#90 HouSE (10-9)

avg: 1296.54  •  sd: 54.82  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
17 STL Lounar Loss 6-13 1309.35 Jul 8th Heavyweights 2023
156 NOMAD Win 13-10 1218.1 Jul 8th Heavyweights 2023
45 Kenji's BBB Win 13-12 1709.66 Jul 8th Heavyweights 2023
117 Chimney Win 12-11 1244.57 Jul 8th Heavyweights 2023
127 Nomads Win 13-9 1469.59 Jul 9th Heavyweights 2023
63 I-69 Loss 7-11 982.42 Jul 9th Heavyweights 2023
73 Knights of Ni Win 13-9 1804.42 Jul 9th Heavyweights 2023
96 Bux Loss 10-11 1163.86 Aug 19th Cooler Classic 34
36 Kansas City Smokestack Loss 5-13 1041.9 Aug 19th Cooler Classic 34
29 Mallard Loss 8-13 1232.11 Aug 19th Cooler Classic 34
135 Trident II Win 13-12 1148.67 Aug 19th Cooler Classic 34
47 Beacon Loss 8-15 998.43 Aug 20th Cooler Classic 34
17 STL Lounar** Loss 6-15 1309.35 Ignored Aug 20th Cooler Classic 34
76 Haymaker Win 15-11 1758.32 Aug 20th Cooler Classic 34
179 Timber Win 15-8 1375.36 Sep 9th 2023 Mens Northwest Plains Sectional Championship
106 MKE Win 15-12 1482.9 Sep 9th 2023 Mens Northwest Plains Sectional Championship
194 UFO Win 15-10 1169.75 Sep 9th 2023 Mens Northwest Plains Sectional Championship
96 Bux Loss 13-15 1074.68 Sep 10th 2023 Mens Northwest Plains Sectional Championship
16 General Strike Loss 7-15 1336.6 Sep 10th 2023 Mens Northwest Plains 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)