#108 Alt Stacks (13-5)

avg: 1077.49  •  sd: 94.77  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
139 Nautilus Win 11-9 1165.1 Jul 21st Vacationland 2018
214 Face Off Win 12-8 934.21 Jul 21st Vacationland 2018
181 RIMIX Win 12-7 1192.87 Jul 21st Vacationland 2018
- Rising Tide Win 12-8 945.84 Jul 21st Vacationland 2018
107 Sunken Circus Win 12-3 1677.52 Jul 21st Vacationland 2018
150 Scarecrow Win 12-4 1467.4 Jul 22nd Vacationland 2018
53 Darkwing Loss 3-14 734.27 Jul 22nd Vacationland 2018
127 Funk Loss 8-13 484.72 Aug 25th The Incident 2018
50 Grand Army Win 11-9 1618.89 Aug 25th The Incident 2018
132 HVAC Win 13-7 1520.06 Aug 25th The Incident 2018
83 Birds Win 12-9 1570.91 Aug 25th The Incident 2018
220 Bees** Win 15-3 1003.91 Ignored Aug 26th The Incident 2018
141 Powermove Win 12-9 1238.88 Aug 26th The Incident 2018
83 Birds Loss 9-14 751.67 Aug 26th The Incident 2018
220 Bees Win 13-11 632.75 Sep 8th Metro New York Mixed Sectional Championship 2018
83 Birds Loss 10-13 897.4 Sep 8th Metro New York Mixed Sectional Championship 2018
- Pink Pear Win 13-10 1119.89 Sep 8th Metro New York Mixed Sectional Championship 2018
141 Powermove Loss 9-11 644.31 Sep 9th Metro New York Mixed 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)