#72 Colt (14-8)

avg: 1389.28  •  sd: 58.75  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
158 Alibi Win 12-2 1487.94 Jul 15th Boston Invite 2023
137 Expendables Win 13-7 1555.94 Jul 15th Boston Invite 2023
163 Crossfire Win 13-6 1459.38 Jul 15th Boston Invite 2023
62 Shade Loss 8-12 1008.48 Jul 15th Boston Invite 2023
158 Alibi Win 13-9 1306.51 Aug 5th Philly Open 2023
161 MOB Ultimate Win 13-8 1360.94 Aug 5th Philly Open 2023
173 Crypt Win 13-3 1424.16 Aug 5th Philly Open 2023
80 Rumspringa Loss 11-12 1240.61 Aug 6th Philly Open 2023
52 Oakgrove Boys Loss 8-9 1402.68 Aug 6th Philly Open 2023
94 Magma Bears Win 13-6 1891.23 Aug 6th Philly Open 2023
246 Army-West Point** Win 13-2 771.41 Ignored Sep 9th 2023 Mens Metro New York Sectional Championship
230 Bartle Boys** Win 13-5 1030.92 Ignored Sep 9th 2023 Mens Metro New York Sectional Championship
209 Long Island Riff Raff** Win 13-4 1189.54 Ignored Sep 9th 2023 Mens Metro New York Sectional Championship
62 Shade Loss 10-13 1121.5 Sep 9th 2023 Mens Metro New York Sectional Championship
94 Magma Bears Win 15-12 1591.72 Sep 10th 2023 Mens Metro New York Sectional Championship
62 Shade Win 14-11 1762.98 Sep 10th 2023 Mens Metro New York Sectional Championship
23 Mephisto Loss 7-13 1225.44 Sep 23rd 2023 Northeast Mens Regional Championship
23 Mephisto Loss 6-15 1182.97 Sep 23rd 2023 Northeast Mens Regional Championship
43 Mystery Box Loss 11-15 1216.37 Sep 23rd 2023 Northeast Mens Regional Championship
62 Shade Loss 9-13 1031.07 Sep 23rd 2023 Northeast Mens Regional Championship
158 Alibi Win 15-10 1341.55 Sep 24th 2023 Northeast Mens Regional Championship
62 Shade Win 12-8 1890.79 Sep 24th 2023 Northeast Mens Regional Championship
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)