#84 Seattle Soul (3-20)

avg: 269.19  •  sd: 96.89  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
38 FAB** Loss 4-13 519.26 Ignored Jun 24th Summer Solstice 2023
36 remix** Loss 1-13 566.72 Jun 24th Summer Solstice 2023
9 Schwa** Loss 1-13 1501.71 Ignored Jun 24th Summer Solstice 2023
28 Oregon Downpour** Loss 4-13 808.63 Ignored Jun 24th Summer Solstice 2023
77 Portland Rain Check Loss 5-8 -43.19 Jun 25th Summer Solstice 2023
51 Seven Devils** Loss 3-13 307.19 Ignored Jun 25th Summer Solstice 2023
32 Crush City** Loss 0-13 713.39 Ignored Aug 19th Ski Town Classic 2023
49 Trainwreck** Loss 5-13 334.06 Ignored Aug 19th Ski Town Classic 2023
45 Rampage Loss 4-8 408.94 Aug 19th Ski Town Classic 2023
87 Haboob Win 9-8 281.71 Aug 20th Ski Town Classic 2023
100 Just Add Water Win 9-8 -56.15 Aug 20th Ski Town Classic 2023
79 Swell Win 11-8 715.26 Aug 20th Ski Town Classic 2023
27 Underground** Loss 1-15 825.92 Ignored Sep 9th 2023 Womens Washington Sectional Championship
50 Drift** Loss 2-15 314.94 Ignored Sep 9th 2023 Womens Washington Sectional Championship
53 Hucklebears** Loss 2-15 277.79 Sep 9th 2023 Womens Washington Sectional Championship
77 Portland Rain Check Loss 8-11 44.8 Sep 10th 2023 Womens Washington Sectional Championship
33 Seattle END** Loss 4-15 674.73 Ignored Sep 10th 2023 Womens Washington Sectional Championship
27 Underground** Loss 1-13 825.92 Ignored Sep 23rd 2023 Northwest Womens Regional Championship
10 Traffic** Loss 0-13 1501.06 Ignored Sep 23rd 2023 Northwest Womens Regional Championship
51 Seven Devils** Loss 2-13 307.19 Ignored Sep 23rd 2023 Northwest Womens Regional Championship
56 Eugene Further Loss 7-11 378.43 Sep 23rd 2023 Northwest Womens Regional Championship
77 Portland Rain Check Loss 9-11 161.21 Sep 24th 2023 Northwest Womens Regional Championship
56 Eugene Further Loss 7-15 245.32 Sep 24th 2023 Northwest Womens Regional 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)