#39 Cincinnati (21-3)

avg: 1811.62  •  sd: 55.94  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
140 George Mason Win 12-11 1429.92 Jan 25th Mid Atlantic Warm Up 2025
154 Johns Hopkins Win 13-2 1856.34 Jan 25th Mid Atlantic Warm Up 2025
70 Dartmouth Win 11-10 1743.5 Jan 25th Mid Atlantic Warm Up 2025
59 James Madison Loss 9-11 1420.52 Jan 26th Mid Atlantic Warm Up 2025
111 Vermont-B Win 14-10 1817.02 Jan 26th Mid Atlantic Warm Up 2025
78 Richmond Win 12-10 1820.58 Jan 26th Mid Atlantic Warm Up 2025
87 Missouri S&T Win 15-10 1961.18 Mar 29th Huck Finn 2025
153 Kentucky Win 15-8 1830.39 Mar 29th Huck Finn 2025
93 Southern Illinois-Edwardsville Win 15-8 2052.44 Mar 29th Huck Finn 2025
56 Indiana Win 10-9 1803.9 Mar 29th Huck Finn 2025
49 Chicago Win 11-7 2215.54 Mar 30th Huck Finn 2025
12 Washington University Loss 7-15 1504.3 Mar 30th Huck Finn 2025
42 Colorado State Win 11-10 1909.76 Mar 30th Huck Finn 2025
217 Akron** Win 13-3 1614.04 Ignored Apr 12th Ohio D I Mens Conferences 2025
77 Ohio State Win 12-11 1713.07 Apr 12th Ohio D I Mens Conferences 2025
218 Miami (Ohio) Win 13-6 1611.72 Apr 12th Ohio D I Mens Conferences 2025
346 Wright State** Win 13-4 1070.23 Ignored Apr 12th Ohio D I Mens Conferences 2025
165 Dayton** Win 15-4 1811.18 Ignored Apr 13th Ohio D I Mens Conferences 2025
77 Ohio State Win 15-5 2188.07 Apr 13th Ohio D I Mens Conferences 2025
217 Akron Win 15-7 1614.04 Apr 26th Ohio Valley D I College Mens Regionals 2025
165 Dayton** Win 15-6 1811.18 Ignored Apr 26th Ohio Valley D I College Mens Regionals 2025
74 Temple Win 15-8 2164.82 Apr 26th Ohio Valley D I College Mens Regionals 2025
77 Ohio State Loss 13-15 1373.89 Apr 27th Ohio Valley D I College Mens Regionals 2025
113 West Chester Win 14-8 1928.67 Apr 27th Ohio Valley D I College Mens Regionals 2025
**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)