#233 Jacksonville State (7-11)

avg: 582.54  •  sd: 51.58  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
242 Alabama-B Win 13-10 860.11 Jan 28th T Town Throwdown1
87 Mississippi State** Loss 5-13 660.75 Ignored Jan 28th T Town Throwdown1
38 Emory** Loss 3-11 935.82 Ignored Jan 28th T Town Throwdown1
220 Florida-B Loss 9-13 214.32 Jan 28th T Town Throwdown1
117 Georgia State Loss 5-13 510.57 Jan 29th T Town Throwdown1
253 Georgia Southern Win 10-7 881.67 Jan 29th T Town Throwdown1
330 North Florida** Win 13-2 492.01 Ignored Jan 29th T Town Throwdown1
85 Tennessee-Chattanooga** Loss 4-13 665.17 Ignored Feb 25th Mardi Gras XXXV
314 Texas Tech Win 13-8 580.37 Feb 25th Mardi Gras XXXV
37 Florida** Loss 4-13 941.54 Ignored Feb 25th Mardi Gras XXXV
320 Trinity Win 13-5 643.34 Feb 25th Mardi Gras XXXV
104 Kennesaw State Loss 2-10 572.36 Feb 26th Mardi Gras XXXV
141 LSU Loss 4-12 406.58 Feb 26th Mardi Gras XXXV
182 Texas State Win 10-9 946.94 Feb 26th Mardi Gras XXXV
84 Alabama** Loss 5-13 672.18 Ignored Mar 25th Magic City Invite 2023
205 Alabama-Birmingham Loss 10-13 385.2 Mar 25th Magic City Invite 2023
227 Samford Win 13-12 722.91 Mar 25th Magic City Invite 2023
227 Samford Loss 11-13 369.07 Mar 26th Magic City Invite 2023
**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)