#34 Ohio State (10-11)

avg: 1641.87  •  sd: 42.87  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
84 Appalachian State Win 15-8 1891.61 Feb 10th Queen City Tune Up 2024
68 James Madison Win 12-7 1897.4 Feb 10th Queen City Tune Up 2024
13 North Carolina State Loss 12-13 1821.6 Feb 10th Queen City Tune Up 2024
106 Notre Dame Win 15-3 1810.32 Feb 10th Queen City Tune Up 2024
154 Harvard Win 11-8 1388.79 Feb 11th Queen City Tune Up 2024
1 North Carolina** Loss 5-13 1688.56 Ignored Feb 11th Queen City Tune Up 2024
158 Kennesaw State** Win 13-3 1610.72 Ignored Feb 24th Easterns Qualifier 2024
61 William & Mary Win 13-10 1760.15 Feb 24th Easterns Qualifier 2024
29 South Carolina Loss 9-10 1558.91 Feb 24th Easterns Qualifier 2024
60 Temple Win 11-9 1684.23 Feb 24th Easterns Qualifier 2024
36 North Carolina-Charlotte Win 13-10 1946.4 Feb 25th Easterns Qualifier 2024
111 SUNY-Binghamton Win 12-5 1791.72 Feb 25th Easterns Qualifier 2024
16 Penn State Loss 11-12 1796.23 Feb 25th Easterns Qualifier 2024
66 Virginia Win 14-11 1707.51 Feb 25th Easterns Qualifier 2024
42 Michigan Loss 11-13 1337.58 Mar 30th Easterns 2024
11 Minnesota Loss 9-13 1583.68 Mar 30th Easterns 2024
1 North Carolina Loss 7-13 1731.03 Mar 30th Easterns 2024
8 Vermont Loss 11-13 1809.76 Mar 30th Easterns 2024
36 North Carolina-Charlotte Loss 10-13 1290.12 Mar 31st Easterns 2024
20 Northeastern Loss 11-15 1449.16 Mar 31st Easterns 2024
33 Wisconsin Loss 11-14 1332.16 Mar 31st Easterns 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)