#182 Sebastopol Orchard (6-14)

avg: 672.27  •  sd: 98.05  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
161 AC Bandits Loss 4-11 200.29 Jun 9th Bay Area Ultimate Classic 2018
74 Alchemy Loss 4-12 665.2 Jun 9th Bay Area Ultimate Classic 2018
35 Classy** Loss 2-15 913.71 Ignored Jun 9th Bay Area Ultimate Classic 2018
245 Hot Stix** Win 8-0 564.35 Ignored Jun 10th Bay Area Ultimate Classic 2018
190 DR Loss 6-10 138.99 Jun 10th Bay Area Ultimate Classic 2018
119 Buckwild Loss 6-15 431.1 Jun 10th Bay Area Ultimate Classic 2018
245 Hot Stix** Win 14-4 564.35 Ignored Jul 21st Revolution 2018
193 Feral Cows Win 11-4 1215.76 Jul 21st Revolution 2018
71 Robot** Loss 2-15 674.9 Ignored Jul 21st Revolution 2018
120 Mimosas Loss 7-9 743.62 Jul 22nd Revolution 2018
109 Superstition Loss 6-13 472.46 Jul 22nd Revolution 2018
170 Spoiler Alert Win 10-6 1254.68 Jul 22nd Revolution 2018
51 Cutthroat** Loss 4-11 757.69 Ignored Sep 8th Nor Cal Mixed Sectional Championship 2018
236 Delta Breeze Win 11-6 755.91 Sep 8th Nor Cal Mixed Sectional Championship 2018
61 Donuts** Loss 3-11 692.12 Ignored Sep 8th Nor Cal Mixed Sectional Championship 2018
119 Buckwild Loss 5-11 431.1 Sep 8th Nor Cal Mixed Sectional Championship 2018
7 Blackbird Loss 5-11 1291.68 Sep 8th Nor Cal Mixed Sectional Championship 2018
210 VU Win 11-10 651.65 Sep 9th Nor Cal Mixed Sectional Championship 2018
187 Megalodon Loss 12-13 518.4 Sep 9th Nor Cal Mixed Sectional Championship 2018
193 Feral Cows Loss 11-13 386.92 Sep 9th Nor Cal Mixed Sectional Championship 2018
**Blowout Eligible

FAQ

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
  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
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)