#74 Cincinnati (10-14)

avg: 1614  •  sd: 62.82  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
69 Central Florida Win 13-10 1966.68 Feb 2nd Florida Warm Up 2024
10 Texas Loss 9-11 1996.98 Feb 2nd Florida Warm Up 2024
18 Northeastern Loss 6-13 1508.05 Feb 2nd Florida Warm Up 2024
85 Cornell Loss 11-13 1346.17 Feb 3rd Florida Warm Up 2024
3 Georgia** Loss 5-13 1856.9 Ignored Feb 3rd Florida Warm Up 2024
36 Wisconsin Loss 11-14 1580.56 Feb 4th Florida Warm Up 2024
48 Auburn Loss 6-13 1182.29 Feb 24th Easterns Qualifier 2024
162 Rutgers Win 13-7 1840.08 Feb 24th Easterns Qualifier 2024
54 Emory Loss 10-13 1427.18 Feb 24th Easterns Qualifier 2024
27 North Carolina-Wilmington Loss 7-12 1474.77 Feb 24th Easterns Qualifier 2024
47 Alabama Loss 6-15 1184.81 Feb 25th Easterns Qualifier 2024
70 James Madison Win 12-9 1978.55 Feb 25th Easterns Qualifier 2024
96 Notre Dame Win 15-12 1831.48 Feb 25th Easterns Qualifier 2024
59 William & Mary Win 11-4 2327.3 Feb 25th Easterns Qualifier 2024
282 Toledo** Win 13-1 1418.55 Ignored Apr 20th Ohio D I Mens Conferences 2024
194 Ohio Win 13-2 1742.62 Apr 20th Ohio D I Mens Conferences 2024
347 Wright State** Win 13-1 1127.36 Ignored Apr 20th Ohio D I Mens Conferences 2024
35 Ohio State Loss 10-11 1778.25 Apr 21st Ohio D I Mens Conferences 2024
157 Miami (Ohio) Win 11-7 1756.66 Apr 21st Ohio D I Mens Conferences 2024
35 Ohio State Loss 11-15 1522.08 Apr 21st Ohio D I Mens Conferences 2024
79 Case Western Reserve Loss 9-15 1080.21 May 4th Ohio Valley D I College Mens Regionals 2024
157 Miami (Ohio) Win 15-10 1743.37 May 4th Ohio Valley D I College Mens Regionals 2024
56 Temple Loss 11-12 1618.67 May 4th Ohio Valley D I College Mens Regionals 2024
97 Lehigh Loss 12-13 1401.34 May 5th Ohio Valley D I 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)