 9th 2023 Mens Northwest Plains Sectional Championship
127 Nomads Loss 9-13 632.46 Sep 9th 2023 Mens Northwest Plains Sectional Championship
29 Mallard** Loss 4-13 1128.26 Ignored Sep 9th 2023 Mens Northwest Plains Sectional Championship
175 Dinkytown Doughboys Win 15-14 945.2 Sep 10th 2023 Mens Northwest Plains Sectional Championship
219 THE BODY Win 14-12 752.55 Sep 10th 2023 Mens Northwest Plains Sectional Championship
156 NOMAD Loss 12-15 589.46 Sep 10th 2023 Mens Northwest Plains Sectional Championship
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)