#173 Nebula (9-16)

avg: 681.68  •  sd: 53.83  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
192 Drip Win 12-5 1161.34 Jun 24th Buried In Disc Satisfaction BIDS
240 SkyLab Win 13-6 742.38 Jun 24th Buried In Disc Satisfaction BIDS
90 Hive Win 8-7 1213.37 Jun 24th Buried In Disc Satisfaction BIDS
100 Igneous Ultimate Loss 5-13 445.47 Jun 24th Buried In Disc Satisfaction BIDS
113 Shipwreck Loss 3-9 402.23 Jul 8th Revolution 2023
230 Birds of Paradise Win 10-5 838.95 Jul 8th Revolution 2023
72 Grit City Loss 2-15 595.37 Jul 8th Revolution 2023
94 Mango Loss 6-11 530.49 Jul 8th Revolution 2023
202 Air Throwmads Win 11-10 640.63 Jul 9th Revolution 2023
75 Cutthroat Loss 6-11 640.25 Jul 9th Revolution 2023
181 VU Loss 12-13 505.66 Jul 9th Revolution 2023
192 Drip Win 11-8 926.95 Aug 12th Kleinman Eruption 2023
205 Surge Loss 7-8 367.97 Aug 12th Kleinman Eruption 2023
172 Choco Ghost House Loss 7-8 565.8 Aug 12th Kleinman Eruption 2023
72 Grit City Loss 4-10 595.37 Aug 12th Kleinman Eruption 2023
70 American Barbecue Loss 3-7 614.93 Aug 13th Kleinman Eruption 2023
122 Garbage Win 12-11 1091.3 Aug 13th Kleinman Eruption 2023
72 Grit City Loss 7-13 637.84 Aug 13th Kleinman Eruption 2023
100 Igneous Ultimate Loss 7-12 524.96 Aug 13th Kleinman Eruption 2023
23 Oregon Scorch** Loss 3-13 1076.76 Ignored Sep 9th 2023 Mixed Oregon Sectional Championship
118 Stump Loss 10-13 657.94 Sep 9th 2023 Mixed Oregon Sectional Championship
172 Choco Ghost House Win 13-6 1290.8 Sep 9th 2023 Mixed Oregon Sectional Championship
229 Night Cap Win 13-10 623.04 Sep 9th 2023 Mixed Oregon Sectional Championship
125 Garage Sale Loss 6-15 333.16 Sep 10th 2023 Mixed Oregon Sectional Championship
100 Igneous Ultimate Loss 8-15 480.66 Sep 10th 2023 Mixed Oregon 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)