#124 Magnanimouse (13-12)

avg: 961.58  •  sd: 46.5  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
46 Revival Loss 4-13 836.06 Jun 24th Seven Cities Show Down
177 District Cocktails Win 11-10 774.62 Jun 24th Seven Cities Show Down
66 HVAC Loss 8-13 748.29 Jun 24th Seven Cities Show Down
234 Voltage** Win 13-2 841.9 Ignored Jun 24th Seven Cities Show Down
201 Spice Win 15-3 1123.93 Jun 25th Seven Cities Show Down
106 Ant Madness Loss 12-15 727.41 Jun 25th Seven Cities Show Down
66 HVAC Loss 5-12 644.45 Jun 25th Seven Cities Show Down
237 Rampage Win 13-7 760.86 Jul 8th Summer Glazed Daze 2023
177 District Cocktails Win 10-7 1039.28 Jul 8th Summer Glazed Daze 2023
248 Pickles** Win 13-3 626.54 Ignored Jul 8th Summer Glazed Daze 2023
79 Brunch Club Loss 9-11 899.92 Jul 8th Summer Glazed Daze 2023
107 Columbus Chaos Loss 10-12 789.41 Jul 9th Summer Glazed Daze 2023
112 Dizzy Kitty Loss 9-11 754.61 Aug 12th HoDown Showdown 2023
248 Pickles** Win 15-5 626.54 Ignored Aug 12th HoDown Showdown 2023
87 m'kay Ultimate Loss 8-13 622.88 Aug 12th HoDown Showdown 2023
153 Memphis STAX Win 14-4 1395.54 Aug 12th HoDown Showdown 2023
208 Piedmont United Win 15-4 1069.97 Aug 13th HoDown Showdown 2023
126 Barefoot Win 11-9 1180.91 Aug 13th HoDown Showdown 2023
98 FlyTrap Win 14-13 1178.4 Aug 13th HoDown Showdown 2023
208 Piedmont United Win 11-6 1016.67 Sep 9th 2023 Mixed North Carolina Sectional Championship
22 Storm Loss 10-13 1361.21 Sep 9th 2023 Mixed North Carolina Sectional Championship
79 Brunch Club Loss 10-12 911 Sep 9th 2023 Mixed North Carolina Sectional Championship
69 Too Much Fun Loss 8-13 721.12 Sep 9th 2023 Mixed North Carolina Sectional Championship
108 Bear Jordan Loss 11-12 902.09 Sep 10th 2023 Mixed North Carolina Sectional Championship
137 Catalyst Win 15-11 1235.93 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)