#166 Enigma (9-17)

avg: 839.67  •  sd: 55.21  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
47 Beacon** Loss 3-15 963.24 Ignored Jun 17th SCINNY1
130 Diesel Loss 9-15 525.18 Jun 17th SCINNY1
139 Hazard Loss 2-15 374.12 Jun 17th SCINNY1
253 Scoop** Win 15-2 648.49 Ignored Jun 18th SCINNY1
185 CMen Win 13-12 886.08 Jun 18th SCINNY1
239 8-Bit Defenders Win 13-9 727.28 Aug 19th Motown Throwdown 2023
239 8-Bit Defenders Win 11-8 674.32 Aug 19th Motown Throwdown 2023
84 Pittsburgh Stealers Loss 6-13 727.67 Aug 19th Motown Throwdown 2023
117 Chimney Loss 8-13 623.41 Aug 19th Motown Throwdown 2023
63 I-69** Loss 6-15 849.32 Ignored Aug 20th Motown Throwdown 2023
117 Chimney Loss 8-11 753.96 Aug 20th Motown Throwdown 2023
139 Hazard Loss 5-12 374.12 Aug 20th Motown Throwdown 2023
47 Beacon** Loss 4-15 963.24 Ignored Sep 9th 2023 Mens East Plains Sectional Championship
185 CMen Win 11-10 886.08 Sep 9th 2023 Mens East Plains Sectional Championship
75 Flying Dutchmen Loss 10-13 1049.4 Sep 9th 2023 Mens East Plains Sectional Championship
117 Chimney Win 15-8 1684.38 Sep 9th 2023 Mens East Plains Sectional Championship
47 Beacon** Loss 6-15 963.24 Ignored Sep 10th 2023 Mens East Plains Sectional Championship
130 Diesel Loss 8-15 475.85 Sep 10th 2023 Mens East Plains Sectional Championship
185 CMen Win 13-10 1089.22 Sep 10th 2023 Mens East Plains Sectional Championship
55 Colonels Loss 6-13 903.69 Sep 23rd 2023 Great Lakes Mens Regional Championship
63 I-69** Loss 2-13 849.32 Ignored Sep 23rd 2023 Great Lakes Mens Regional Championship
135 Trident II Win 13-10 1351.82 Sep 23rd 2023 Great Lakes Mens Regional Championship
76 Haymaker Loss 6-15 777.16 Sep 23rd 2023 Great Lakes Mens Regional Championship
228 Mischief Win 15-10 920.24 Sep 24th 2023 Great Lakes Mens Regional Championship
135 Trident II Loss 14-15 898.67 Sep 24th 2023 Great Lakes Mens Regional Championship
117 Chimney Loss 7-15 519.57 Sep 24th 2023 Great Lakes Mens Regional 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)