#33 Ohio State (12-7)

avg: 1633.9  •  sd: 55.69  •  top 16/20: 1.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
80 American Win 10-1 1773.83 Jan 28th Winta Binta Vinta
130 Liberty** Win 9-2 1373.74 Ignored Jan 28th Winta Binta Vinta
145 Virginia-B** Win 11-2 1255.94 Ignored Jan 28th Winta Binta Vinta
62 William & Mary Win 10-4 1877.48 Jan 28th Winta Binta Vinta
62 William & Mary Win 9-0 1877.48 Jan 29th Winta Binta Vinta
14 Virginia Loss 7-12 1410.15 Jan 29th Winta Binta Vinta
47 Florida Win 12-8 1909.05 Feb 25th Commonwealth Cup Weekend2 2023
71 Massachusetts Win 15-7 1833.48 Feb 25th Commonwealth Cup Weekend2 2023
95 Temple** Win 15-6 1629.11 Ignored Feb 25th Commonwealth Cup Weekend2 2023
30 South Carolina Loss 7-9 1381.46 Feb 26th Commonwealth Cup Weekend2 2023
89 Columbia Win 11-8 1444.27 Feb 26th Commonwealth Cup Weekend2 2023
14 Virginia Loss 8-13 1434.5 Feb 26th Commonwealth Cup Weekend2 2023
18 Colorado State Loss 7-13 1253.97 Mar 18th Womens Centex1
10 Northeastern Loss 4-13 1534.33 Mar 18th Womens Centex1
44 Pennsylvania Win 11-7 1951.2 Mar 18th Womens Centex1
36 Brown Win 13-10 1908.21 Mar 19th Womens Centex1
31 California Loss 10-12 1419.84 Mar 19th Womens Centex1
14 Virginia Loss 8-13 1434.5 Mar 19th Womens Centex1
35 Michigan Win 12-10 1857.7 Mar 19th Womens Centex1
**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)