#199 Messiah (7-13)

avg: 832.24  •  sd: 65.57  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
125 Davidson Loss 9-10 1021.57 Feb 17th Commonwealth Cup Weekend 1 2024
216 North Carolina State-B Win 12-5 1360.32 Feb 17th Commonwealth Cup Weekend 1 2024
296 South Carolina-B Win 11-5 969.12 Feb 17th Commonwealth Cup Weekend 1 2024
102 Davenport Win 12-10 1455.39 Feb 18th Commonwealth Cup Weekend 1 2024
58 Maryland Loss 9-12 1097.6 Feb 18th Commonwealth Cup Weekend 1 2024
116 Liberty Loss 7-13 625.43 Feb 18th Commonwealth Cup Weekend 1 2024
242 Butler Win 13-11 897.21 Mar 2nd FCS D III Tune Up 2024
99 Elon Loss 9-13 823.94 Mar 2nd FCS D III Tune Up 2024
174 North Carolina-Asheville Loss 9-13 517.91 Mar 2nd FCS D III Tune Up 2024
80 Lewis & Clark Loss 5-13 739.69 Mar 2nd FCS D III Tune Up 2024
231 Christopher Newport Win 13-11 941.45 Mar 3rd FCS D III Tune Up 2024
226 Embry-Riddle Win 13-12 855 Mar 3rd FCS D III Tune Up 2024
179 Missouri S&T Loss 11-13 675.26 Mar 3rd FCS D III Tune Up 2024
198 Delaware Loss 7-10 445.01 Mar 23rd Garden State 2024
166 Villanova Loss 9-10 833.54 Mar 23rd Garden State 2024
152 West Chester Loss 7-10 636.9 Mar 23rd Garden State 2024
198 Delaware Win 11-6 1381.37 Mar 24th Garden State 2024
203 West Virginia Loss 8-11 453.76 Mar 24th Garden State 2024
152 West Chester Loss 10-13 698.43 Mar 24th Garden State 2024
203 West Virginia Loss 7-9 540.04 Mar 24th Garden State 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)