#175 Milwaukee Revival (5-19)

avg: 637.91  •  sd: 47.79  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
34 Mad Men** Loss 5-13 874.41 Ignored Jul 13th The Bropen 2019
52 Mallard** Loss 5-13 729.58 Ignored Jul 13th The Bropen 2019
100 Timber Loss 5-13 441.48 Jul 13th The Bropen 2019
87 Red Hots - u20 Loss 9-12 768.66 Jul 13th The Bropen 2019
95 HouSE Loss 8-11 704.39 Jul 14th The Bropen 2019
174 Red Bat Win 10-9 764.73 Jul 14th The Bropen 2019
48 Cryptic** Loss 5-13 756.2 Ignored Aug 3rd Heavyweights 2019
99 Black Market II Loss 10-13 724.26 Aug 3rd Heavyweights 2019
204 Red Imp.ala Loss 10-11 306.21 Aug 3rd Heavyweights 2019
191 Midnight Meat Train Win 13-9 944.97 Aug 4th Heavyweights 2019
149 Ditto A Win 10-9 899.9 Aug 4th Heavyweights 2019
102 THE BODY Loss 7-13 471.61 Aug 4th Heavyweights 2019
117 Satellite Loss 10-13 629.05 Aug 17th Cooler Classic 31
99 Black Market II Loss 12-13 927.4 Aug 17th Cooler Classic 31
174 Red Bat Loss 9-13 221.17 Aug 17th Cooler Classic 31
179 Chimney Win 11-7 1052.58 Aug 18th Cooler Classic 31
118 CaSTLe Loss 5-11 353.91 Aug 18th Cooler Classic 31
100 Timber Loss 4-9 441.48 Aug 18th Cooler Classic 31
20 Yogosbo** Loss 3-13 1133.03 Ignored Sep 7th Northwest Plains Mens Club Sectional Championship 2019
41 MKE** Loss 4-13 810.84 Ignored Sep 7th Northwest Plains Mens Club Sectional Championship 2019
167 DINGWOP Win 13-11 906.43 Sep 7th Northwest Plains Mens Club Sectional Championship 2019
100 Timber Loss 5-13 441.48 Sep 7th Northwest Plains Mens Club Sectional Championship 2019
52 Mallard** Loss 5-13 729.58 Ignored Sep 8th Northwest Plains Mens Club Sectional Championship 2019
95 HouSE Loss 6-12 490.69 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)