#319 Mississippi (3-14)

avg: 612.05  •  sd: 62.22  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
293 Trinity Loss 8-11 363.02 Feb 22nd Mardi Gras XXXVII
320 Mississippi State-B Loss 8-9 478.09 Feb 22nd Mardi Gras XXXVII
373 LSU-B Win 12-11 417.13 Feb 22nd Mardi Gras XXXVII
305 Sam Houston Loss 7-10 278.04 Feb 22nd Mardi Gras XXXVII
171 Alabama-Birmingham Loss 4-13 592.55 Mar 22nd 2025 Annual Magic City Invite
193 Miami (Florida) Loss 3-13 488.75 Mar 22nd 2025 Annual Magic City Invite
185 Union (Tennessee) Loss 3-13 529.2 Mar 22nd 2025 Annual Magic City Invite
320 Mississippi State-B Loss 10-13 274.95 Mar 22nd 2025 Annual Magic City Invite
339 Alabama-B Win 11-7 1000.69 Mar 23rd 2025 Annual Magic City Invite
193 Miami (Florida) Loss 6-15 488.75 Mar 23rd 2025 Annual Magic City Invite
324 Samford Win 13-8 1094.06 Mar 23rd 2025 Annual Magic City Invite
100 Alabama Loss 7-13 908.06 Apr 12th Gulf Coast D I Mens Conferences 2025
171 Alabama-Birmingham Loss 7-13 635.01 Apr 12th Gulf Coast D I Mens Conferences 2025
68 Alabama-Huntsville** Loss 3-13 1029.91 Ignored Apr 12th Gulf Coast D I Mens Conferences 2025
117 Mississippi State** Loss 3-13 775.25 Ignored Apr 12th Gulf Coast D I Mens Conferences 2025
190 Vanderbilt Loss 5-13 506.32 Apr 13th Gulf Coast D I Mens Conferences 2025
128 LSU Loss 8-13 844.85 Apr 13th Gulf Coast D I Mens Conferences 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)