#152 Family Style (5-13)

avg: 795.65  •  sd: 60.23  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
49 Donuts** Loss 4-13 796.52 Ignored Jul 15th TCT Select Flight West 2023
230 Birds of Paradise Win 9-7 544.39 Jul 15th TCT Select Flight West 2023
33 Tower** Loss 2-15 948.37 Ignored Jul 15th TCT Select Flight West 2023
74 Sego Loss 10-15 737.74 Jul 16th TCT Select Flight West 2023
169 Octonauts Loss 1-2 606.99 Jul 16th TCT Select Flight West 2023
139 Karma Loss 4-6 486.35 Jul 16th TCT Select Flight West 2023
53 Quick Draw Win 10-9 1475.55 Aug 19th Ski Town Classic 2023
101 Green Chiles Loss 10-13 716.11 Aug 19th Ski Town Classic 2023
157 Mesteño Win 11-10 912.33 Aug 19th Ski Town Classic 2023
75 Cutthroat Loss 9-13 768.38 Aug 20th Ski Town Classic 2023
101 Green Chiles Loss 9-11 795.05 Aug 20th Ski Town Classic 2023
144 The Strangers Win 11-10 954.16 Aug 20th Ski Town Classic 2023
160 Spoiler Alert Win 11-8 1151.3 Sep 9th 2023 Mixed So Cal Sectional Championship
17 Lawless** Loss 5-13 1163.19 Ignored Sep 9th 2023 Mixed So Cal Sectional Championship
39 Lotus** Loss 5-13 895.04 Ignored Sep 9th 2023 Mixed So Cal Sectional Championship
80 Flagstaff Ultimate Loss 6-11 596.3 Sep 9th 2023 Mixed So Cal Sectional Championship
169 Octonauts Loss 9-11 482.78 Sep 10th 2023 Mixed So Cal Sectional Championship
139 Karma Loss 9-11 602.76 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)