#98 Mississippi State (17-5)

avg: 1128.15  •  sd: 66.44  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
211 Alabama-Huntsville** Win 13-0 1082.58 Ignored Jan 26th Clutch Classic 2019
93 Kennesaw State Loss 9-13 769.47 Jan 26th Clutch Classic 2019
180 Georgia State Win 9-3 1230.73 Jan 26th Clutch Classic 2019
189 Tulane Win 12-4 1193.98 Jan 26th Clutch Classic 2019
144 Tennessee Win 10-7 1285.72 Jan 27th Clutch Classic 2019
87 Auburn Loss 10-11 1110.26 Jan 27th Clutch Classic 2019
104 Boston College Loss 1-9 494.98 Jan 27th Clutch Classic 2019
201 Indiana Win 8-2 1138.64 Feb 16th First Annual Jillz Jamboree
143 Alabama Win 12-6 1476.82 Feb 16th First Annual Jillz Jamboree
247 Southern Indiana** Win 8-2 762.58 Ignored Feb 16th First Annual Jillz Jamboree
146 Belmont Win 10-6 1384.76 Feb 16th First Annual Jillz Jamboree
143 Alabama Win 13-6 1497.51 Feb 17th First Annual Jillz Jamboree
111 Michigan State Loss 4-15 458.24 Feb 17th First Annual Jillz Jamboree
146 Belmont Win 9-7 1167.94 Feb 17th First Annual Jillz Jamboree
191 Texas Christian Win 13-3 1178.39 Mar 2nd Mardi Gras XXXII
143 Alabama Win 11-7 1364.4 Mar 2nd Mardi Gras XXXII
- Sam Houston State** Win 13-3 600 Ignored Mar 2nd Mardi Gras XXXII
112 Central Florida Win 9-7 1333.6 Mar 3rd Mardi Gras XXXII
214 Mississippi** Win 13-4 1045.64 Ignored Mar 23rd T town Throwdown Women
247 Southern Indiana** Win 13-4 762.58 Ignored Mar 23rd T town Throwdown Women
143 Alabama Loss 10-11 772.51 Mar 24th T town Throwdown Women
180 Georgia State Win 15-7 1230.73 Mar 24th T town Throwdown Women
**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)