#21 Ohio State (13-6)

avg: 2266.62  •  sd: 99.74  •  top 16/20: 56.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
57 James Madison Win 12-3 2281.44 Jan 25th Winta Binta Vinta 2025
77 Penn State** Win 12-1 2097.26 Ignored Jan 25th Winta Binta Vinta 2025
245 Virginia-B** Win 13-0 600 Ignored Jan 25th Winta Binta Vinta 2025
57 James Madison Win 13-1 2281.44 Jan 26th Winta Binta Vinta 2025
140 North Carolina-Wilmington** Win 13-0 1613.23 Ignored Jan 26th Winta Binta Vinta 2025
20 Virginia Loss 7-8 2143.93 Jan 26th Winta Binta Vinta 2025
37 American Win 13-8 2393 Feb 22nd 2025 Commonwealth Cup Weekend 2
29 Georgia Tech Loss 9-10 1933.72 Feb 22nd 2025 Commonwealth Cup Weekend 2
6 Vermont Loss 6-13 2071.2 Feb 22nd 2025 Commonwealth Cup Weekend 2
19 Notre Dame Win 11-9 2535.3 Feb 23rd 2025 Commonwealth Cup Weekend 2
23 Pennsylvania Win 11-10 2370.85 Feb 23rd 2025 Commonwealth Cup Weekend 2
6 Vermont Loss 7-14 2088.31 Feb 23rd 2025 Commonwealth Cup Weekend 2
70 Connecticut Win 11-5 2168.1 Mar 29th East Coast Invite 2025
24 Minnesota Win 9-8 2259.28 Mar 29th East Coast Invite 2025
49 William & Mary Win 11-4 2413.92 Mar 29th East Coast Invite 2025
3 Tufts Loss 7-15 2240.06 Mar 29th East Coast Invite 2025
40 Haverford/Bryn Mawr Win 10-5 2417.27 Mar 30th East Coast Invite 2025
30 Wisconsin Win 13-10 2351.12 Mar 30th East Coast Invite 2025
3 Tufts Loss 1-15 2240.06 Mar 30th East Coast Invite 2025
**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)