#7 Ohio State (8-3)

avg: 2069.2  •  sd: 75.86  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
117 Appalachian State** Win 13-4 1743.66 Ignored Feb 8th Queen City Tune Up 2020 Open
92 Duke** Win 12-5 1862.44 Ignored Feb 8th Queen City Tune Up 2020 Open
58 Virginia Win 13-8 1948.32 Feb 8th Queen City Tune Up 2020 Open
1 North Carolina Loss 8-11 1964.78 Feb 9th Queen City Tune Up 2020 Open
21 North Carolina State Win 12-6 2400.23 Mar 7th Smoky Mountain Invite 2020
10 Carleton College-CUT Win 11-8 2399.24 Mar 7th Smoky Mountain Invite 2020
31 Texas-Dallas Loss 12-13 1572.27 Mar 7th Smoky Mountain Invite 2020
22 Georgia Win 15-10 2270.56 Mar 7th Smoky Mountain Invite 2020
20 North Carolina-Wilmington Win 14-13 1949.97 Mar 8th Smoky Mountain Invite 2020
1 North Carolina Loss 12-15 2029.9 Mar 8th Smoky Mountain Invite 2020
13 Brown Win 14-13 2053.06 Mar 8th Smoky Mountain Invite 2020
**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)