#258 Equinox (2-9)

avg: 365.21  •  sd: 112.4  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
229 Enough Monkeys Loss 8-12 82.96 Jul 6th AntlerLock 2019
124 Happy Valley** Loss 6-15 467.36 Ignored Jul 6th AntlerLock 2019
204 Rainbow Win 11-8 1046.65 Jul 6th AntlerLock 2019
212 Sorted Beans Loss 9-12 304.23 Jul 6th AntlerLock 2019
264 Albany Airbenders Loss 10-11 206.85 Jul 7th AntlerLock 2019
224 Side Hustle Loss 8-14 49.79 Jul 7th AntlerLock 2019
264 Albany Airbenders Loss 8-13 -164.31 Jul 20th Vacationland 2019
39 Darkwing** Loss 2-13 941.07 Ignored Jul 20th Vacationland 2019
210 Face Off Loss 10-12 419.75 Jul 20th Vacationland 2019
194 Nautilus Loss 7-8 578.4 Jul 20th Vacationland 2019
275 Dead Reckoning Win 11-6 795.93 Jul 21st Vacationland 2019
**Blowout Eligible


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)