#253 Scoop (0-21)

avg: 48.49  •  sd: 97.68  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
88 Black Lung** Loss 0-13 702.74 Ignored Jun 17th SCINNY1
75 Flying Dutchmen** Loss 0-13 777.54 Ignored Jun 17th SCINNY1
205 On Ramp Loss 6-13 40.19 Jun 17th SCINNY1
166 Enigma** Loss 2-15 239.67 Ignored Jun 18th SCINNY1
185 CMen** Loss 5-15 161.08 Ignored Jun 18th SCINNY1
127 Nomads** Loss 5-13 451.02 Ignored Jul 8th Heavyweights 2023
106 MKE** Loss 3-13 582.41 Ignored Jul 8th Heavyweights 2023
194 UFO Loss 6-13 116.15 Jul 8th Heavyweights 2023
55 Colonels Loss 7-13 946.16 Jul 8th Heavyweights 2023
201 Trident III Loss 7-13 118.96 Jul 9th Heavyweights 2023
191 DCVIII Loss 7-13 178.05 Jul 9th Heavyweights 2023
130 Diesel** Loss 0-13 440.66 Ignored Aug 19th Motown Throwdown 2023
63 I-69** Loss 1-13 849.32 Ignored Aug 19th Motown Throwdown 2023
139 Hazard** Loss 0-13 374.12 Ignored Aug 19th Motown Throwdown 2023
228 Mischief Loss 3-10 -133.37 Aug 20th Motown Throwdown 2023
239 8-Bit Defenders Loss 9-10 183.71 Aug 20th Motown Throwdown 2023
228 Mischief Loss 5-13 -133.37 Sep 9th 2023 Mens East Plains Sectional Championship
47 Beacon** Loss 2-13 963.24 Ignored Sep 9th 2023 Mens East Plains Sectional Championship
250 Rotisserie Chicken Loss 13-15 -106.14 Sep 10th 2023 Mens East Plains Sectional Championship
234 MARC Loss 5-15 -197.3 Sep 10th 2023 Mens East Plains Sectional Championship
216 Exhaust Pipe Loss 5-15 -65.84 Sep 10th 2023 Mens East 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)