#71 Grand Army (11-10)

avg: 1204.31  •  sd: 42.17  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
47 Darkwing Loss 9-10 1294.5 Jul 15th Boston Invite 2023
38 Pittsburgh Port Authority Loss 10-11 1376.11 Jul 15th Boston Invite 2023
62 Funk Win 10-7 1651.56 Jul 15th Boston Invite 2023
7 XIST** Loss 1-13 1320.86 Ignored Jul 15th Boston Invite 2023
177 District Cocktails Win 12-6 1228.93 Aug 5th Philly Open 2023
95 Scarecrow Loss 8-9 943.5 Aug 5th Philly Open 2023
195 Swampbenders** Win 13-5 1148.56 Ignored Aug 5th Philly Open 2023
184 Crucible** Win 13-2 1200.41 Ignored Aug 5th Philly Open 2023
114 One More Year Win 9-8 1123.32 Aug 6th Philly Open 2023
97 Farm Show Win 10-5 1628.99 Aug 6th Philly Open 2023
78 Deadweight Loss 11-12 1036.64 Sep 9th 2023 Mixed Metro New York Sectional Championship
178 Eat Lightning Win 13-4 1249.39 Sep 9th 2023 Mixed Metro New York Sectional Championship
155 NY Swipes Win 13-7 1347.95 Sep 9th 2023 Mixed Metro New York Sectional Championship
162 Room Temperature Win 13-8 1271.03 Sep 9th 2023 Mixed Metro New York Sectional Championship
155 NY Swipes Win 15-4 1390.42 Sep 10th 2023 Mixed Metro New York Sectional Championship
68 Heat Wave Loss 9-12 890.81 Sep 10th 2023 Mixed Metro New York Sectional Championship
78 Deadweight Win 11-10 1286.64 Sep 23rd 2023 Northeast Mixed Regional Championship
6 Sprocket** Loss 5-13 1324.96 Ignored Sep 23rd 2023 Northeast Mixed Regional Championship
45 Wild Card Loss 6-15 857.53 Sep 23rd 2023 Northeast Mixed Regional Championship
62 Funk Loss 11-12 1136.89 Sep 24th 2023 Northeast Mixed Regional Championship
68 Heat Wave Loss 8-9 1111.17 Sep 24th 2023 Northeast Mixed 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)