#19 Ohio State (7-6)

avg: 1745.87  •  sd: 69.73  •  top 16/20: 53%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
108 Tennessee** Win 14-6 1745.82 Ignored Feb 11th Queen City Tune Up1
70 Notre Dame Win 15-7 1955.69 Feb 11th Queen City Tune Up1
50 Virginia Win 12-11 1568.6 Feb 11th Queen City Tune Up1
39 William & Mary Win 14-11 1848.78 Feb 11th Queen City Tune Up1
2 North Carolina Loss 8-10 1875.72 Feb 12th Queen City Tune Up1
15 North Carolina State Loss 8-13 1309.12 Feb 12th Queen City Tune Up1
12 Carleton College Loss 10-12 1625.37 Mar 4th Smoky Mountain Invite
6 Colorado Loss 10-13 1664.25 Mar 4th Smoky Mountain Invite
21 Georgia Win 13-10 2064.46 Mar 4th Smoky Mountain Invite
5 Vermont Loss 6-13 1410.71 Mar 4th Smoky Mountain Invite
58 Auburn Win 15-7 2000.05 Mar 5th Smoky Mountain Invite
21 Georgia Win 14-10 2135.02 Mar 5th Smoky Mountain Invite
27 Northeastern Loss 13-15 1468.27 Mar 5th Smoky Mountain Invite
**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)