#179 North Carolina-Asheville (11-8)

avg: 1199.53  •  sd: 72.2  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
168 Kenyon Win 13-10 1580.5 Mar 2nd FCS D III Tune Up 2024
198 Messiah Win 13-9 1547.56 Mar 2nd FCS D III Tune Up 2024
129 Michigan Tech Loss 7-13 824.51 Mar 2nd FCS D III Tune Up 2024
172 Union (Tennessee) Win 13-12 1358.68 Mar 2nd FCS D III Tune Up 2024
274 Air Force Win 13-5 1450.51 Mar 3rd FCS D III Tune Up 2024
206 Embry-Riddle Win 13-8 1595.06 Mar 3rd FCS D III Tune Up 2024
123 Oberlin Loss 8-13 900.56 Mar 3rd FCS D III Tune Up 2024
196 Charleston Loss 6-11 586.84 Mar 23rd Needle in a Ho Stack 2024
98 Georgia State Loss 4-11 920.82 Mar 23rd Needle in a Ho Stack 2024
346 Coastal Carolina** Win 11-4 1131.02 Ignored Mar 24th Needle in a Ho Stack 2024
306 High Point Win 12-5 1292.5 Mar 24th Needle in a Ho Stack 2024
251 North Carolina State-B Win 10-9 1067.94 Mar 24th Needle in a Ho Stack 2024
241 Wake Forest Win 11-8 1352.6 Mar 24th Needle in a Ho Stack 2024
209 Christopher Newport Loss 10-13 753.34 Apr 20th Atlantic Coast D III Mens Conferences 2024
84 Elon Loss 3-15 976.44 Apr 20th Atlantic Coast D III Mens Conferences 2024
176 Navy Win 12-6 1791.74 Apr 20th Atlantic Coast D III Mens Conferences 2024
114 Davidson Loss 9-10 1312.27 Apr 21st Atlantic Coast D III Mens Conferences 2024
306 High Point Win 15-5 1292.5 Apr 21st Atlantic Coast D III Mens Conferences 2024
176 Navy Loss 9-10 1087.43 Apr 21st Atlantic Coast 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)