#106 Ant Madness (19-7)

avg: 1041.14  •  sd: 44.11  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
163 Espionage Win 11-4 1379.5 Jun 24th Seven Cities Show Down
202 Spice Win 11-3 1135.71 Jun 24th Seven Cities Show Down
93 Brackish Loss 5-10 523.3 Jun 24th Seven Cities Show Down
105 Legion Win 10-9 1168.98 Jun 24th Seven Cities Show Down
197 Swampbenders Win 11-5 1158.19 Jun 24th Seven Cities Show Down
46 Revival Loss 8-15 888.89 Jun 25th Seven Cities Show Down
122 Magnanimouse Win 15-12 1281.78 Jun 25th Seven Cities Show Down
105 Legion Win 15-14 1168.98 Jun 25th Seven Cities Show Down
248 Pickles** Win 13-4 650.83 Ignored Jul 22nd Filling the Void 2023
130 904 Shipwreck Win 13-12 1033.32 Jul 22nd Filling the Void 2023
141 Catalyst Win 11-10 991.38 Jul 22nd Filling the Void 2023
93 Brackish Win 12-11 1222.19 Jul 22nd Filling the Void 2023
236 Rampage** Win 13-3 825.56 Ignored Jul 23rd Filling the Void 2023
105 Legion Win 12-11 1168.98 Jul 23rd Filling the Void 2023
66 Malice in Wonderland Win 8-8 1268.7 Jul 23rd Filling the Void 2023
74 Deadweight Loss 11-13 979.63 Aug 19th Philly Invite 2023
178 Philly Twist Win 14-8 1208.1 Aug 19th Philly Invite 2023
67 HVAC Loss 9-13 843.78 Aug 19th Philly Invite 2023
142 PS Win 15-12 1157 Aug 20th Philly Invite 2023
213 Milk Win 15-9 956.71 Aug 20th Philly Invite 2023
105 Legion Loss 12-14 823.02 Aug 20th Philly Invite 2023
232 Voltage** Win 15-2 848.87 Ignored Sep 9th 2023 Mixed Capital Sectional Championship
197 Swampbenders Win 12-8 999.34 Sep 9th 2023 Mixed Capital Sectional Championship
163 Espionage Win 15-6 1379.5 Sep 10th 2023 Mixed Capital Sectional Championship
57 Greater Baltimore Anthem Loss 7-11 851.25 Sep 10th 2023 Mixed Capital Sectional Championship
84 Fireball Loss 7-12 633.8 Sep 10th 2023 Mixed Capital Sectional 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)