#77 Seattle Soft Serve (13-7)

avg: 1188.81  •  sd: 64.34  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
128 Garage Sale Win 11-5 1526.54 Aug 12th Kleinman Eruption 2023
109 Garbage Loss 6-9 607.3 Aug 12th Kleinman Eruption 2023
239 SkyLab** Win 11-0 780.19 Ignored Aug 12th Kleinman Eruption 2023
31 Spoke Loss 4-10 1046.02 Aug 12th Kleinman Eruption 2023
168 Choco Ghost House Win 12-4 1342.11 Aug 13th Kleinman Eruption 2023
31 Spoke Loss 4-9 1046.02 Aug 13th Kleinman Eruption 2023
62 American Barbecue Win 9-7 1569.04 Aug 13th Kleinman Eruption 2023
101 Igneous Ultimate Win 8-6 1358.86 Aug 13th Kleinman Eruption 2023
184 Mola Mola Win 13-5 1222.55 Aug 26th Spawnfest 2023
171 BOP Win 14-5 1336.55 Aug 26th Spawnfest 2023
114 Squid Inc. Win 11-7 1485.82 Aug 26th Spawnfest 2023
171 BOP Win 15-7 1336.55 Aug 27th Spawnfest 2023
114 Squid Inc. Win 11-7 1485.82 Aug 27th Spawnfest 2023
128 Garage Sale Win 11-6 1473.23 Aug 27th Spawnfest 2023
200 Surge Win 13-6 1149.77 Sep 9th 2023 Mixed Washington Sectional Championship
59 Grit City Loss 13-14 1187.32 Sep 9th 2023 Mixed Washington Sectional Championship
63 Pegasus Loss 8-13 792.36 Sep 9th 2023 Mixed Washington Sectional Championship
54 Lights Out Loss 8-15 778.32 Sep 9th 2023 Mixed Washington Sectional Championship
79 Bullet Train Loss 10-14 779.54 Sep 9th 2023 Mixed Washington Sectional Championship
171 BOP Win 13-8 1232.71 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)