#27 Omen (11-9)

avg: 1739.79  •  sd: 67.53  •  top 16/20: 0.6%

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# Opponent Result Game Rating Status Date Event
6 Ring of Fire Win 15-11 2561.82 Jul 15th TCT Pro Elite Challenge East 2023
31 Garden State Ultimate Win 15-12 1976.61 Jul 15th TCT Pro Elite Challenge East 2023
25 Mad Men Win 15-10 2210.88 Jul 15th TCT Pro Elite Challenge East 2023
28 Tanasi Loss 9-10 1607.45 Jul 16th TCT Pro Elite Challenge East 2023
8 Johnny Bravo Loss 12-15 1812.28 Aug 4th 2023 US Open Club Championships ICC
5 Chicago Machine Loss 11-15 1808.54 Aug 4th 2023 US Open Club Championships ICC
14 Sockeye Loss 10-15 1546.27 Aug 5th 2023 US Open Club Championships ICC
39 Pittsburgh Temper Win 15-13 1838.89 Aug 19th TCT Elite Select Challenge 2023
13 Vault Loss 10-15 1550.75 Aug 19th TCT Elite Select Challenge 2023
26 Sprout Win 15-11 2126.93 Aug 19th TCT Elite Select Challenge 2023
4 Chain Lightning Loss 7-15 1611.89 Aug 20th TCT Elite Select Challenge 2023
19 Sub Zero Loss 10-15 1411.98 Aug 20th TCT Elite Select Challenge 2023
14 Sockeye Win 14-13 2124.88 Aug 20th TCT Elite Select Challenge 2023
47 Beacon Loss 9-13 1144.67 Sep 23rd 2023 Great Lakes Mens Regional Championship
126 Blink-122 Weekends Win 13-7 1611.92 Sep 23rd 2023 Great Lakes Mens Regional Championship
228 Mischief** Win 13-1 1066.63 Ignored Sep 23rd 2023 Great Lakes Mens Regional Championship
117 Chimney** Win 15-2 1719.57 Ignored Sep 23rd 2023 Great Lakes Mens Regional Championship
75 Flying Dutchmen Win 14-13 1502.54 Sep 24th 2023 Great Lakes Mens Regional Championship
76 Haymaker Win 15-9 1892.64 Sep 24th 2023 Great Lakes Mens Regional Championship
5 Chicago Machine Loss 7-15 1589.71 Sep 24th 2023 Great Lakes 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)