#143 STL Moonar (9-12)

avg: 966.77  •  sd: 75.57  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
130 Diesel Loss 6-13 440.66 Jul 8th Heavyweights 2023
73 Knights of Ni Loss 6-13 785.85 Jul 8th Heavyweights 2023
191 DCVIII Win 12-11 860.58 Jul 8th Heavyweights 2023
135 Trident II Loss 9-13 605.11 Jul 8th Heavyweights 2023
131 NOx Loss 7-13 481.08 Jul 9th Heavyweights 2023
117 Chimney Loss 6-13 519.57 Jul 9th Heavyweights 2023
194 UFO Win 13-8 1212.31 Jul 9th Heavyweights 2023
131 NOx Win 9-7 1317.95 Aug 19th Cooler Classic 34
159 Choice City Hops Win 13-10 1212.79 Aug 19th Cooler Classic 34
219 THE BODY Win 10-8 794.26 Aug 19th Cooler Classic 34
76 Haymaker Loss 2-13 777.16 Aug 19th Cooler Classic 34
170 Rubicon Rapids Win 13-10 1158.33 Aug 20th Cooler Classic 34
106 MKE Loss 6-13 582.41 Aug 20th Cooler Classic 34
127 Nomads Loss 6-9 632.46 Aug 20th Cooler Classic 34
17 STL Lounar Loss 8-13 1413.19 Sep 9th 2023 Mens West Plains Sectional Shampionship
103 Scythe Loss 8-13 706.93 Sep 9th 2023 Mens West Plains Sectional Shampionship
204 Loaded Panda Win 13-8 1138.45 Sep 9th 2023 Mens West Plains Sectional Shampionship
46 DeMo Loss 14-15 1438.65 Sep 9th 2023 Mens West Plains Sectional Shampionship
214 Meadowlark Win 15-4 1148.73 Sep 10th 2023 Mens West Plains Sectional Shampionship
131 NOx Win 15-8 1603.42 Sep 10th 2023 Mens West Plains Sectional Shampionship
103 Scythe Loss 8-13 706.93 Sep 10th 2023 Mens West Plains Sectional Shampionship
**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)