#3 Seattle Mixtape (20-3)

avg: 2083.72  •  sd: 44.5  •  top 16/20: 100%

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# Opponent Result Game Rating Status Date Event
82 Pegasus** Win 11-3 1845.6 Ignored Jun 15th Northwest Mixer 2019
43 Birdfruit Win 11-2 2117.79 Jun 15th Northwest Mixer 2019
28 Lights Out Win 11-4 2243.61 Jun 15th Northwest Mixer 2019
11 Lochsa Win 11-6 2430.44 Jun 15th Northwest Mixer 2019
87 Garbage Win 11-5 1805.37 Jun 15th Northwest Mixer 2019
14 Love Tractor Win 13-11 2055.14 Jul 13th TCT Pro Elite Challenge 2019
37 Jughandle Win 12-9 1908.33 Jul 13th TCT Pro Elite Challenge 2019
20 Polar Bears Win 11-7 2222.95 Jul 13th TCT Pro Elite Challenge 2019
18 Columbus Cocktails Win 13-7 2320.14 Jul 14th TCT Pro Elite Challenge 2019
14 Love Tractor Win 13-9 2244.87 Jul 14th TCT Pro Elite Challenge 2019
8 shame. Win 13-9 2345.16 Jul 14th TCT Pro Elite Challenge 2019
1 Drag'n Thrust Loss 9-15 1680.88 Aug 2nd 2019 US Open Club Championship
7 Mischief Win 13-12 2052.51 Aug 2nd 2019 US Open Club Championship
6 BFG Win 15-10 2420.71 Aug 3rd 2019 US Open Club Championship
7 Mischief Win 14-10 2326.21 Aug 3rd 2019 US Open Club Championship
1 Drag'n Thrust Loss 13-14 2071.36 Aug 4th 2019 US Open Club Championship
29 Lotus Win 11-9 1892.29 Aug 17th Northwest Fruit Bowl 2019
19 The Chad Larson Experience Win 13-5 2359.72 Aug 17th Northwest Fruit Bowl 2019
52 Mesteño** Win 13-4 2041.75 Ignored Aug 17th Northwest Fruit Bowl 2019
51 Minnesota Star Power Win 11-8 1824.64 Aug 17th Northwest Fruit Bowl 2019
6 BFG Loss 11-13 1738.27 Aug 18th Northwest Fruit Bowl 2019
28 Lights Out Win 13-10 1971.75 Aug 18th Northwest Fruit Bowl 2019
19 The Chad Larson Experience Win 13-11 1988.56 Aug 18th Northwest Fruit Bowl 2019
**Blowout Eligible

FAQ

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
  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
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)