#168 Kenyon (6-11)

avg: 1252.36  •  sd: 73.67  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
232 Butler Win 12-10 1248.34 Mar 2nd FCS D III Tune Up 2024
129 Michigan Tech Loss 6-13 782.05 Mar 2nd FCS D III Tune Up 2024
179 North Carolina-Asheville Loss 10-13 871.38 Mar 2nd FCS D III Tune Up 2024
65 Richmond Loss 3-13 1074.99 Mar 2nd FCS D III Tune Up 2024
88 Berry Loss 6-13 951.68 Mar 3rd FCS D III Tune Up 2024
84 Elon Loss 11-13 1347.6 Mar 3rd FCS D III Tune Up 2024
52 Whitman Loss 7-13 1199.19 Mar 3rd FCS D III Tune Up 2024
86 Cedarville Loss 5-13 955.52 Apr 13th Ohio D III Mens Conferences 2024
68 Franciscan Loss 9-12 1315.12 Apr 13th Ohio D III Mens Conferences 2024
173 Xavier Win 13-4 1833.43 Apr 13th Ohio D III Mens Conferences 2024
123 Oberlin Win 12-8 1837.87 Apr 13th Ohio D III Mens Conferences 2024
68 Franciscan Loss 4-13 1060.48 Apr 27th Ohio Valley D III College Mens Regionals 2024
174 Grove City Win 11-10 1349.99 Apr 27th Ohio Valley D III College Mens Regionals 2024
234 Haverford Win 13-9 1418.09 Apr 27th Ohio Valley D III College Mens Regionals 2024
198 Messiah Loss 12-13 1003.99 Apr 27th Ohio Valley D III College Mens Regionals 2024
68 Franciscan Loss 5-13 1060.48 Apr 28th Ohio Valley D III College Mens Regionals 2024
171 Scranton Win 11-8 1603.8 Apr 28th Ohio Valley D III College Mens Regionals 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)