#103 houSE (14-14)

avg: 823.54  •  sd: 53.71  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
154 Black Market II Win 13-7 864.36 Aug 4th Heavyweights 2018
152 Green Bay Quackers Win 13-5 932.36 Aug 4th Heavyweights 2018
61 Tanasi Loss 4-13 495.92 Aug 4th Heavyweights 2018
- Baemaker Win 12-7 815.36 Aug 5th Heavyweights 2018
121 BlackER Market Loss 10-11 581.46 Aug 5th Heavyweights 2018
152 Green Bay Quackers Win 13-4 932.36 Aug 5th Heavyweights 2018
95 Scythe Win 14-12 1100.02 Aug 18th Cooler Classic 30
121 BlackER Market Win 13-7 1263.99 Aug 18th Cooler Classic 30
117 THE BODY Win 13-6 1330.94 Aug 18th Cooler Classic 30
163 Hippie Mafia** Win 13-2 748.52 Ignored Aug 18th Cooler Classic 30
124 Wisconsin Hops Loss 11-15 315.18 Aug 19th Cooler Classic 30
60 DeMo Loss 9-15 581.1 Aug 19th Cooler Classic 30
117 THE BODY Win 15-9 1246.42 Aug 19th Cooler Classic 30
49 CaSTLe Loss 11-13 985.31 Aug 25th The Bropen 2018
121 BlackER Market Win 13-9 1125.02 Aug 25th The Bropen 2018
39 Mad Men Loss 7-13 721.44 Aug 25th The Bropen 2018
56 Haymaker Loss 7-13 572.74 Aug 25th The Bropen 2018
49 CaSTLe Loss 4-13 614.15 Aug 26th The Bropen 2018
31 Black Market Loss 5-13 746.73 Aug 26th The Bropen 2018
39 Mad Men Loss 6-13 678.97 Aug 26th The Bropen 2018
161 Ironside** Win 13-3 807.16 Ignored Aug 26th The Bropen 2018
132 DingWop Win 13-7 1166.72 Sep 8th Northwest Plains Mens Sectional Championship 2018
32 General Strike Loss 4-13 746.5 Sep 8th Northwest Plains Mens Sectional Championship 2018
72 Swans Loss 7-13 459.33 Sep 8th Northwest Plains Mens Sectional Championship 2018
161 Ironside Win 12-6 786.47 Sep 8th Northwest Plains Mens Sectional Championship 2018
59 Mallard Loss 13-15 883.88 Sep 9th Northwest Plains Mens Sectional Championship 2018
117 THE BODY Win 13-10 1059.08 Sep 9th Northwest Plains Mens Sectional Championship 2018
70 Imperial Loss 6-15 430.56 Sep 9th Northwest Plains Mens Sectional Championship 2018
**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)