#74 Sego (13-11)

avg: 1191.34  •  sd: 58.11  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
1 shame.** Loss 2-15 1567.21 Ignored Jun 24th Colorado Summer Solstice 2023
80 Flagstaff Ultimate Loss 7-10 753.33 Jun 24th Colorado Summer Solstice 2023
102 Space Ghosts Loss 8-10 775.49 Jun 24th Colorado Summer Solstice 2023
101 Green Chiles Loss 9-10 919.26 Jun 25th Colorado Summer Solstice 2023
157 Mesteño Win 15-7 1387.33 Jun 25th Colorado Summer Solstice 2023
102 Space Ghosts Win 12-10 1276.28 Jun 25th Colorado Summer Solstice 2023
60 Minnesota Star Power Loss 10-15 842.15 Jul 15th TCT Select Flight West 2023
169 Octonauts Win 13-9 1150.55 Jul 15th TCT Select Flight West 2023
75 Cutthroat Loss 7-13 629.42 Jul 15th TCT Select Flight West 2023
152 Family Style Win 15-10 1249.25 Jul 16th TCT Select Flight West 2023
139 Karma Win 11-9 1101.17 Jul 16th TCT Select Flight West 2023
58 Lights Out Win 12-11 1436.04 Jul 16th TCT Select Flight West 2023
100 Igneous Ultimate Win 10-7 1435.14 Aug 19th Ski Town Classic 2023
102 Space Ghosts Win 13-10 1366.3 Aug 19th Ski Town Classic 2023
75 Cutthroat Loss 12-13 1061.95 Aug 19th Ski Town Classic 2023
100 Igneous Ultimate Loss 9-10 920.47 Aug 20th Ski Town Classic 2023
144 The Strangers Win 13-6 1429.16 Aug 20th Ski Town Classic 2023
101 Green Chiles Win 12-7 1564.77 Aug 20th Ski Town Classic 2023
53 Quick Draw Win 13-11 1579.39 Sep 9th 2023 Mixed Big Sky Sectional Championship
25 MOONDOG Loss 9-11 1399.07 Sep 9th 2023 Mixed Big Sky Sectional Championship
180 Fishpix! Win 15-2 1235.61 Sep 9th 2023 Mixed Big Sky Sectional Championship
53 Quick Draw Loss 6-15 750.55 Sep 10th 2023 Mixed Big Sky Sectional Championship
143 Bozos Win 15-8 1396.82 Sep 10th 2023 Mixed Big Sky Sectional Championship
25 MOONDOG Loss 10-15 1194.68 Sep 10th 2023 Mixed Big Sky 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)