#132 Mousetrap (11-8)

avg: 898.86  •  sd: 65.82  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
104 Queen City Gambit Win 12-8 1490.79 Jul 8th Heavyweights 2023
240 PanIC** Win 13-4 741.66 Ignored Jul 8th Heavyweights 2023
181 Frostbite Win 12-8 1082.31 Jul 8th Heavyweights 2023
121 Jabba Loss 9-10 859.87 Jul 8th Heavyweights 2023
143 Skyhawks Win 13-6 1451.49 Jul 9th Heavyweights 2023
156 Stackcats Win 12-9 1159.19 Jul 9th Heavyweights 2023
117 Spectre Loss 5-12 410.45 Aug 19th Cooler Classic 34
164 Pandamonium Win 9-8 899.31 Aug 19th Cooler Classic 34
245 Underdogs** Win 13-5 674.21 Ignored Aug 19th Cooler Classic 34
78 Northern Comfort Loss 11-12 1063.75 Aug 19th Cooler Classic 34
112 Pushovers Loss 9-13 603.43 Aug 20th Cooler Classic 34
144 Point of No Return Win 11-9 1096.11 Aug 20th Cooler Classic 34
182 The Force Win 13-7 1196.75 Aug 20th Cooler Classic 34
144 Point of No Return Loss 11-12 721.9 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
249 Midnight Nut Busters Win 15-7 561.05 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
182 The Force Win 15-9 1154.7 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
174 Boomtown Pandas Loss 9-10 588.09 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
64 Minnesota Star Power Loss 12-15 982.86 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
182 The Force Loss 14-15 514.22 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)