#39 Lotus (16-5)

avg: 1528.95  •  sd: 69.85  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
153 DR** Win 15-4 1420.51 Ignored Jun 10th Bay Area Ultimate Classic 2023
134 Firefly** Win 15-1 1497.39 Ignored Jun 10th Bay Area Ultimate Classic 2023
221 Moonlight Ultimate** Win 15-1 966.77 Ignored Jun 10th Bay Area Ultimate Classic 2023
47 Donuts Win 14-8 1979.41 Jun 11th Bay Area Ultimate Classic 2023
41 BW Ultimate Win 15-12 1800.77 Jun 11th Bay Area Ultimate Classic 2023
35 LIT Ultimate Win 12-11 1688.08 Jun 11th Bay Area Ultimate Classic 2023
230 Birds of Paradise** Win 15-3 905.44 Ignored Jul 15th TCT Select Flight West 2023
36 Tower Win 10-9 1677.94 Jul 15th TCT Select Flight West 2023
43 California Burrito Win 12-11 1618.76 Jul 16th TCT Select Flight West 2023
64 Minnesota Star Power Win 13-7 1840.89 Jul 16th TCT Select Flight West 2023
11 Seattle Mixtape Loss 9-13 1486.6 Aug 26th Northwest Fruit Bowl 2023
63 Pegasus Loss 10-11 1163.52 Aug 26th Northwest Fruit Bowl 2023
44 Classy Win 11-10 1584.93 Aug 26th Northwest Fruit Bowl 2023
54 Lights Out Loss 10-13 1014.99 Aug 26th Northwest Fruit Bowl 2023
47 Donuts Loss 6-13 843.38 Aug 27th Northwest Fruit Bowl 2023
63 Pegasus Win 13-6 1888.52 Aug 27th Northwest Fruit Bowl 2023
165 Octonauts** Win 13-3 1366.23 Ignored Sep 9th 2023 Mixed So Cal Sectional Championship
145 Family Style** Win 13-5 1439.74 Ignored Sep 9th 2023 Mixed So Cal Sectional Championship
43 California Burrito Win 12-8 1934.91 Sep 9th 2023 Mixed So Cal Sectional Championship
133 Karma Win 13-6 1498.56 Sep 9th 2023 Mixed So Cal Sectional Championship
18 Lawless Loss 10-13 1424.42 Sep 10th 2023 Mixed So 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)