#127 Sin Nombre (10-9)

avg: 952.79  •  sd: 63.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
28 Flight Club Loss 7-13 1120.89 Jun 24th Colorado Summer Solstice 2023
43 California Burrito Loss 6-10 997.6 Jun 24th Colorado Summer Solstice 2023
147 Mesteño Win 8-7 963.07 Jun 24th Colorado Summer Solstice 2023
80 Flagstaff Ultimate Loss 5-9 647.98 Jun 24th Colorado Summer Solstice 2023
192 Springs Mixed Greens Win 13-6 1191.15 Jun 25th Colorado Summer Solstice 2023
137 The Strangers Loss 7-12 358.42 Jun 25th Colorado Summer Solstice 2023
133 Karma Win 9-7 1177.89 Jun 25th Colorado Summer Solstice 2023
147 Mesteño Win 9-8 963.07 Aug 19th Ski Town Classic 2023
36 Tower** Loss 4-13 952.94 Ignored Aug 19th Ski Town Classic 2023
99 Green Chiles Loss 12-13 936.22 Aug 19th Ski Town Classic 2023
147 Mesteño Loss 8-10 575.4 Aug 20th Ski Town Classic 2023
95 Space Ghosts Win 9-6 1497.1 Aug 20th Ski Town Classic 2023
133 Karma Win 8-7 1023.56 Aug 20th Ski Town Classic 2023
95 Space Ghosts Loss 13-15 864.35 Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
192 Springs Mixed Greens Win 10-9 716.15 Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
137 The Strangers Win 15-12 1179.42 Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
99 Green Chiles Loss 9-15 545.74 Sep 10th 2023 Mixed Rocky Mountain Sectional Championship
187 Celebrities of Ultimate Win 15-10 1066.73 Sep 10th 2023 Mixed Rocky Mountain Sectional Championship
131 Mostly Harmless Win 14-10 1304.71 Sep 10th 2023 Mixed Rocky Mountain 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)