#106 California Burrito (12-9)

avg: 1148.22  •  sd: 65.6  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
80 Alchemy Loss 10-11 1128.48 Jul 20th Revolution 2019
179 Long Beach Legacy Win 11-8 1139.43 Jul 20th Revolution 2019
143 Superstition Win 10-9 1075.28 Jul 20th Revolution 2019
102 Family Style Win 13-5 1767.56 Jul 20th Revolution 2019
116 Absolute Zero Loss 9-13 686.26 Jul 21st Revolution 2019
29 Lotus Loss 4-13 1043.08 Jul 21st Revolution 2019
59 Donuts Loss 7-12 863.35 Jul 21st Revolution 2019
128 Springs Mixed Ulty Team Win 9-8 1165.35 Aug 24th Ski Town Classic 2019
104 Moontower Win 12-11 1284.12 Aug 24th Ski Town Classic 2019
59 Donuts Loss 5-10 809.97 Aug 24th Ski Town Classic 2019
246 Rogue** Win 13-2 1053.95 Ignored Aug 24th Ski Town Classic 2019
84 Ouzel Win 12-8 1675.93 Aug 25th Ski Town Classic 2019
116 Absolute Zero Win 12-7 1625.34 Aug 25th Ski Town Classic 2019
128 Springs Mixed Ulty Team Loss 10-11 915.35 Aug 25th Ski Town Classic 2019
239 Fear and Loathing** Win 15-3 1078.37 Ignored Sep 7th So Cal Mixed Club Sectional Championship 2019
143 Superstition Win 13-7 1507.81 Sep 7th So Cal Mixed Club Sectional Championship 2019
44 Pivot Loss 6-13 900.13 Sep 7th So Cal Mixed Club Sectional Championship 2019
69 Instant Karma Loss 7-12 785.78 Sep 8th So Cal Mixed Club Sectional Championship 2019
102 Family Style Win 9-8 1292.56 Sep 8th So Cal Mixed Club Sectional Championship 2019
101 Robot Win 9-8 1292.93 Sep 8th So Cal Mixed Club Sectional Championship 2019
60 Rubix Loss 6-12 785.59 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)