#184 Georgia Southern (9-2)

avg: 642.54  •  sd: 67.58  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
175 Georgia College Loss 6-9 256.35 Jan 25th Clutch Classic 2020
188 Saint Louis Win 9-8 748.22 Jan 25th Clutch Classic 2020
196 North Georgia Win 9-8 711.21 Jan 25th Clutch Classic 2020
245 North Florida Win 11-4 795.94 Jan 25th Clutch Classic 2020
171 Kennesaw State Loss 6-10 192.25 Jan 26th Clutch Classic 2020
204 Berry Win 9-8 670.24 Jan 26th Clutch Classic 2020
262 High Point** Win 10-4 581.56 Ignored Feb 15th 2nd Annual Royal Crown Classic
261 Trevecca** Win 11-4 588.29 Ignored Feb 15th 2nd Annual Royal Crown Classic
248 Georgia-B Win 8-4 737.84 Feb 15th 2nd Annual Royal Crown Classic
267 Tulane-B** Win 15-6 458.56 Ignored Feb 15th 2nd Annual Royal Crown Classic
217 Georgia Tech-B Win 9-5 975.69 Feb 16th 2nd Annual Royal Crown Classic
**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)