#117 Satellite (13-6)

avg: 957.19  •  sd: 60.32  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
193 Carolina Sky Win 13-10 836.23 Aug 3rd Heavyweights 2019
149 Ditto A Win 13-12 899.9 Aug 3rd Heavyweights 2019
60 Swans Loss 6-13 671.27 Aug 3rd Heavyweights 2019
154 Foxtrot Win 13-8 1257.77 Aug 4th Heavyweights 2019
204 Red Imp.ala Win 13-6 1031.21 Aug 4th Heavyweights 2019
101 Imperial Win 13-11 1268.68 Aug 4th Heavyweights 2019
175 Milwaukee Revival Win 13-10 966.06 Aug 17th Cooler Classic 31
99 Black Market II Win 11-10 1177.4 Aug 17th Cooler Classic 31
130 Kansas City Smokestack Win 13-9 1315.2 Aug 17th Cooler Classic 31
56 Scythe Loss 8-13 791.9 Aug 17th Cooler Classic 31
118 CaSTLe Win 9-8 1078.91 Aug 18th Cooler Classic 31
74 DeMo Win 8-7 1293.98 Aug 18th Cooler Classic 31
99 Black Market II Win 6-5 1177.4 Aug 18th Cooler Classic 31
169 MomINtuM Win 13-5 1272.06 Sep 7th Central Plains Mens Club Sectional Championship 2019
149 Ditto A Loss 12-13 649.9 Sep 7th Central Plains Mens Club Sectional Championship 2019
231 Black Market III Win 13-7 692.94 Sep 7th Central Plains Mens Club Sectional Championship 2019
21 Brickyard** Loss 4-13 1087.79 Ignored Sep 7th Central Plains Mens Club Sectional Championship 2019
99 Black Market II Loss 12-15 751.91 Sep 8th Central Plains Mens Club Sectional Championship 2019
149 Ditto A Loss 11-15 393.74 Sep 8th Central 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)