#144 Point of No Return (10-10)

avg: 846.9  •  sd: 67.72  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
104 Queen City Gambit Win 11-8 1415.24 Jul 8th Heavyweights 2023
212 ELevate Win 13-10 770.92 Jul 8th Heavyweights 2023
117 Spectre Loss 12-13 885.45 Jul 8th Heavyweights 2023
155 Madison United Mixed Ultimate Win 13-6 1417.14 Jul 8th Heavyweights 2023
38 UNION Loss 9-13 1128.13 Jul 9th Heavyweights 2023
92 Mad Udderburn Loss 8-11 734.55 Jul 9th Heavyweights 2023
121 Jabba Loss 5-13 384.87 Jul 9th Heavyweights 2023
181 Frostbite Win 10-7 1030.82 Aug 19th Cooler Classic 34
196 Great Minnesota Get Together Win 13-7 1116.28 Aug 19th Cooler Classic 34
156 Stackcats Win 13-11 1042.66 Aug 19th Cooler Classic 34
92 Mad Udderburn Loss 11-12 975.16 Aug 19th Cooler Classic 34
155 Madison United Mixed Ultimate Loss 8-10 554.47 Aug 20th Cooler Classic 34
132 Mousetrap Loss 9-11 649.65 Aug 20th Cooler Classic 34
174 Boomtown Pandas Win 13-8 1209.25 Aug 20th Cooler Classic 34
132 Mousetrap Win 12-11 1023.86 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
249 Midnight Nut Busters** Win 15-3 561.05 Ignored Sep 9th 2023 Mixed Northwest Plains Sectional Championship
56 No Touching! Loss 9-13 905.69 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
164 Pandamonium Loss 4-15 174.31 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
182 The Force Win 13-12 764.22 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
174 Boomtown Pandas Loss 10-14 314.39 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)