#159 Pandamonium (10-9)

avg: 785.85  •  sd: 85.67  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
235 Ca$h Cow$ Win 13-5 836.54 Jul 15th Cheep Thrills 2023
211 Lake Superior Disc Win 13-3 1046.79 Jul 15th Cheep Thrills 2023
103 Bird Loss 7-11 570.64 Jul 15th Cheep Thrills 2023
189 Great Minnesota Get Together Win 11-7 1033.78 Jul 15th Cheep Thrills 2023
2 Drag'n Thrust** Loss 3-13 1472.18 Ignored Jul 16th Cheep Thrills 2023
182 Melt Win 13-6 1220.06 Jul 16th Cheep Thrills 2023
244 Underdogs** Win 13-3 684.14 Ignored Jul 16th Cheep Thrills 2023
109 Pushovers Loss 8-11 659.91 Aug 19th Cooler Classic 34
145 Madison United Mixed Ultimate Win 11-7 1291.11 Aug 19th Cooler Classic 34
128 Mousetrap Loss 8-9 789.95 Aug 19th Cooler Classic 34
170 Boomtown Pandas Loss 9-13 308.47 Aug 19th Cooler Classic 34
211 Lake Superior Disc Win 11-8 812.39 Aug 20th Cooler Classic 34
189 Great Minnesota Get Together Loss 11-15 185.72 Aug 20th Cooler Classic 34
179 Frostbite Loss 9-15 130.67 Aug 20th Cooler Classic 34
226 Dinosaur Fancy Win 15-7 921.78 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
73 Northern Comfort Loss 8-15 629.39 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
170 Boomtown Pandas Win 11-10 852.04 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
135 Point of No Return Win 15-4 1461.51 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
60 Minnesota Star Power Loss 5-15 695.76 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
**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)