#191 Yacht Club (3-16)

avg: 527.56  •  sd: 69.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
73 Swans Loss 9-13 764.94 Jun 29th Spirit of the Plains 2019
177 Red Bat Loss 11-12 487.6 Jun 29th Spirit of the Plains 2019
18 Yogosbo** Loss 3-13 1116.13 Ignored Jun 29th Spirit of the Plains 2019
30 Mad Men** Loss 3-9 896.08 Ignored Jun 30th Spirit of the Plains 2019
103 Imperial Loss 5-9 505 Jun 30th Spirit of the Plains 2019
137 Kansas City Smokestack Loss 4-11 272.45 Jun 30th Spirit of the Plains 2019
73 Swans Loss 5-11 583.51 Jul 20th The Royal Experience 2019
113 Choice City Hops Loss 9-10 866.9 Jul 20th The Royal Experience 2019
71 Dreadnought Loss 5-11 590.7 Jul 20th The Royal Experience 2019
137 Kansas City Smokestack Loss 6-7 747.45 Jul 20th The Royal Experience 2019
148 Syndicate Win 14-12 1013.6 Jul 21st The Royal Experience 2019
236 Identity Crisis Win 15-6 610.25 Jul 21st The Royal Experience 2019
219 Tsunami B Loss 10-11 182.11 Jul 21st The Royal Experience 2019
219 Tsunami B Win 15-0 907.11 Jul 21st The Royal Experience 2019
123 CaSTLe Loss 6-15 323.61 Sep 7th West Plains Mens Club Sectional Championship 2019
76 DeMo Loss 7-15 570.86 Sep 7th West Plains Mens Club Sectional Championship 2019
32 Prairie Fire** Loss 4-15 883.73 Ignored Sep 7th West Plains Mens Club Sectional Championship 2019
137 Kansas City Smokestack Loss 8-15 307.64 Sep 8th West Plains Mens Club Sectional Championship 2019
177 Red Bat Loss 8-13 116.45 Sep 8th West 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)