#86 Mad Udderburn (12-7)

avg: 1121.14  •  sd: 55.62  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
231 POW!** Win 13-4 859.66 Ignored Jul 8th Heavyweights 2023
105 Bandwagon Win 10-9 1153.24 Jul 8th Heavyweights 2023
170 Boomtown Pandas Win 13-6 1327.04 Jul 8th Heavyweights 2023
135 Point of No Return Win 11-8 1227.12 Jul 9th Heavyweights 2023
105 Bandwagon Win 7-3 1628.24 Jul 9th Heavyweights 2023
57 Steamboat Loss 9-12 994.04 Jul 9th Heavyweights 2023
88 Spectre Win 10-7 1498.28 Aug 19th Cooler Classic 34
135 Point of No Return Win 12-11 986.51 Aug 19th Cooler Classic 34
27 Chicago Parlay Loss 7-12 1117 Aug 19th Cooler Classic 34
60 Minnesota Star Power Loss 8-10 1033.09 Aug 19th Cooler Classic 34
85 Risky Business Loss 10-14 727.77 Aug 20th Cooler Classic 34
73 Northern Comfort Win 13-11 1423.04 Aug 20th Cooler Classic 34
60 Minnesota Star Power Loss 10-13 967.62 Aug 20th Cooler Classic 34
103 Bird Loss 10-12 799.41 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
189 Great Minnesota Get Together Win 13-5 1166.89 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
236 Mad City Vibes** Win 13-2 833.02 Ignored Sep 9th 2023 Mixed Northwest Plains Sectional Championship
145 Madison United Mixed Ultimate Win 13-7 1381.75 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
209 Mastodon** Win 15-6 1063.36 Ignored Sep 10th 2023 Mixed Northwest Plains Sectional Championship
76 Bantr Loss 11-14 870.71 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)