#234 Xavier (7-12)

avg: 773.54  •  sd: 53.08  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
251 Alabama-B Win 8-7 857.69 Jan 21st Tupelo Tuneup
347 Mississippi College Win 10-7 476.27 Jan 21st Tupelo Tuneup
87 Tennessee-Chattanooga** Loss 3-13 836.6 Ignored Jan 21st Tupelo Tuneup
209 Alabama-Birmingham Loss 4-7 403.92 Jan 22nd Tupelo Tuneup
89 Mississippi State** Loss 4-13 834.06 Ignored Jan 22nd Tupelo Tuneup
304 Mississippi State-B Win 8-5 885.62 Jan 22nd Tupelo Tuneup
182 Berry Loss 6-13 413.88 Mar 4th FCS D III Tune Up 2023
127 Elon Loss 8-13 766.96 Mar 4th FCS D III Tune Up 2023
126 Franciscan Loss 11-12 1142.6 Mar 4th FCS D III Tune Up 2023
202 Wooster Win 11-10 1061.38 Mar 4th FCS D III Tune Up 2023
171 Brandeis Loss 6-11 520.16 Mar 5th FCS D III Tune Up 2023
235 Air Force Loss 10-11 645.77 Mar 5th FCS D III Tune Up 2023
101 Navy** Loss 2-13 776.49 Ignored Mar 5th FCS D III Tune Up 2023
59 Cincinnati** Loss 4-10 978.71 Ignored Mar 25th Midwest Invite Plan B
164 Butler Loss 3-12 500.88 Mar 25th Midwest Invite Plan B
354 Butler-B** Win 13-1 552.83 Ignored Mar 25th Midwest Invite Plan B
215 North Park Loss 8-9 756.54 Mar 25th Midwest Invite Plan B
306 Rose-Hulman Win 13-4 1018.24 Mar 26th Midwest Invite Plan B
191 Grace Win 10-9 1106.11 Mar 26th Midwest Invite Plan B
**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)