#21 Ohio State (21-6)

avg: 2082.16  •  sd: 59.45  •  top 16/20: 53.3%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
59 Georgetown Win 13-3 2119.65 Jan 27th Winta Binta Vinta 2024
70 James Madison** Win 11-3 2019.57 Ignored Jan 27th Winta Binta Vinta 2024
181 Virginia-B** Win 13-0 1061.64 Ignored Jan 27th Winta Binta Vinta 2024
123 Liberty** Win 13-2 1578.52 Ignored Jan 27th Winta Binta Vinta 2024
39 Virginia Win 9-5 2267.95 Jan 28th Winta Binta Vinta 2024
88 Virginia Tech** Win 13-2 1897.26 Ignored Jan 28th Winta Binta Vinta 2024
57 William & Mary Win 10-5 2118.3 Jan 28th Winta Binta Vinta 2024
16 Georgia Loss 7-12 1634.24 Feb 10th Queen City Tune Up 2024
12 Michigan Loss 10-15 1858 Feb 10th Queen City Tune Up 2024
4 North Carolina Loss 8-15 2057.6 Feb 10th Queen City Tune Up 2024
17 Pennsylvania Win 11-10 2249.71 Feb 10th Queen City Tune Up 2024
137 Case Western Reserve** Win 15-5 1499.44 Ignored Feb 11th Queen City Tune Up 2024
39 Virginia Win 12-9 2084.26 Feb 11th Queen City Tune Up 2024
96 Chicago Win 15-7 1851 Feb 24th Commonwealth Cup Weekend 2 2024
25 Pittsburgh Loss 10-11 1837.92 Feb 24th Commonwealth Cup Weekend 2 2024
52 Yale Win 15-5 2194.05 Feb 24th Commonwealth Cup Weekend 2 2024
16 Georgia Win 9-8 2279.75 Feb 25th Commonwealth Cup Weekend 2 2024
39 Virginia Win 11-7 2205.78 Feb 25th Commonwealth Cup Weekend 2 2024
17 Pennsylvania Loss 8-10 1862.04 Feb 25th Commonwealth Cup Weekend 2 2024
41 SUNY-Binghamton Win 12-3 2303.59 Feb 25th Commonwealth Cup Weekend 2 2024
55 Southern California Win 13-5 2156.09 Mar 16th Womens Centex 2024
27 Utah Win 10-9 2046.93 Mar 16th Womens Centex 2024
37 Washington University Win 13-8 2264.81 Mar 16th Womens Centex 2024
31 Brown Loss 10-11 1749.19 Mar 17th Womens Centex 2024
96 Chicago** Win 15-2 1851 Ignored Mar 17th Womens Centex 2024
46 Texas Win 13-8 2173.15 Mar 17th Womens Centex 2024
36 Texas-Dallas Win 13-5 2393.5 Mar 17th Womens Centex 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)