#103 Imperial (13-14)

avg: 1034.06  •  sd: 48.59  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
73 Swans Loss 7-10 793.85 Jun 29th Spirit of the Plains 2019
177 Red Bat Win 13-9 1031.17 Jun 29th Spirit of the Plains 2019
18 Yogosbo** Loss 2-13 1116.13 Ignored Jun 29th Spirit of the Plains 2019
25 General Strike Loss 4-13 999.45 Jun 30th Spirit of the Plains 2019
97 THE BODY Loss 10-13 721.54 Jun 30th Spirit of the Plains 2019
57 Cryptic Loss 6-8 1002.98 Jun 30th Spirit of the Plains 2019
191 Yacht Club Win 9-5 1056.62 Jun 30th Spirit of the Plains 2019
193 Midnight Meat Train Win 13-5 1105.08 Aug 3rd Heavyweights 2019
61 Battery Loss 6-13 679.3 Aug 3rd Heavyweights 2019
238 Kettering** Win 13-5 532.34 Ignored Aug 3rd Heavyweights 2019
150 Foxtrot Win 13-5 1377.94 Aug 4th Heavyweights 2019
122 Satellite Loss 11-13 697.19 Aug 4th Heavyweights 2019
208 Red Imp.ala Win 13-7 963.08 Aug 4th Heavyweights 2019
111 Black Market II Win 13-11 1224.09 Aug 17th Cooler Classic 31
97 THE BODY Win 13-6 1649.68 Aug 17th Cooler Classic 31
137 Kansas City Smokestack Win 13-9 1291.01 Aug 17th Cooler Classic 31
177 Red Bat Win 13-2 1212.6 Aug 17th Cooler Classic 31
46 Haymaker Loss 8-9 1271.63 Aug 18th Cooler Classic 31
57 Cryptic Loss 4-11 703.47 Aug 18th Cooler Classic 31
96 HouSE Win 9-6 1471.06 Aug 18th Cooler Classic 31
25 General Strike Loss 2-11 999.45 Sep 7th Northwest Plains Mens Club Sectional Championship 2019
73 Swans Loss 8-11 817.9 Sep 7th Northwest Plains Mens Club Sectional Championship 2019
169 DINGWOP Win 11-6 1220.25 Sep 7th Northwest Plains Mens Club Sectional Championship 2019
223 Fargoats** Win 11-3 852.17 Ignored Sep 8th Northwest Plains Mens Club Sectional Championship 2019
30 Mad Men Loss 2-11 896.08 Sep 8th Northwest Plains Mens Club Sectional Championship 2019
116 Timber Loss 9-12 623.47 Sep 8th Northwest Plains Mens Club Sectional Championship 2019
96 HouSE Loss 12-13 927.49 Sep 8th Northwest Plains Mens 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)