#165 Georgia Southern (9-17)

avg: 1091.91  •  sd: 48.41  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
159 Mississippi State Loss 7-10 736.14 Feb 2nd Royal Crown Classic 2019
361 Miami** Win 13-4 1022.51 Ignored Feb 2nd Royal Crown Classic 2019
208 Berry Win 15-14 1083.78 Feb 2nd Royal Crown Classic 2019
429 Columbus State** Win 13-2 571.58 Ignored Feb 2nd Royal Crown Classic 2019
159 Mississippi State Loss 8-12 684.65 Feb 3rd Royal Crown Classic 2019
234 Florida Tech Win 13-11 1135.1 Feb 3rd Royal Crown Classic 2019
120 James Madison Loss 8-13 786.64 Feb 16th Easterns Qualifier 2019
139 Pennsylvania Loss 9-12 884.31 Feb 16th Easterns Qualifier 2019
38 Purdue** Loss 3-13 1107.04 Ignored Feb 16th Easterns Qualifier 2019
39 Vermont** Loss 4-13 1105.77 Ignored Feb 16th Easterns Qualifier 2019
155 Elon Loss 14-15 1024.58 Feb 17th Easterns Qualifier 2019
145 Dayton Loss 12-14 968.72 Feb 17th Easterns Qualifier 2019
197 George Mason Win 15-10 1455 Feb 17th Easterns Qualifier 2019
72 Alabama-Huntsville Loss 11-13 1255.15 Mar 16th Tally Classic XIV
15 Central Florida Loss 7-13 1432.78 Mar 16th Tally Classic XIV
52 Notre Dame Loss 6-13 1026.67 Mar 16th Tally Classic XIV
103 Georgia State Win 15-12 1648.87 Mar 16th Tally Classic XIV
143 Minnesota-Duluth Loss 13-14 1074.07 Mar 17th Tally Classic XIV
68 Cincinnati Loss 8-15 950.56 Mar 17th Tally Classic XIV
94 Appalachian State Loss 10-13 1044.29 Mar 23rd College Southerns XVIII
56 California-San Diego Loss 7-13 1035.23 Mar 23rd College Southerns XVIII
321 Carleton Hot Karls Win 13-6 1189.49 Mar 23rd College Southerns XVIII
173 Georgia College Win 9-8 1194.11 Mar 23rd College Southerns XVIII
173 Georgia College Loss 12-15 768.62 Mar 24th College Southerns XVIII
240 Wisconsin-Eau Claire Win 15-9 1405.32 Mar 24th College Southerns XVIII
146 North Carolina-Asheville Loss 10-12 950.04 Mar 24th College Southerns XVIII
**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)