#119 Berry (13-7)

avg: 1172.32  •  sd: 58.96  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
105 Mississippi State Loss 6-11 664.09 Feb 10th Golden Triangle Invitational
76 Purdue Loss 9-11 1108.01 Feb 10th Golden Triangle Invitational
222 Mississippi State -B Win 10-8 998.99 Feb 10th Golden Triangle Invitational
139 LSU Win 13-7 1642.14 Feb 10th Golden Triangle Invitational
158 Kennesaw State Win 15-13 1224.9 Feb 10th Golden Triangle Invitational
40 Illinois Loss 10-12 1341.56 Feb 11th Golden Triangle Invitational
122 Oberlin Win 13-7 1710.08 Mar 2nd FCS D III Tune Up 2024
99 Elon Win 13-12 1367.51 Mar 2nd FCS D III Tune Up 2024
59 Whitman Loss 11-13 1207.94 Mar 2nd FCS D III Tune Up 2024
73 Richmond Loss 11-13 1135.42 Mar 2nd FCS D III Tune Up 2024
231 Christopher Newport Win 13-9 1131.17 Mar 3rd FCS D III Tune Up 2024
51 Franciscan Loss 6-13 896.28 Mar 3rd FCS D III Tune Up 2024
217 Kenyon Win 13-6 1358.95 Mar 3rd FCS D III Tune Up 2024
201 Alabama-Birmingham Win 10-8 1089.53 Mar 23rd Magic City Invite 2024
222 Mississippi State -B Win 13-5 1336.33 Mar 23rd Magic City Invite 2024
264 Jacksonville State** Win 13-3 1141.75 Ignored Mar 23rd Magic City Invite 2024
326 Samford** Win 13-2 795.66 Ignored Mar 23rd Magic City Invite 2024
268 Alabama-B Win 13-6 1120.94 Mar 24th Magic City Invite 2024
268 Alabama-B Win 13-6 1120.94 Mar 24th Magic City Invite 2024
201 Alabama-Birmingham Loss 12-13 701.87 Mar 24th Magic City Invite 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)