#96 Robot (12-10)

avg: 1120.72  •  sd: 63.47  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
118 Absolute Zero Loss 11-13 825.35 Jun 8th Bay Area Ultimate Classic 2019
163 VU Win 11-5 1414.21 Jun 8th Bay Area Ultimate Classic 2019
30 Lotus Loss 7-10 1227.72 Jun 8th Bay Area Ultimate Classic 2019
269 Sebastopol Orchard** Win 13-2 843.88 Ignored Jun 8th Bay Area Ultimate Classic 2019
237 Feral Cows Win 15-7 1031.23 Jun 9th Bay Area Ultimate Classic 2019
64 Donuts Loss 8-13 776.61 Jun 9th Bay Area Ultimate Classic 2019
177 Spoiler Alert Win 15-10 1184.09 Jun 9th Bay Area Ultimate Classic 2019
119 Bulleit Train Win 11-7 1520.66 Jul 20th Revolution 2019
104 Argo Loss 9-10 976.84 Jul 20th Revolution 2019
66 Firefly Loss 6-13 657.29 Jul 20th Revolution 2019
148 Sweet Action Win 13-2 1471.05 Jul 20th Revolution 2019
88 Alchemy Loss 6-11 606.46 Jul 21st Revolution 2019
98 Family Style Win 11-7 1585.21 Jul 21st Revolution 2019
142 Superstition Win 13-8 1393.96 Jul 21st Revolution 2019
195 Birds of Paradise Win 10-8 911.43 Sep 7th So Cal Mixed Club Sectional Championship 2019
98 Family Style Win 11-9 1367.53 Sep 7th So Cal Mixed Club Sectional Championship 2019
30 Lotus Loss 8-10 1354.72 Sep 7th So Cal Mixed Club Sectional Championship 2019
243 Rogue Win 9-4 1010.15 Sep 7th So Cal Mixed Club Sectional Championship 2019
97 California Burrito Loss 8-9 995.63 Sep 8th So Cal Mixed Club Sectional Championship 2019
69 Instant Karma Loss 7-13 685 Sep 8th So Cal Mixed Club Sectional Championship 2019
48 Pivot Loss 8-13 944.35 Sep 8th So Cal Mixed Club Sectional Championship 2019
56 Rubix Win 13-11 1563.32 Sep 8th So Cal Mixed Club Sectional Championship 2019
**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)