#139 Karma (6-17)

avg: 851.96  •  sd: 55.12  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
197 Springs Mixed Greens Win 9-7 820.66 Jun 24th Colorado Summer Solstice 2023
35 Impact** Loss 4-15 917.09 Ignored Jun 24th Colorado Summer Solstice 2023
21 Love Tractor** Loss 4-15 1107.03 Ignored Jun 24th Colorado Summer Solstice 2023
32 Mile High Trash** Loss 5-15 962.85 Ignored Jun 24th Colorado Summer Solstice 2023
134 Sin Nombre Loss 7-9 583.5 Jun 25th Colorado Summer Solstice 2023
101 Green Chiles Loss 8-11 678.65 Jun 25th Colorado Summer Solstice 2023
157 Mesteño Win 13-5 1387.33 Jun 25th Colorado Summer Solstice 2023
36 BW Ultimate** Loss 2-13 911.22 Ignored Jul 15th TCT Select Flight West 2023
41 California Burrito** Loss 5-15 888.22 Ignored Jul 15th TCT Select Flight West 2023
58 Lights Out Loss 10-11 1186.04 Jul 15th TCT Select Flight West 2023
74 Sego Loss 9-11 942.14 Jul 16th TCT Select Flight West 2023
152 Family Style Win 6-4 1161.26 Jul 16th TCT Select Flight West 2023
75 Cutthroat Loss 5-11 586.95 Aug 19th Ski Town Classic 2023
100 Igneous Ultimate Loss 8-13 549.31 Aug 19th Ski Town Classic 2023
144 The Strangers Win 9-3 1429.16 Aug 19th Ski Town Classic 2023
134 Sin Nombre Loss 7-8 737.83 Aug 20th Ski Town Classic 2023
102 Space Ghosts Loss 8-11 672.55 Aug 20th Ski Town Classic 2023
157 Mesteño Loss 8-9 662.33 Aug 20th Ski Town Classic 2023
169 Octonauts Loss 6-9 313.42 Sep 9th 2023 Mixed So Cal Sectional Championship
230 Birds of Paradise Win 10-5 838.95 Sep 9th 2023 Mixed So Cal Sectional Championship
39 Lotus Loss 6-13 895.04 Sep 9th 2023 Mixed So Cal Sectional Championship
67 Robot Loss 7-8 1113.99 Sep 10th 2023 Mixed So Cal Sectional Championship
152 Family Style Win 11-9 1044.85 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)