#125 Dynasty (11-8)

avg: 908.32  •  sd: 75.28  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
172 Hazard Win 15-5 1256.28 Jun 22nd SCINNY 2019
128 Enigma Win 15-13 1113.31 Jun 22nd SCINNY 2019
242 Flying Piglet** Win 15-4 262.11 Ignored Jun 22nd SCINNY 2019
179 Chimney Win 14-4 1185.69 Jun 23rd SCINNY 2019
129 Kentucky Flying Circus Loss 5-13 298.44 Jun 23rd SCINNY 2019
70 Omen Loss 8-13 706.36 Jun 23rd SCINNY 2019
204 Red Imp.ala Win 13-6 1031.21 Jun 23rd SCINNY 2019
191 Midnight Meat Train Win 13-7 1083.93 Jul 6th Motown Throwdown 2019
204 Red Imp.ala Win 13-5 1031.21 Jul 6th Motown Throwdown 2019
30 Black Market I Loss 7-13 977.85 Jul 6th Motown Throwdown 2019
84 Mango Tree Win 13-12 1243.59 Jul 7th Motown Throwdown 2019
200 NEO Win 13-6 1063.49 Jul 7th Motown Throwdown 2019
70 Omen Loss 12-13 1077.52 Jul 7th Motown Throwdown 2019
128 Enigma Loss 4-9 299.13 Jul 7th Motown Throwdown 2019
169 MomINtuM Win 13-7 1229.59 Aug 24th Indy Invite Club 2019
224 Bird Patrol Win 13-6 862.11 Aug 24th Indy Invite Club 2019
99 Black Market II Loss 7-13 494.87 Aug 24th Indy Invite Club 2019
28 Nain Rouge** Loss 2-15 946.08 Ignored Aug 25th Indy Invite Club 2019
128 Enigma Loss 12-15 598.63 Aug 25th Indy Invite Club 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)