#221 Moonlight Ultimate (3-20)

avg: 366.77  •  sd: 73.15  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
47 Donuts** Loss 1-15 843.38 Ignored Jun 10th Bay Area Ultimate Classic 2023
153 DR Loss 7-15 220.51 Jun 10th Bay Area Ultimate Classic 2023
39 Lotus** Loss 1-15 928.95 Ignored Jun 10th Bay Area Ultimate Classic 2023
198 Air Throwmads Loss 8-11 190.64 Jun 11th Bay Area Ultimate Classic 2023
134 Firefly Loss 7-11 430.49 Jun 11th Bay Area Ultimate Classic 2023
179 VU Loss 7-12 151.26 Jun 11th Bay Area Ultimate Classic 2023
211 Quails Loss 8-9 335.09 Jul 8th Revolution 2023
153 DR Loss 6-11 273.82 Jul 8th Revolution 2023
179 VU Loss 6-10 175.61 Jul 8th Revolution 2023
151 Spoiler Alert Loss 4-14 224.59 Jul 9th Revolution 2023
230 Birds of Paradise Win 10-8 568.11 Jul 9th Revolution 2023
153 DR Loss 5-12 220.51 Aug 12th Flower Power 2023
244 Erosion Win 8-6 375.28 Aug 12th Flower Power 2023
134 Firefly Loss 5-12 297.39 Aug 12th Flower Power 2023
35 LIT Ultimate** Loss 1-13 963.08 Ignored Aug 12th Flower Power 2023
165 Octonauts Loss 10-11 641.23 Aug 13th Flower Power 2023
198 Air Throwmads Win 12-5 1156.25 Aug 13th Flower Power 2023
151 Spoiler Alert Loss 5-12 224.59 Aug 13th Flower Power 2023
153 DR Loss 5-13 220.51 Sep 9th 2023 Mixed Nor Cal Sectional Championship
62 American Barbecue** Loss 2-13 689.71 Ignored Sep 9th 2023 Mixed Nor Cal Sectional Championship
60 Cutthroat** Loss 3-13 710.28 Ignored Sep 9th 2023 Mixed Nor Cal Sectional Championship
15 Mischief** Loss 2-13 1222.59 Ignored Sep 9th 2023 Mixed Nor Cal Sectional Championship
211 Quails Loss 8-9 335.09 Sep 10th 2023 Mixed Nor Cal 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)