#63 Pegasus (9-9)

avg: 1288.52  •  sd: 60.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
112 Pushovers Win 13-10 1350.14 Jul 15th TCT Select Flight West 2023
47 Donuts Win 11-9 1692.59 Jul 15th TCT Select Flight West 2023
230 Birds of Paradise** Win 13-5 905.44 Ignored Jul 15th TCT Select Flight West 2023
60 Cutthroat Loss 9-12 964.92 Jul 16th TCT Select Flight West 2023
41 BW Ultimate Loss 6-10 1004.11 Jul 16th TCT Select Flight West 2023
11 Seattle Mixtape Loss 8-13 1409.01 Aug 26th Northwest Fruit Bowl 2023
44 Classy Loss 8-12 1018.77 Aug 26th Northwest Fruit Bowl 2023
39 Lotus Win 11-10 1653.95 Aug 26th Northwest Fruit Bowl 2023
32 Kansas City United Loss 8-13 1147.27 Aug 26th Northwest Fruit Bowl 2023
43 California Burrito Loss 11-13 1264.91 Aug 27th Northwest Fruit Bowl 2023
39 Lotus Loss 6-13 928.95 Aug 27th Northwest Fruit Bowl 2023
77 Seattle Soft Serve Win 13-8 1684.97 Sep 9th 2023 Mixed Washington Sectional Championship
200 Surge** Win 13-5 1149.77 Ignored Sep 9th 2023 Mixed Washington Sectional Championship
171 BOP Win 15-9 1252.03 Sep 9th 2023 Mixed Washington Sectional Championship
31 Spoke Loss 8-15 1081.21 Sep 9th 2023 Mixed Washington Sectional Championship
79 Bullet Train Win 12-11 1303.25 Sep 10th 2023 Mixed Washington Sectional Championship
54 Lights Out Win 13-11 1571.97 Sep 10th 2023 Mixed Washington Sectional Championship
31 Spoke Loss 10-15 1192.42 Sep 10th 2023 Mixed Washington 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)