#86 Cedarville (15-7)

avg: 1555.52  •  sd: 60.97  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
254 Michigan-B** Win 13-2 1528.81 Ignored Feb 17th Commonwealth Cup Weekend 1 2024
214 North Carolina-B Win 13-7 1631.33 Feb 17th Commonwealth Cup Weekend 1 2024
84 Elon Win 11-9 1825.65 Feb 17th Commonwealth Cup Weekend 1 2024
110 Davenport Win 12-11 1592.57 Feb 18th Commonwealth Cup Weekend 1 2024
114 Davidson Loss 9-10 1312.27 Feb 18th Commonwealth Cup Weekend 1 2024
84 Elon Loss 10-11 1451.44 Feb 18th Commonwealth Cup Weekend 1 2024
265 Georgia College** Win 13-0 1489.6 Ignored Mar 23rd Needle in a Ho Stack 2024
215 East Carolina Win 13-3 1665.59 Mar 23rd Needle in a Ho Stack 2024
251 North Carolina State-B** Win 13-3 1542.94 Ignored Mar 24th Needle in a Ho Stack 2024
164 Kennesaw State Win 12-7 1789.64 Mar 24th Needle in a Ho Stack 2024
114 Davidson Win 10-3 2037.27 Mar 24th Needle in a Ho Stack 2024
98 Georgia State Loss 6-8 1220.33 Mar 24th Needle in a Ho Stack 2024
123 Oberlin Win 11-8 1762.33 Apr 13th Ohio D III Mens Conferences 2024
173 Xavier Win 10-7 1623.1 Apr 13th Ohio D III Mens Conferences 2024
168 Kenyon Win 13-5 1852.36 Apr 13th Ohio D III Mens Conferences 2024
68 Franciscan Win 13-8 2156.64 Apr 13th Ohio D III Mens Conferences 2024
173 Xavier Win 11-7 1700.32 Apr 27th Ohio Valley D III College Mens Regionals 2024
318 Swarthmore** Win 11-4 1255.47 Ignored Apr 27th Ohio Valley D III College Mens Regionals 2024
123 Oberlin Loss 9-10 1271.72 Apr 27th Ohio Valley D III College Mens Regionals 2024
171 Scranton Loss 11-12 1113.19 Apr 27th Ohio Valley D III College Mens Regionals 2024
68 Franciscan Loss 8-13 1164.33 Apr 28th Ohio Valley D III College Mens Regionals 2024
171 Scranton Loss 12-13 1113.19 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)