#127 Nomads (15-12)

avg: 1051.02  •  sd: 64.13  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
147 DINGWOP Win 8-6 1240.61 Jun 18th Spirit of the Plains
192 Minnesota Superior B Win 7-6 853.26 Jun 18th Spirit of the Plains
204 Loaded Panda Loss 9-11 393.08 Jun 18th Spirit of the Plains
219 THE BODY Win 13-5 1131.59 Jun 24th Spirit of the Plains
135 Trident II Win 10-9 1148.67 Jun 24th Spirit of the Plains
46 DeMo Loss 4-13 963.65 Jun 24th Spirit of the Plains
148 Minnesota Superior A Loss 4-11 329.49 Jun 25th Spirit of the Plains
253 Scoop** Win 13-5 648.49 Ignored Jul 8th Heavyweights 2023
106 MKE Loss 11-13 953.57 Jul 8th Heavyweights 2023
194 UFO Win 13-6 1316.15 Jul 8th Heavyweights 2023
55 Colonels Win 13-9 1922.26 Jul 8th Heavyweights 2023
130 Diesel Loss 9-13 622.1 Jul 9th Heavyweights 2023
106 MKE Loss 8-9 1057.41 Jul 9th Heavyweights 2023
90 HouSE Loss 9-13 877.98 Jul 9th Heavyweights 2023
96 Bux Loss 10-11 1163.86 Aug 19th Cooler Classic 34
192 Minnesota Superior B Win 13-11 957.1 Aug 19th Cooler Classic 34
156 NOMAD Win 12-11 1014.95 Aug 19th Cooler Classic 34
241 Middleton High School** Win 13-3 849.82 Ignored Aug 19th Cooler Classic 34
147 DINGWOP Win 15-9 1455.6 Aug 20th Cooler Classic 34
143 STL Moonar Win 9-6 1385.34 Aug 20th Cooler Classic 34
135 Trident II Loss 9-13 605.11 Aug 20th Cooler Classic 34
73 Knights of Ni Loss 8-13 889.69 Sep 9th 2023 Mens Northwest Plains Sectional Championship
191 DCVIII Win 13-9 1154.15 Sep 9th 2023 Mens Northwest Plains Sectional Championship
29 Mallard** Loss 5-13 1128.26 Ignored Sep 9th 2023 Mens Northwest Plains Sectional Championship
245 Milwaukee Revival** Win 13-3 800.48 Ignored Sep 10th 2023 Mens Northwest Plains Sectional Championship
156 NOMAD Win 15-10 1343.56 Sep 10th 2023 Mens Northwest Plains Sectional Championship
73 Knights of Ni Loss 11-15 1004.68 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)