#161 DR (13-12)

avg: 784.62  •  sd: 47.2  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
49 Donuts** Loss 2-15 796.52 Ignored Jun 10th Bay Area Ultimate Classic 2023
138 Firefly Loss 10-12 615.57 Jun 10th Bay Area Ultimate Classic 2023
224 Moonlight Ultimate Win 15-7 927.66 Jun 10th Bay Area Ultimate Classic 2023
39 Lotus** Loss 4-15 895.04 Ignored Jun 10th Bay Area Ultimate Classic 2023
202 Air Throwmads Win 14-8 1051.66 Jun 11th Bay Area Ultimate Classic 2023
181 VU Loss 8-13 134.5 Jun 11th Bay Area Ultimate Classic 2023
224 Moonlight Ultimate Win 11-6 874.35 Jul 8th Revolution 2023
181 VU Win 7-6 755.66 Jul 8th Revolution 2023
202 Air Throwmads Loss 7-8 390.63 Jul 8th Revolution 2023
113 Shipwreck Loss 8-9 877.23 Jul 8th Revolution 2023
215 Quails Win 15-4 1019.03 Jul 9th Revolution 2023
160 Spoiler Alert Win 10-8 1048.35 Jul 9th Revolution 2023
224 Moonlight Ultimate Win 12-5 927.66 Aug 12th Flower Power 2023
230 Birds of Paradise Win 11-2 865.05 Aug 12th Flower Power 2023
247 Erosion** Win 13-4 635.95 Ignored Aug 12th Flower Power 2023
230 Birds of Paradise Win 12-8 706.21 Aug 12th Flower Power 2023
67 Robot Loss 5-14 638.99 Aug 13th Flower Power 2023
121 Party Wave Loss 5-9 438.83 Aug 13th Flower Power 2023
113 Shipwreck Win 13-11 1231.07 Aug 13th Flower Power 2023
36 BW Ultimate Loss 6-13 911.22 Sep 9th 2023 Mixed Nor Cal Sectional Championship
33 Tower** Loss 4-13 948.37 Ignored Sep 9th 2023 Mixed Nor Cal Sectional Championship
181 VU Loss 10-11 505.66 Sep 9th 2023 Mixed Nor Cal Sectional Championship
224 Moonlight Ultimate Win 13-5 927.66 Sep 9th 2023 Mixed Nor Cal Sectional Championship
138 Firefly Loss 9-10 728.69 Sep 10th 2023 Mixed Nor Cal Sectional Championship
202 Air Throwmads Win 11-9 764.83 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)