#224 Georgia Southern (7-20)

avg: 861.24  •  sd: 58.08  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
244 Berry Loss 7-13 226.11 Jan 27th Clutch Classic 2018
418 Kennesaw State-B** Win 13-1 445.2 Ignored Jan 27th Clutch Classic 2018
376 Tulane-B Win 13-2 878 Jan 27th Clutch Classic 2018
75 Tennessee-Chattanooga Loss 5-12 815.67 Jan 27th Clutch Classic 2018
140 Florida Tech Loss 5-15 567.47 Jan 28th Clutch Classic 2018
282 Wingate Win 11-10 787.96 Jan 28th Clutch Classic 2018
124 Indiana Loss 5-13 626.26 Feb 17th Easterns Qualifier 2018
64 North Carolina-Charlotte** Loss 5-13 862.5 Ignored Feb 17th Easterns Qualifier 2018
113 Lehigh Loss 11-12 1159.08 Feb 17th Easterns Qualifier 2018
102 Richmond Loss 4-13 726.88 Feb 17th Easterns Qualifier 2018
34 William & Mary** Loss 5-13 1048.2 Ignored Feb 17th Easterns Qualifier 2018
133 Case Western Reserve Loss 7-14 592.88 Feb 18th Easterns Qualifier 2018
151 George Mason Loss 9-15 601.36 Feb 18th Easterns Qualifier 2018
62 Vermont** Loss 5-15 865.83 Ignored Feb 18th Easterns Qualifier 2018
124 Indiana Loss 10-13 898.12 Mar 10th Tally Classic XIII
16 North Carolina-Wilmington** Loss 3-13 1284.51 Ignored Mar 10th Tally Classic XIII
231 Alabama-Birmingham Win 15-7 1421.08 Mar 10th Tally Classic XIII
88 Alabama-Huntsville Loss 7-13 830.77 Mar 10th Tally Classic XIII
168 South Florida Loss 11-12 938.99 Mar 10th Tally Classic XIII
140 Florida Tech Win 15-10 1621.07 Mar 11th Tally Classic XIII
303 Charleston Win 13-10 899.62 Mar 17th College Southerns 2018
125 Georgia College Loss 7-13 658.28 Mar 17th College Southerns 2018
273 Wake Forest Loss 6-8 401.18 Mar 17th College Southerns 2018
69 Carleton College-GoP Loss 6-13 849.46 Mar 17th College Southerns 2018
150 North Carolina-Asheville Loss 11-12 1006.08 Mar 18th College Southerns 2018
303 Charleston Win 13-6 1171.48 Mar 18th College Southerns 2018
76 Chicago Loss 3-13 815.31 Mar 18th College Southerns 2018
**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)