#54 Lights Out (8-10)

avg: 1343.13  •  sd: 65.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
60 Cutthroat Loss 10-14 911.58 Jul 15th TCT Select Flight West 2023
133 Karma Win 11-10 1023.56 Jul 15th TCT Select Flight West 2023
64 Minnesota Star Power Loss 11-12 1158.35 Jul 15th TCT Select Flight West 2023
112 Pushovers Win 13-9 1440.56 Jul 16th TCT Select Flight West 2023
71 Sego Loss 11-12 1111.74 Jul 16th TCT Select Flight West 2023
230 Birds of Paradise** Win 15-2 905.44 Ignored Jul 16th TCT Select Flight West 2023
21 Sunshine Loss 9-10 1608.69 Aug 26th Northwest Fruit Bowl 2023
23 MOONDOG Loss 6-12 1140.97 Aug 26th Northwest Fruit Bowl 2023
32 Kansas City United Loss 8-13 1147.27 Aug 26th Northwest Fruit Bowl 2023
39 Lotus Win 13-10 1857.09 Aug 26th Northwest Fruit Bowl 2023
32 Kansas City United Loss 8-13 1147.27 Aug 27th Northwest Fruit Bowl 2023
48 Wild Card Loss 12-13 1316.83 Aug 27th Northwest Fruit Bowl 2023
9 Red Flag Loss 5-15 1341.57 Sep 9th 2023 Mixed Washington Sectional Championship
77 Seattle Soft Serve Win 15-8 1753.62 Sep 9th 2023 Mixed Washington Sectional Championship
171 BOP Win 13-6 1336.55 Sep 9th 2023 Mixed Washington Sectional Championship
109 Garbage Win 13-6 1625.87 Sep 9th 2023 Mixed Washington Sectional Championship
63 Pegasus Loss 11-13 1059.68 Sep 10th 2023 Mixed Washington Sectional Championship
125 TT Win 15-9 1491.82 Sep 10th 2023 Mixed Washington 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)