#144 Mississippi State (13-15)

avg: 1020.85  •  sd: 51.83  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
60 LSU Loss 3-11 824.45 Jan 18th TTown Throwdown 2020 Open
62 Florida Loss 9-11 1169.65 Jan 18th TTown Throwdown 2020 Open
101 Vanderbilt Loss 7-8 1084.68 Jan 18th TTown Throwdown 2020 Open
164 Illinois State Loss 5-11 373.7 Jan 18th TTown Throwdown 2020 Open
172 South Florida Win 9-8 1067.95 Jan 18th TTown Throwdown 2020 Open
49 Alabama-Huntsville Loss 7-11 1033.8 Jan 19th TTown Throwdown 2020 Open
101 Vanderbilt Win 15-8 1774.49 Jan 19th TTown Throwdown 2020 Open
71 Kentucky Loss 3-10 773.75 Feb 8th Chattanooga Classic 2020
161 Georgia State Win 10-8 1251.16 Feb 8th Chattanooga Classic 2020
133 Missouri Loss 8-10 830 Feb 8th Chattanooga Classic 2020
215 Saint Louis Win 10-7 1161.36 Feb 8th Chattanooga Classic 2020
65 Tennessee-Chattanooga Loss 8-12 954.75 Feb 9th Chattanooga Classic 2020
215 Saint Louis Win 11-9 1020.91 Feb 9th Chattanooga Classic 2020
300 Belmont University** Win 12-3 939.97 Ignored Feb 22nd Music City Tune Up 2020
146 Michigan State Loss 11-13 785.51 Feb 22nd Music City Tune Up 2020
236 Samford Win 9-7 991.1 Feb 22nd Music City Tune Up 2020
108 Chicago Loss 6-9 763.9 Feb 22nd Music City Tune Up 2020
243 Toledo Win 10-9 795.36 Feb 23rd Music City Tune Up 2020
236 Samford Win 12-8 1152.92 Feb 23rd Music City Tune Up 2020
212 Union (Tennessee) Win 12-8 1245.18 Feb 23rd Music City Tune Up 2020
288 St John's Win 13-5 1022.64 Feb 29th Mardi Gras XXXIII
69 Texas A&M Loss 10-13 1056.44 Feb 29th Mardi Gras XXXIII
119 Emory Loss 5-13 534.06 Feb 29th Mardi Gras XXXIII
277 Texas-San Antonio Win 13-6 1066.7 Feb 29th Mardi Gras XXXIII
153 Florida State Win 9-4 1603.71 Mar 1st Mardi Gras XXXIII
60 LSU Loss 8-10 1161.78 Mar 1st Mardi Gras XXXIII
139 Texas Tech Loss 8-9 918.55 Mar 1st Mardi Gras XXXIII
87 Texas State Loss 12-13 1161.68 Mar 1st Mardi Gras XXXIII
**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)