#134 Watch City (8-12)

avg: 588.9  •  sd: 79.9  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
67 Red Circus Loss 10-15 585.91 Jun 23rd Boston Invite 2018
96 Shrike Loss 14-15 736.82 Jun 23rd Boston Invite 2018
158 Shades Win 11-4 858.19 Jun 23rd Boston Invite 2018
94 Red Tide Loss 12-15 581.03 Jun 24th Boston Invite 2018
67 Red Circus Loss 14-15 914.52 Jun 24th Boston Invite 2018
158 Shades Win 15-5 858.19 Jun 24th Boston Invite 2018
105 Bruises Loss 8-11 424.36 Jul 21st Vacationland 2018
138 Ender's Outcasts Win 11-6 1073.46 Jul 21st Vacationland 2018
131 Somerville BAG Win 15-7 1228.37 Jul 21st Vacationland 2018
167 Neap Tide Win 15-5 678.63 Jul 21st Vacationland 2018
94 Red Tide Loss 4-15 281.52 Jul 22nd Vacationland 2018
85 Pesterbug Loss 6-11 383.51 Jul 22nd Vacationland 2018
138 Ender's Outcasts Loss 6-15 -73.23 Aug 25th End of Season Round Robin Boston
156 One Night Win 14-12 519.69 Aug 25th End of Season Round Robin Boston
105 Bruises Loss 8-13 293.81 Sep 8th East New England Mens Sectional Championship 2018
29 Big Wrench Loss 6-13 777.85 Sep 8th East New England Mens Sectional Championship 2018
156 One Night Win 10-8 561.4 Sep 8th East New England Mens Sectional Championship 2018
85 Pesterbug Loss 5-13 330.2 Sep 8th East New England Mens Sectional Championship 2018
138 Ender's Outcasts Win 13-8 1022.93 Sep 9th East New England Mens Sectional Championship 2018
131 Somerville BAG Loss 10-13 300.23 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)