#20 Zyzzyva (14-7)

avg: 1860.21  •  sd: 70.78  •  top 16/20: 16.3%

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# Opponent Result Game Rating Status Date Event
60 Switchback Win 12-10 1713.9 Jun 24th Summer Solstice 2023
11 Furious George Loss 9-10 1932.44 Jun 24th Summer Solstice 2023
14 Sockeye Loss 7-13 1442.34 Jun 24th Summer Solstice 2023
33 Blackfish Win 11-10 1791.59 Jun 25th Summer Solstice 2023
86 Oregon Trainwreck Win 13-7 1869.46 Jun 25th Summer Solstice 2023
18 Dark Star-D Loss 10-13 1555.15 Jun 25th Summer Solstice 2023
4 Chain Lightning Loss 10-14 1813.19 Aug 19th TCT Elite Select Challenge 2023
12 Raleigh-Durham United Win 13-12 2131.62 Aug 19th TCT Elite Select Challenge 2023
25 Mad Men Win 13-10 2085.42 Aug 19th TCT Elite Select Challenge 2023
7 DiG Loss 10-15 1713.91 Aug 20th TCT Elite Select Challenge 2023
13 Vault Loss 11-14 1691.02 Aug 20th TCT Elite Select Challenge 2023
22 SoCal Condors Win 13-12 1977.4 Aug 20th TCT Elite Select Challenge 2023
152 Journeymen** Win 15-6 1513.71 Ignored Sep 9th 2023 Mens Nor Cal Sectional Championship
211 Ursa** Win 15-5 1175.38 Ignored Sep 9th 2023 Mens Nor Cal Sectional Championship
107 Ghost** Win 15-2 1781.81 Ignored Sep 9th 2023 Mens Nor Cal Sectional Championship
70 OAT Win 15-10 1853.07 Sep 10th 2023 Mens Nor Cal Sectional Championship
240 Bonsoon** Win 15-1 850.91 Ignored Sep 23rd 2023 Southwest Mens Regional Championship
66 OC Crows Win 14-9 1910.75 Sep 23rd 2023 Southwest Mens Regional Championship
3 Revolver Loss 9-15 1729.82 Sep 24th 2023 Southwest Mens Regional Championship
78 Drought Win 15-9 1889.59 Sep 24th 2023 Southwest Mens Regional Championship
22 SoCal Condors Win 15-12 2152.9 Sep 24th 2023 Southwest Mens Regional 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)