#59 Cincinnati (14-7)

avg: 1578.71  •  sd: 82.17  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
85 Alabama Loss 12-13 1321.99 Feb 25th Easterns Qualifier 2023
41 William & Mary Loss 10-12 1480.76 Feb 25th Easterns Qualifier 2023
24 North Carolina-Charlotte Loss 7-13 1336.95 Feb 25th Easterns Qualifier 2023
45 Georgetown Loss 11-13 1467.85 Feb 25th Easterns Qualifier 2023
77 Temple Win 15-6 2080.32 Feb 26th Easterns Qualifier 2023
150 George Washington Win 15-12 1448.74 Feb 26th Easterns Qualifier 2023
104 Florida State Win 15-8 1909.82 Feb 26th Easterns Qualifier 2023
301 Purdue-B** Win 13-0 1044.41 Ignored Mar 25th Midwest Invite Plan B
234 Xavier** Win 10-4 1373.54 Ignored Mar 25th Midwest Invite Plan B
306 Rose-Hulman** Win 13-0 1018.24 Ignored Mar 25th Midwest Invite Plan B
191 Grace Win 13-2 1581.11 Mar 25th Midwest Invite Plan B
354 Butler-B** Win 13-1 552.83 Ignored Mar 26th Midwest Invite Plan B
164 Butler Win 13-6 1700.88 Mar 26th Midwest Invite Plan B
49 Notre Dame Loss 5-7 1315.11 Apr 1st Huck Finn1
22 Washington University Win 6-5 2030.32 Apr 1st Huck Finn1
61 Emory Win 6-5 1701.98 Apr 1st Huck Finn1
108 Vanderbilt Win 13-1 1927.62 Apr 1st Huck Finn1
85 Alabama Win 10-8 1709.66 Apr 2nd Huck Finn1
98 Kentucky Win 9-8 1541.81 Apr 2nd Huck Finn1
35 Missouri Loss 5-14 1186.83 Apr 2nd Huck Finn1
49 Notre Dame Loss 7-11 1176.36 Apr 2nd Huck Finn1
**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)