#104 Legion (17-9)

avg: 1034.18  •  sd: 50.23  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
164 Espionage Win 11-4 1371.56 Jun 24th Seven Cities Show Down
201 Spice Win 10-7 913.59 Jun 24th Seven Cities Show Down
106 Ant Madness Loss 9-10 902.9 Jun 24th Seven Cities Show Down
195 Swampbenders Win 11-5 1148.56 Jun 24th Seven Cities Show Down
91 Brackish Loss 7-10 696.43 Jun 24th Seven Cities Show Down
106 Ant Madness Loss 14-15 902.9 Jun 25th Seven Cities Show Down
91 Brackish Loss 6-15 486.1 Jun 25th Seven Cities Show Down
66 HVAC Win 11-10 1369.45 Jun 25th Seven Cities Show Down
195 Swampbenders Win 12-6 1127.87 Jul 22nd Filling the Void 2023
69 Too Much Fun Win 12-11 1342.28 Jul 22nd Filling the Void 2023
61 Malice in Wonderland Loss 8-13 798.09 Jul 22nd Filling the Void 2023
208 Piedmont United Win 13-7 1027.5 Jul 23rd Filling the Void 2023
106 Ant Madness Loss 11-12 902.9 Jul 23rd Filling the Void 2023
137 Catalyst Win 8-7 979.77 Jul 23rd Filling the Void 2023
38 Pittsburgh Port Authority Loss 4-15 901.11 Aug 19th Philly Invite 2023
68 Heat Wave Loss 5-15 636.17 Aug 19th Philly Invite 2023
213 Milk Win 15-8 992.16 Aug 19th Philly Invite 2023
175 Philly Twist Win 15-10 1109.15 Aug 20th Philly Invite 2023
106 Ant Madness Win 14-12 1248.86 Aug 20th Philly Invite 2023
146 Heavy Flow Win 15-12 1124.48 Aug 20th Philly Invite 2023
252 Pumphouse** Win 13-2 353.94 Ignored Sep 9th 2023 Mixed Capital Sectional Championship
177 District Cocktails Win 11-8 1015.23 Sep 9th 2023 Mixed Capital Sectional Championship
146 Heavy Flow Win 13-9 1242.56 Sep 9th 2023 Mixed Capital Sectional Championship
14 Rally** Loss 5-13 1233.74 Ignored Sep 10th 2023 Mixed Capital Sectional Championship
114 One More Year Win 11-10 1123.32 Sep 10th 2023 Mixed Capital Sectional Championship
91 Brackish Win 10-9 1211.1 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)