#43 California Burrito (15-9)

avg: 1493.76  •  sd: 48.84  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
127 Sin Nombre Win 10-6 1448.95 Jun 24th Colorado Summer Solstice 2023
95 Space Ghosts Win 10-6 1574.69 Jun 24th Colorado Summer Solstice 2023
28 Flight Club Loss 6-12 1099.11 Jun 24th Colorado Summer Solstice 2023
147 Mesteño Win 14-7 1420.95 Jun 24th Colorado Summer Solstice 2023
80 Flagstaff Ultimate Win 12-6 1756.35 Jun 25th Colorado Summer Solstice 2023
20 Love Tractor Loss 8-13 1238.01 Jun 25th Colorado Summer Solstice 2023
33 Mile High Trash Loss 11-12 1517.35 Jun 25th Colorado Summer Solstice 2023
165 Octonauts** Win 13-1 1366.23 Ignored Jul 15th TCT Select Flight West 2023
133 Karma Win 15-5 1498.56 Jul 15th TCT Select Flight West 2023
60 Cutthroat Win 15-5 1910.28 Jul 15th TCT Select Flight West 2023
47 Donuts Win 14-10 1842.08 Jul 16th TCT Select Flight West 2023
39 Lotus Loss 11-12 1403.95 Jul 16th TCT Select Flight West 2023
47 Donuts Loss 10-11 1318.38 Aug 26th Northwest Fruit Bowl 2023
22 Oregon Scorch Loss 8-13 1235.21 Aug 26th Northwest Fruit Bowl 2023
41 BW Ultimate Loss 11-13 1271.43 Aug 26th Northwest Fruit Bowl 2023
33 Mile High Trash Loss 10-12 1404.22 Aug 26th Northwest Fruit Bowl 2023
47 Donuts Win 13-11 1672.22 Aug 27th Northwest Fruit Bowl 2023
63 Pegasus Win 13-11 1517.36 Aug 27th Northwest Fruit Bowl 2023
113 Shipwreck Win 13-6 1621.03 Sep 9th 2023 Mixed So Cal Sectional Championship
230 Birds of Paradise** Win 13-2 905.44 Ignored Sep 9th 2023 Mixed So Cal Sectional Championship
80 Flagstaff Ultimate Win 10-7 1566.71 Sep 9th 2023 Mixed So Cal Sectional Championship
39 Lotus Loss 8-12 1087.8 Sep 9th 2023 Mixed So Cal Sectional Championship
69 Robot Win 15-6 1854.28 Sep 10th 2023 Mixed So Cal Sectional Championship
113 Shipwreck Win 11-3 1621.03 Sep 10th 2023 Mixed So 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)