#45 Queen Cake (13-7)

avg: 1116.99  •  sd: 65.49  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
26 Virginia Rebellion Loss 7-13 890.17 Jun 15th ATL Classic 2019
57 Outbreak Win 11-9 1113.57 Jun 15th ATL Classic 2019
78 cATLanta Win 11-8 775.09 Jun 15th ATL Classic 2019
61 Red Hot Ultimate Win 8-7 895.23 Jun 15th ATL Classic 2019
22 Tabby Rosa Loss 5-12 957.69 Jun 16th ATL Classic 2019
50 Taco Truck Loss 9-12 708.63 Jun 16th ATL Classic 2019
47 Crush City Loss 5-12 485.87 Jul 20th 2019 Club Terminus
86 Honey Pot** Win 13-4 852 Ignored Jul 20th 2019 Club Terminus
63 Huntsville Laika Win 11-8 1130.69 Jul 20th 2019 Club Terminus
32 Steel Loss 8-12 938.49 Jul 20th 2019 Club Terminus
47 Crush City Loss 10-11 960.87 Jul 21st 2019 Club Terminus
63 Huntsville Laika Win 11-9 1014.29 Jul 21st 2019 Club Terminus
75 Viva Win 13-4 1161.06 Aug 24th Ski Town Classic 2019
47 Crush City Win 11-9 1335.08 Aug 24th Ski Town Classic 2019
35 Seattle Soul Win 11-10 1382.99 Aug 24th Ski Town Classic 2019
82 Seven Devils Win 13-6 900.21 Aug 24th Ski Town Classic 2019
47 Crush City Win 10-4 1685.87 Aug 25th Ski Town Classic 2019
35 Seattle Soul Loss 10-11 1132.99 Aug 25th Ski Town Classic 2019
63 Huntsville Laika Win 12-7 1285.6 Sep 7th Gulf Coast Womens Club Sectional Championship 2019
32 Steel Win 12-10 1617.77 Sep 7th Gulf Coast Womens Club Sectional Championship 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)