#87 Mississippi State (20-8)

avg: 1260.75  •  sd: 61.22  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
205 Alabama-Birmingham Win 13-4 1313.34 Jan 21st Tupelo Tuneup
275 Memphis** Win 13-3 971.92 Ignored Jan 21st Tupelo Tuneup
257 Harding** Win 13-1 1072.65 Ignored Jan 21st Tupelo Tuneup
260 Southern Mississippi** Win 13-2 1065.72 Ignored Jan 21st Tupelo Tuneup
240 Xavier** Win 13-4 1143.78 Ignored Jan 22nd Tupelo Tuneup
85 Tennessee-Chattanooga Loss 4-13 665.17 Jan 22nd Tupelo Tuneup
242 Alabama-B** Win 13-4 1131.97 Ignored Jan 28th T Town Throwdown1
233 Jacksonville State** Win 13-5 1182.54 Ignored Jan 28th T Town Throwdown1
220 Florida-B** Win 13-5 1232.88 Ignored Jan 28th T Town Throwdown1
89 Central Florida Win 7-2 1846.55 Jan 29th T Town Throwdown1
141 LSU Win 13-5 1606.58 Jan 29th T Town Throwdown1
38 Emory Loss 9-11 1286.61 Jan 29th T Town Throwdown1
89 Central Florida Loss 9-10 1121.55 Feb 25th Mardi Gras XXXV
324 Mississippi** Win 13-2 541.36 Ignored Feb 25th Mardi Gras XXXV
209 Tulane-B Win 13-6 1288.22 Feb 25th Mardi Gras XXXV
80 Texas A&M Loss 6-11 737.96 Feb 25th Mardi Gras XXXV
141 LSU Win 8-7 1131.58 Feb 26th Mardi Gras XXXV
37 Florida Loss 5-11 941.54 Feb 26th Mardi Gras XXXV
80 Texas A&M Loss 7-8 1159.65 Feb 26th Mardi Gras XXXV
85 Tennessee-Chattanooga Win 9-6 1683.73 Feb 26th Mardi Gras XXXV
36 Alabama-Huntsville Loss 9-15 1060.69 Mar 11th 2023 College Huckfest
260 Southern Mississippi** Win 15-1 1065.72 Ignored Mar 11th 2023 College Huckfest
85 Tennessee-Chattanooga Loss 10-15 811.56 Mar 12th 2023 College Huckfest
242 Alabama-B Win 13-8 1028.13 Mar 25th Magic City Invite 2023
205 Alabama-Birmingham Win 13-3 1313.34 Mar 25th Magic City Invite 2023
141 LSU Win 13-4 1606.58 Mar 25th Magic City Invite 2023
84 Alabama Win 11-7 1739.08 Mar 26th Magic City Invite 2023
141 LSU Win 13-6 1606.58 Mar 26th Magic City Invite 2023
**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)