#240 Xavier (7-12)

avg: 543.78  •  sd: 51.2  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
242 Alabama-B Win 8-7 656.97 Jan 21st Tupelo Tuneup
329 Mississippi College Win 10-7 286.56 Jan 21st Tupelo Tuneup
85 Tennessee-Chattanooga** Loss 3-13 665.17 Ignored Jan 21st Tupelo Tuneup
205 Alabama-Birmingham Loss 4-7 217.18 Jan 22nd Tupelo Tuneup
87 Mississippi State** Loss 4-13 660.75 Ignored Jan 22nd Tupelo Tuneup
292 Mississippi State-B Win 8-5 698.19 Jan 22nd Tupelo Tuneup
181 Berry Loss 6-13 223.93 Mar 4th FCS D III Tune Up 2023
126 Elon Loss 8-13 566.38 Mar 4th FCS D III Tune Up 2023
144 Franciscan Loss 11-12 856 Mar 4th FCS D III Tune Up 2023
200 Wooster Win 11-10 859.8 Mar 4th FCS D III Tune Up 2023
176 Brandeis Loss 6-11 311.15 Mar 5th FCS D III Tune Up 2023
241 Air Force Loss 10-11 416.15 Mar 5th FCS D III Tune Up 2023
78 Navy** Loss 2-13 699.22 Ignored Mar 5th FCS D III Tune Up 2023
63 Cincinnati** Loss 4-10 780.22 Ignored Mar 25th Midwest Invite Plan B
170 Butler Loss 3-12 277.06 Mar 25th Midwest Invite Plan B
336 Butler-B** Win 13-1 320.09 Ignored Mar 25th Midwest Invite Plan B
231 North Park Loss 8-9 461.99 Mar 25th Midwest Invite Plan B
304 Rose-Hulman Win 13-4 757.19 Mar 26th Midwest Invite Plan B
207 Grace Win 10-9 834.43 Mar 26th Midwest Invite Plan B
**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)