#96 Bear Jordan (12-5)

avg: 1076  •  sd: 65.41  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
202 Spice Win 11-9 784.91 Jul 8th Summer Glazed Daze 2023
130 904 Shipwreck Win 12-8 1349.48 Jul 8th Summer Glazed Daze 2023
152 Verdant Win 11-9 1073.7 Jul 8th Summer Glazed Daze 2023
85 Too Much Fun Win 9-8 1275.52 Jul 8th Summer Glazed Daze 2023
206 Piedmont United Win 15-5 1088.76 Aug 12th HoDown Showdown 2023
126 Barefoot Win 15-5 1567.13 Aug 12th HoDown Showdown 2023
195 MoonPi Win 12-9 913.29 Aug 12th HoDown Showdown 2023
115 Dizzy Kitty Win 15-10 1472.02 Aug 13th HoDown Showdown 2023
73 m'kay Ultimate Loss 11-15 852.58 Aug 13th HoDown Showdown 2023
86 Crown Peach Loss 9-10 1021.98 Aug 13th HoDown Showdown 2023
236 Rampage** Win 13-3 825.56 Ignored Sep 9th 2023 Mixed North Carolina Sectional Championship
248 Pickles** Win 13-5 650.83 Ignored Sep 9th 2023 Mixed North Carolina Sectional Championship
107 FlyTrap Loss 12-13 913.84 Sep 9th 2023 Mixed North Carolina Sectional Championship
141 Catalyst Win 13-8 1362.54 Sep 9th 2023 Mixed North Carolina Sectional Championship
122 Magnanimouse Win 12-11 1106.28 Sep 10th 2023 Mixed North Carolina Sectional Championship
75 Brunch Club Loss 8-13 696.78 Sep 10th 2023 Mixed North Carolina Sectional Championship
107 FlyTrap Loss 13-15 824.66 Sep 10th 2023 Mixed North Carolina Sectional Championship
**Blowout Eligible

FAQ

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
  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
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)