#135 Mississippi State (18-13)

avg: 1111.28  •  sd: 46.35  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
313 Alabama-B Win 13-6 901.37 Jan 25th T Town Throwdown XX
172 Alabama-Birmingham Win 9-7 1229.54 Jan 25th T Town Throwdown XX
185 Union (Tennessee) Win 13-8 1375.08 Jan 25th T Town Throwdown XX
94 Tennessee-Chattanooga Win 11-9 1528.77 Jan 25th T Town Throwdown XX
104 Alabama Loss 12-15 938.47 Jan 26th T Town Throwdown XX
250 Illinois State Win 15-5 1212.58 Jan 26th T Town Throwdown XX
269 Jacksonville State Win 13-10 863.45 Jan 26th T Town Throwdown XX
160 LSU Win 9-2 1592.08 Jan 26th T Town Throwdown XX
268 Harding Win 15-3 1135.96 Feb 8th Bulldog Brawl
133 Lipscomb Win 13-11 1343.7 Feb 8th Bulldog Brawl
194 Saint Louis Win 13-6 1452 Feb 8th Bulldog Brawl
110 Berry Loss 13-15 993.37 Feb 9th Bulldog Brawl
194 Saint Louis Win 15-13 1066.18 Feb 9th Bulldog Brawl
90 Missouri S&T Loss 9-10 1170.31 Feb 9th Bulldog Brawl
209 Arkansas Win 13-3 1362.15 Feb 22nd Mardi Gras XXXVII
30 Ave Maria** Loss 5-13 1123.9 Feb 22nd Mardi Gras XXXVII
134 South Florida Win 10-9 1237.4 Feb 22nd Mardi Gras XXXVII
103 Texas A&M Loss 4-13 640.66 Feb 22nd Mardi Gras XXXVII
129 Arizona State Win 9-8 1249.63 Mar 15th Mens Centex 2025
76 Colorado College Loss 5-8 916.11 Mar 15th Mens Centex 2025
57 Illinois Loss 10-11 1390.42 Mar 15th Mens Centex 2025
136 North Texas Loss 6-11 563.77 Mar 15th Mens Centex 2025
209 Arkansas Win 15-10 1215.75 Mar 16th Mens Centex 2025
107 Iowa Loss 6-15 622.36 Mar 16th Mens Centex 2025
188 Oklahoma State Win 15-11 1244.98 Mar 16th Mens Centex 2025
82 St Olaf Win 11-10 1446 Mar 29th Huck Finn 2025
51 Purdue Loss 6-13 955.86 Mar 29th Huck Finn 2025
141 Northwestern Loss 9-10 947.86 Mar 29th Huck Finn 2025
103 Texas A&M Loss 9-10 1115.66 Mar 29th Huck Finn 2025
104 Alabama Loss 6-11 692.27 Mar 30th Huck Finn 2025
150 Kentucky Win 12-10 1290.38 Mar 30th Huck Finn 2025
**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)