#124 Wisconsin Hops (12-8)

avg: 696.35  •  sd: 84.42  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
159 Midnight Meat Train Win 13-4 835.84 Aug 4th Heavyweights 2018
73 Greater Gary Goblins Y Loss 9-13 597.23 Aug 4th Heavyweights 2018
117 THE BODY Loss 8-9 605.94 Aug 4th Heavyweights 2018
164 Fifty-Fifty Win 13-6 737.74 Aug 5th Heavyweights 2018
- Kettering** Win 13-2 600 Ignored Aug 5th Heavyweights 2018
120 KC SmokeStack Loss 6-11 165.05 Aug 5th Heavyweights 2018
123 Satellite Loss 11-13 472.31 Aug 18th Cooler Classic 30
149 Chimney Win 13-9 765.59 Aug 18th Cooler Classic 30
152 Green Bay Quackers Win 13-7 889.89 Aug 18th Cooler Classic 30
116 Greater Gary Goblins X Win 13-10 1069.3 Aug 18th Cooler Classic 30
123 Satellite Win 9-8 826.15 Aug 19th Cooler Classic 30
111 Cryptic Loss 11-15 385.3 Aug 19th Cooler Classic 30
103 houSE Win 15-11 1204.7 Aug 19th Cooler Classic 30
59 Mallard Win 15-14 1223.06 Sep 8th Northwest Plains Mens Sectional Championship 2018
70 Imperial Loss 8-15 465.75 Sep 8th Northwest Plains Mens Sectional Championship 2018
163 Hippie Mafia Win 15-7 748.52 Sep 8th Northwest Plains Mens Sectional Championship 2018
117 THE BODY Loss 11-15 349.77 Sep 8th Northwest Plains Mens Sectional Championship 2018
132 DingWop Win 8-6 909.68 Sep 9th Northwest Plains Mens Sectional Championship 2018
152 Green Bay Quackers Win 13-7 889.89 Sep 9th Northwest Plains Mens Sectional Championship 2018
161 Ironside Loss 11-12 82.16 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)