#237 Rampage (5-17)

avg: 203.33  •  sd: 70.8  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
177 District Cocktails Loss 7-12 129.11 Jul 8th Summer Glazed Daze 2023
79 Brunch Club Loss 6-13 549.12 Jul 8th Summer Glazed Daze 2023
124 Magnanimouse Loss 7-13 404.05 Jul 8th Summer Glazed Daze 2023
248 Pickles Win 13-7 584.07 Jul 8th Summer Glazed Daze 2023
137 Catalyst Loss 6-13 254.77 Jul 22nd Filling the Void 2023
248 Pickles Loss 6-13 -573.46 Jul 22nd Filling the Void 2023
91 Brackish** Loss 2-13 486.1 Ignored Jul 22nd Filling the Void 2023
133 904 Shipwreck** Loss 2-13 273.62 Ignored Jul 22nd Filling the Void 2023
106 Ant Madness** Loss 3-13 427.9 Ignored Jul 23rd Filling the Void 2023
208 Piedmont United Loss 7-13 -87.56 Jul 23rd Filling the Void 2023
52 Roma Ultima** Loss 0-15 754.1 Ignored Aug 12th HoDown Showdown 2023
79 Brunch Club** Loss 5-15 549.12 Ignored Aug 12th HoDown Showdown 2023
154 Moontower Loss 3-15 194.6 Aug 12th HoDown Showdown 2023
199 MoonPi Loss 5-14 -68.09 Aug 13th HoDown Showdown 2023
248 Pickles Win 11-10 151.54 Aug 13th HoDown Showdown 2023
219 Flood Zone Loss 3-13 -223.09 Aug 13th HoDown Showdown 2023
61 Malice in Wonderland** Loss 1-13 694.25 Ignored Sep 9th 2023 Mixed North Carolina Sectional Championship
137 Catalyst Loss 6-11 308.07 Sep 9th 2023 Mixed North Carolina Sectional Championship
108 Bear Jordan** Loss 3-13 427.09 Ignored Sep 9th 2023 Mixed North Carolina Sectional Championship
248 Pickles Win 12-11 151.54 Sep 9th 2023 Mixed North Carolina Sectional Championship
208 Piedmont United Win 13-11 698.81 Sep 10th 2023 Mixed North Carolina Sectional Championship
248 Pickles Win 13-9 445.11 Sep 10th 2023 Mixed North Carolina 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)