#18 Ohio State (15-5)

avg: 1845.53  •  sd: 72.1  •  top 16/20: 82.8%

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# Opponent Result Game Rating Status Date Event
64 James Madison** Win 11-3 1750.57 Ignored Jan 27th Winta Binta Vinta 2024
103 Liberty** Win 13-2 1353.8 Ignored Jan 27th Winta Binta Vinta 2024
53 Georgetown Win 13-3 1901.64 Jan 27th Winta Binta Vinta 2024
130 Virginia-B** Win 13-0 865.8 Ignored Jan 27th Winta Binta Vinta 2024
34 Virginia Win 9-5 2055.7 Jan 28th Winta Binta Vinta 2024
75 Virginia Tech** Win 13-2 1590.56 Ignored Jan 28th Winta Binta Vinta 2024
50 William & Mary Win 10-5 1899.85 Jan 28th Winta Binta Vinta 2024
2 North Carolina Loss 8-15 1838.84 Feb 10th Queen City Tune Up 2024
9 Michigan Loss 10-15 1605.05 Feb 10th Queen City Tune Up 2024
14 Georgia Loss 7-12 1377.31 Feb 10th Queen City Tune Up 2024
15 Pennsylvania Win 11-10 2012.79 Feb 10th Queen City Tune Up 2024
98 Case Western Reserve** Win 15-5 1397.24 Ignored Feb 11th Queen City Tune Up 2024
34 Virginia Win 12-9 1872 Feb 11th Queen City Tune Up 2024
56 Chicago Win 15-7 1885.16 Feb 24th Commonwealth Cup Weekend 2 2024
42 Yale Win 15-5 2056.32 Feb 24th Commonwealth Cup Weekend 2 2024
24 Pittsburgh Loss 10-11 1577.98 Feb 24th Commonwealth Cup Weekend 2 2024
43 SUNY-Binghamton Win 12-3 2010.79 Feb 25th Commonwealth Cup Weekend 2 2024
14 Georgia Win 9-8 2022.82 Feb 25th Commonwealth Cup Weekend 2 2024
34 Virginia Win 11-7 1993.53 Feb 25th Commonwealth Cup Weekend 2 2024
15 Pennsylvania Loss 8-10 1625.12 Feb 25th Commonwealth Cup Weekend 2 2024
**Blowout Eligible

FAQ

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
  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
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)