#181 VU (10-17)

avg: 630.66  •  sd: 58.71  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
202 Air Throwmads Win 11-10 640.63 Jun 10th Bay Area Ultimate Classic 2023
36 BW Ultimate** Loss 3-15 911.22 Ignored Jun 10th Bay Area Ultimate Classic 2023
37 LIT Ultimate** Loss 3-15 909.2 Ignored Jun 10th Bay Area Ultimate Classic 2023
94 Mango Loss 7-15 477.18 Jun 10th Bay Area Ultimate Classic 2023
161 DR Win 13-8 1280.78 Jun 11th Bay Area Ultimate Classic 2023
224 Moonlight Ultimate Win 12-7 848.17 Jun 11th Bay Area Ultimate Classic 2023
161 DR Loss 6-7 659.62 Jul 8th Revolution 2023
160 Spoiler Alert Win 9-8 910.69 Jul 8th Revolution 2023
230 Birds of Paradise Loss 6-8 -35.44 Jul 8th Revolution 2023
224 Moonlight Ultimate Win 10-6 823.81 Jul 8th Revolution 2023
138 Firefly Loss 9-10 728.69 Jul 9th Revolution 2023
173 Nebula Win 13-12 806.68 Jul 9th Revolution 2023
72 Grit City Loss 6-12 616.06 Jul 9th Revolution 2023
174 BOP Loss 6-11 130.02 Aug 26th Spawnfest 2023
188 Fable Loss 10-13 245.39 Aug 26th Spawnfest 2023
125 Garage Sale Loss 6-15 333.16 Aug 26th Spawnfest 2023
90 Hive Loss 4-14 488.37 Aug 26th Spawnfest 2023
188 Fable Win 8-5 1027.14 Aug 27th Spawnfest 2023
125 Garage Sale Loss 7-15 333.16 Aug 27th Spawnfest 2023
191 Mola Mola Win 12-11 687.52 Aug 27th Spawnfest 2023
161 DR Win 11-10 909.62 Sep 9th 2023 Mixed Nor Cal Sectional Championship
70 American Barbecue Loss 5-14 614.93 Sep 9th 2023 Mixed Nor Cal Sectional Championship
36 BW Ultimate** Loss 5-13 911.22 Ignored Sep 9th 2023 Mixed Nor Cal Sectional Championship
33 Tower** Loss 3-13 948.37 Ignored Sep 9th 2023 Mixed Nor Cal Sectional Championship
202 Air Throwmads Win 14-5 1115.63 Sep 10th 2023 Mixed Nor Cal Sectional Championship
138 Firefly Loss 11-13 624.85 Sep 10th 2023 Mixed Nor Cal Sectional Championship
94 Mango Loss 7-13 519.65 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)