#177 District Cocktails (8-16)

avg: 649.62  •  sd: 56.39  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
46 Revival** Loss 3-13 836.06 Ignored Jun 24th Seven Cities Show Down
234 Voltage Win 13-3 841.9 Jun 24th Seven Cities Show Down
66 HVAC Loss 6-12 665.14 Jun 24th Seven Cities Show Down
124 Magnanimouse Loss 10-11 836.58 Jun 24th Seven Cities Show Down
91 Brackish Loss 6-15 486.1 Jun 25th Seven Cities Show Down
66 HVAC Loss 11-15 863.29 Jun 25th Seven Cities Show Down
201 Spice Win 10-9 648.93 Jun 25th Seven Cities Show Down
237 Rampage Win 12-7 723.84 Jul 8th Summer Glazed Daze 2023
124 Magnanimouse Loss 7-10 571.92 Jul 8th Summer Glazed Daze 2023
248 Pickles Win 12-9 371.9 Jul 8th Summer Glazed Daze 2023
79 Brunch Club Loss 9-12 803.76 Jul 8th Summer Glazed Daze 2023
61 Malice in Wonderland Loss 7-15 694.25 Jul 9th Summer Glazed Daze 2023
119 Mashed Loss 9-11 734.78 Aug 5th Philly Open 2023
111 Lampshade Win 9-7 1284.76 Aug 5th Philly Open 2023
175 Philly Twist Win 11-7 1122.44 Aug 5th Philly Open 2023
71 Grand Army Loss 6-12 625 Aug 5th Philly Open 2023
155 NY Swipes Loss 4-13 190.42 Aug 6th Philly Open 2023
146 Heavy Flow Loss 6-10 327.83 Aug 6th Philly Open 2023
252 Pumphouse** Win 13-5 353.94 Ignored Sep 9th 2023 Mixed Capital Sectional Championship
14 Rally** Loss 3-13 1233.74 Ignored Sep 9th 2023 Mixed Capital Sectional Championship
104 Legion Loss 8-11 668.57 Sep 9th 2023 Mixed Capital Sectional Championship
146 Heavy Flow Loss 8-13 327.83 Sep 9th 2023 Mixed Capital Sectional Championship
250 Vanguard and Friends** Win 14-4 478.94 Ignored Sep 10th 2023 Mixed Capital Sectional Championship
91 Brackish Loss 5-14 486.1 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)