#3 Ohio State (23-3)

avg: 2373  •  sd: 66.61  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
43 Georgia Tech Win 12-10 1793.71 Jan 19th Florida Winter Classic 2019
112 Central Florida** Win 12-1 1654.26 Ignored Jan 19th Florida Winter Classic 2019
38 Florida Win 10-8 1873.78 Jan 19th Florida Winter Classic 2019
26 Georgia Win 11-3 2448.3 Jan 19th Florida Winter Classic 2019
43 Georgia Tech Win 12-6 2134.9 Jan 20th Florida Winter Classic 2019
38 Florida** Win 10-4 2211.11 Ignored Jan 20th Florida Winter Classic 2019
8 Dartmouth Loss 13-14 2033.85 Jan 20th Florida Winter Classic 2019
69 Notre Dame** Win 12-2 1928.85 Ignored Feb 9th Queen City Tune Up 2019 Women
57 Cornell** Win 9-2 2060.62 Ignored Feb 9th Queen City Tune Up 2019 Women
45 Virginia** Win 12-4 2145.7 Ignored Feb 9th Queen City Tune Up 2019 Women
22 Tufts Loss 7-8 1809.61 Feb 9th Queen City Tune Up 2019 Women
1 North Carolina Loss 10-14 2131.37 Feb 10th Queen City Tune Up 2019 Women
38 Florida** Win 15-4 2211.11 Ignored Feb 10th Queen City Tune Up 2019 Women
20 North Carolina-Wilmington Win 13-12 2085.18 Feb 10th Queen City Tune Up 2019 Women
40 Michigan** Win 13-4 2169.43 Ignored Feb 23rd Commonwealth Cup 2019
20 North Carolina-Wilmington Win 8-4 2524.98 Feb 23rd Commonwealth Cup 2019
11 Pittsburgh Win 9-4 2683.27 Feb 23rd Commonwealth Cup 2019
44 Brown** Win 13-2 2154.41 Ignored Feb 24th Commonwealth Cup 2019
31 West Chester Win 12-8 2155.18 Feb 24th Commonwealth Cup 2019
1 North Carolina Win 11-9 2779.27 Feb 24th Commonwealth Cup 2019
2 California-San Diego Win 14-12 2640.02 Mar 30th NW Challenge Tier 1 Womens
32 Brigham Young** Win 15-3 2310.48 Ignored Mar 30th NW Challenge Tier 1 Womens
6 British Columbia Win 15-11 2612.93 Mar 30th NW Challenge Tier 1 Womens
13 Stanford Win 14-12 2276.59 Mar 30th NW Challenge Tier 1 Womens
5 Carleton College-Syzygy Win 15-7 2865.5 Mar 31st NW Challenge Tier 1 Womens
6 British Columbia Win 15-11 2612.93 Mar 31st NW Challenge Tier 1 Womens
**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)