#322 Mississippi (4-16)

avg: 587.21  •  sd: 65.97  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
185 Alabama-Birmingham Loss 4-10 431.96 Jan 26th T Town Throwdown
160 Vanderbilt Loss 5-10 550.48 Jan 26th T Town Throwdown
106 Illinois State** Loss 2-11 727.34 Ignored Jan 26th T Town Throwdown
296 LSU-B Loss 8-14 159.77 Jan 27th T Town Throwdown
309 Illinois State-B Win 13-12 758.22 Jan 27th T Town Throwdown
159 Mississippi State Loss 5-13 525.81 Jan 27th T Town Throwdown
207 North Florida Loss 7-11 498.62 Mar 2nd Mardi Gras XXXII
212 Texas Christian Loss 7-11 483.73 Mar 2nd Mardi Gras XXXII
363 Texas State -B Win 11-3 1015.34 Mar 2nd Mardi Gras XXXII
298 Texas A&M-B Win 13-10 1012.09 Mar 2nd Mardi Gras XXXII
36 Alabama** Loss 2-13 1123.14 Ignored Mar 2nd Mardi Gras XXXII
209 Trinity Loss 8-13 461.73 Mar 3rd Mardi Gras XXXII
212 Texas Christian Loss 6-12 371.31 Mar 3rd Mardi Gras XXXII
117 Jacksonville State Loss 5-10 718.6 Mar 23rd Magic City Invite 2019
208 Berry Loss 3-11 358.78 Mar 23rd Magic City Invite 2019
196 Middle Tennessee State Loss 6-11 456.45 Mar 23rd Magic City Invite 2019
185 Alabama-Birmingham Loss 10-11 906.96 Mar 23rd Magic City Invite 2019
308 Alabama-B Loss 5-9 110.76 Mar 23rd Magic City Invite 2019
251 Samford Win 13-10 1179.47 Mar 24th Magic City Invite 2019
208 Berry Loss 6-13 358.78 Mar 24th Magic City Invite 2019
**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)