#156 One Night (3-16)

avg: 298.74  •  sd: 73.81  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
87 Westchester Magma Bears Loss 10-15 460.27 Jul 7th AntlerLock 2018
131 Somerville BAG Loss 8-14 92.34 Jul 7th AntlerLock 2018
94 Red Tide Loss 10-15 427.92 Jul 7th AntlerLock 2018
105 Bruises Loss 9-15 274.49 Jul 8th AntlerLock 2018
77 Deathsquad Loss 9-15 465.44 Jul 8th AntlerLock 2018
- Allen's Adolescents Win 13-4 600 Ignored Jul 21st Vacationland 2018
105 Bruises Loss 5-13 189.97 Jul 21st Vacationland 2018
138 Ender's Outcasts Loss 12-13 401.77 Jul 21st Vacationland 2018
94 Red Tide Loss 4-13 281.52 Jul 21st Vacationland 2018
85 Pesterbug** Loss 4-13 330.2 Ignored Jul 21st Vacationland 2018
- Allen's Adolescents Win 15-2 600 Ignored Jul 22nd Vacationland 2018
138 Ender's Outcasts Win 13-6 1126.77 Jul 22nd Vacationland 2018
138 Ender's Outcasts Loss 7-15 -73.23 Aug 25th End of Season Round Robin Boston
134 Watch City Loss 12-14 367.94 Aug 25th End of Season Round Robin Boston
138 Ender's Outcasts Loss 3-13 -73.23 Sep 8th East New England Mens Sectional Championship 2018
29 Big Wrench** Loss 3-13 777.85 Ignored Sep 8th East New England Mens Sectional Championship 2018
134 Watch City Loss 8-10 326.23 Sep 8th East New England Mens Sectional Championship 2018
85 Pesterbug Loss 6-13 330.2 Sep 8th East New England Mens Sectional Championship 2018
105 Bruises Loss 6-13 189.97 Sep 9th East New England 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)