#191 Midnight Meat Train (6-13)

avg: 526.4  •  sd: 64.17  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
30 Black Market I** Loss 2-13 935.38 Ignored Jul 6th Motown Throwdown 2019
204 Red Imp.ala Win 13-6 1031.21 Jul 6th Motown Throwdown 2019
125 Dynasty Loss 7-13 350.79 Jul 6th Motown Throwdown 2019
137 Babe Loss 9-13 423.34 Jul 7th Motown Throwdown 2019
233 Buffalo Open Win 13-5 670.58 Jul 7th Motown Throwdown 2019
200 NEO Win 13-7 1021.02 Jul 7th Motown Throwdown 2019
205 BlackER Market X Win 11-8 795.6 Jul 7th Motown Throwdown 2019
54 Battery** Loss 1-13 707.49 Ignored Aug 3rd Heavyweights 2019
101 Imperial Loss 5-13 439.84 Aug 3rd Heavyweights 2019
237 Kettering Win 13-5 558.38 Aug 3rd Heavyweights 2019
175 Milwaukee Revival Loss 9-13 219.35 Aug 4th Heavyweights 2019
102 THE BODY Loss 6-13 429.14 Aug 4th Heavyweights 2019
149 Ditto A Loss 9-13 356.34 Aug 4th Heavyweights 2019
23 CLE Smokestack** Loss 2-11 1035.66 Ignored Sep 7th East Plains Mens Club Sectional Championship 2019
200 NEO Loss 9-11 214.28 Sep 7th East Plains Mens Club Sectional Championship 2019
225 Flying Dutchmen Win 11-5 847 Sep 7th East Plains Mens Club Sectional Championship 2019
129 Kentucky Flying Circus Loss 8-11 532.83 Sep 7th East Plains Mens Club Sectional Championship 2019
82 Black Lung Loss 2-11 526.33 Sep 8th East Plains Mens Club Sectional Championship 2019
128 Enigma Loss 4-11 299.13 Sep 8th East Plains Mens Club Sectional Championship 2019
**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)