#163 Hippie Mafia (3-15)

avg: 148.52  •  sd: 88.86  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
95 Scythe Loss 7-13 321.54 Aug 4th Heavyweights 2018
121 BlackER Market Loss 7-13 148.93 Aug 4th Heavyweights 2018
119 MomINtuM Loss 1-13 112.34 Aug 4th Heavyweights 2018
154 Black Market II Win 12-11 431.83 Aug 5th Heavyweights 2018
159 Midnight Meat Train Loss 7-13 -321.69 Aug 5th Heavyweights 2018
161 Ironside Win 10-6 703.32 Aug 5th Heavyweights 2018
121 BlackER Market Loss 5-13 106.46 Aug 18th Cooler Classic 30
117 THE BODY Loss 7-13 173.41 Aug 18th Cooler Classic 30
103 houSE** Loss 2-13 223.54 Ignored Aug 18th Cooler Classic 30
121 BlackER Market Loss 13-14 581.46 Aug 19th Cooler Classic 30
152 Green Bay Quackers Win 15-10 785.97 Aug 19th Cooler Classic 30
136 Pipeline Loss 7-15 -19.18 Aug 19th Cooler Classic 30
124 Wisconsin Hops Loss 7-15 96.35 Sep 8th Northwest Plains Mens Sectional Championship 2018
152 Green Bay Quackers Loss 7-13 -225.17 Sep 8th Northwest Plains Mens Sectional Championship 2018
59 Mallard** Loss 5-15 498.06 Ignored Sep 8th Northwest Plains Mens Sectional Championship 2018
70 Imperial** Loss 4-15 430.56 Ignored Sep 8th Northwest Plains Mens Sectional Championship 2018
132 DingWop Loss 1-14 9.19 Sep 9th Northwest Plains Mens Sectional Championship 2018
161 Ironside Loss 6-15 -392.84 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)