#146 Dirty D (5-12)

avg: 413.23  •  sd: 63.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
47 MKE** Loss 2-11 624.38 Ignored Jul 7th Motown Throwdown 2018
154 Black Market II Win 11-6 853.53 Jul 7th Motown Throwdown 2018
65 Mango Tree Loss 9-11 794.96 Jul 7th Motown Throwdown 2018
112 Enigma Loss 7-15 164.44 Jul 7th Motown Throwdown 2018
- COAT Win 15-12 572.39 Jul 8th Motown Throwdown 2018
127 Dynasty Loss 5-11 51.97 Jul 8th Motown Throwdown 2018
123 Satellite Loss 7-13 143.62 Aug 4th Heavyweights 2018
70 Imperial** Loss 4-13 430.56 Ignored Aug 4th Heavyweights 2018
- Kettering Win 13-5 600 Ignored Aug 4th Heavyweights 2018
- Baemaker Win 13-11 523.69 Aug 5th Heavyweights 2018
112 Enigma Loss 8-13 268.28 Aug 5th Heavyweights 2018
152 Green Bay Quackers Loss 10-11 207.36 Aug 5th Heavyweights 2018
149 Chimney Loss 7-11 -119.86 Sep 8th East Plains Mens Sectional Championship 2018
136 Pipeline Win 12-10 818.94 Sep 8th East Plains Mens Sectional Championship 2018
65 Mango Tree** Loss 0-13 444.17 Ignored Sep 8th East Plains Mens Sectional Championship 2018
36 CLE Smokestack** Loss 4-13 699.63 Ignored Sep 9th East Plains Mens Sectional Championship 2018
104 Black Lung Loss 10-11 683.72 Sep 9th East Plains Mens Sectional Championship 2018
**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)