#88 Berry (19-7)

avg: 1551.68  •  sd: 57.06  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
82 Mississippi State Loss 6-11 1036.21 Feb 10th Golden Triangle Invitational
62 Purdue Loss 9-11 1440.54 Feb 10th Golden Triangle Invitational
242 Mississippi State -B Win 10-8 1248.73 Feb 10th Golden Triangle Invitational
116 LSU Win 13-7 1988.62 Feb 10th Golden Triangle Invitational
164 Kennesaw State Win 15-13 1483.31 Feb 10th Golden Triangle Invitational
39 Illinois Loss 10-12 1647.25 Feb 11th Golden Triangle Invitational
123 Oberlin Win 13-7 1954.25 Mar 2nd FCS D III Tune Up 2024
84 Elon Win 13-12 1701.44 Mar 2nd FCS D III Tune Up 2024
52 Whitman Loss 11-13 1527.88 Mar 2nd FCS D III Tune Up 2024
65 Richmond Loss 11-13 1446.15 Mar 2nd FCS D III Tune Up 2024
209 Christopher Newport Win 13-9 1500.05 Mar 3rd FCS D III Tune Up 2024
68 Franciscan Loss 6-13 1060.48 Mar 3rd FCS D III Tune Up 2024
168 Kenyon Win 13-6 1852.36 Mar 3rd FCS D III Tune Up 2024
195 Alabama-Birmingham Win 10-8 1404.92 Mar 23rd Magic City Invite 2024
242 Mississippi State -B Win 13-5 1586.06 Mar 23rd Magic City Invite 2024
277 Jacksonville State** Win 13-3 1442.53 Ignored Mar 23rd Magic City Invite 2024
358 Samford** Win 13-2 1065.09 Ignored Mar 23rd Magic City Invite 2024
303 Alabama-B Win 13-6 1311.11 Mar 24th Magic City Invite 2024
303 Alabama-B Win 13-6 1311.11 Mar 24th Magic City Invite 2024
195 Alabama-Birmingham Loss 12-13 1017.26 Mar 24th Magic City Invite 2024
73 Ave Maria Win 13-8 2110.87 Apr 13th Southeast D III Mens Conferences 2024
265 Georgia College Win 13-7 1447.13 Apr 13th Southeast D III Mens Conferences 2024
206 Embry-Riddle Win 13-7 1656.43 Apr 13th Southeast D III Mens Conferences 2024
73 Ave Maria Win 13-12 1739.71 Apr 14th Southeast D III Mens Conferences 2024
172 Union (Tennessee) Win 13-8 1729.84 Apr 14th Southeast D III Mens Conferences 2024
206 Embry-Riddle Win 15-7 1698.9 Apr 14th Southeast D III Mens Conferences 2024
**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)