#105 Mississippi State (17-10)

avg: 1210.79  •  sd: 82.91  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
57 Auburn Loss 8-13 951.03 Feb 10th Golden Triangle Invitational
119 Berry Win 11-6 1719.02 Feb 10th Golden Triangle Invitational
264 Jacksonville State** Win 11-3 1141.75 Ignored Feb 10th Golden Triangle Invitational
76 Purdue Loss 9-11 1108.01 Feb 10th Golden Triangle Invitational
139 LSU Win 12-11 1209.6 Feb 10th Golden Triangle Invitational
192 Harding Win 8-0 1451.11 Feb 11th Golden Triangle Invitational
110 Arizona State Loss 9-10 1067.59 Feb 24th Mardi Gras XXXVI college
200 Spring Hill Loss 8-11 462.63 Feb 24th Mardi Gras XXXVI college
333 LSU-B** Win 13-0 751.44 Ignored Feb 24th Mardi Gras XXXVI college
87 Tennessee-Chattanooga Loss 4-13 709.98 Feb 24th Mardi Gras XXXVI college
379 Tulane-B** Win 13-3 172.13 Ignored Feb 25th Mardi Gras XXXVI college
274 Trinity** Win 13-3 1103.96 Ignored Feb 25th Mardi Gras XXXVI college
172 Texas-San Antonio Win 11-9 1189.5 Feb 25th Mardi Gras XXXVI college
201 Alabama-Birmingham Win 13-7 1384.4 Mar 16th Tally Classic XVIII
57 Auburn Loss 8-11 1081.58 Mar 16th Tally Classic XVIII
173 Clemson Win 13-6 1539.13 Mar 16th Tally Classic XVIII
360 North Florida** Win 13-3 559.96 Ignored Mar 16th Tally Classic XVIII
97 Florida State Win 10-8 1510.43 Mar 17th Tally Classic XVIII
97 Florida State Loss 6-15 647.77 Mar 17th Tally Classic XVIII
106 Notre Dame Loss 13-15 996.14 Mar 17th Tally Classic XVIII
91 Indiana Win 8-6 1571.3 Mar 30th Huck Finn 2024
121 Iowa State Win 10-9 1280.06 Mar 30th Huck Finn 2024
209 Oklahoma Win 10-1 1384 Mar 30th Huck Finn 2024
65 Stanford Loss 9-10 1279.93 Mar 30th Huck Finn 2024
118 Michigan Tech Win 12-11 1298.62 Mar 31st Huck Finn 2024
76 Purdue Loss 9-10 1232.22 Mar 31st Huck Finn 2024
108 Wisconsin-Milwaukee Win 8-6 1500.19 Mar 31st Huck Finn 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)