#30 Ohio State (9-11)

avg: 1835.97  •  sd: 66.18  •  top 16/20: 5.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
107 Tennessee Win 14-6 1941.72 Feb 11th Queen City Tune Up1
49 Notre Dame Win 15-7 2243.26 Feb 11th Queen City Tune Up1
51 Virginia Win 12-11 1760.46 Feb 11th Queen City Tune Up1
41 William & Mary Win 14-11 2032.22 Feb 11th Queen City Tune Up1
1 North Carolina Loss 8-10 2129.99 Feb 12th Queen City Tune Up1
20 North Carolina State Loss 8-13 1449.24 Feb 12th Queen City Tune Up1
14 Carleton College Loss 10-12 1811.75 Mar 4th Smoky Mountain Invite
6 Colorado Loss 10-13 1869.43 Mar 4th Smoky Mountain Invite
19 Georgia Win 13-10 2279 Mar 4th Smoky Mountain Invite
5 Vermont Loss 6-13 1610.06 Mar 4th Smoky Mountain Invite
72 Auburn Win 15-7 2097.8 Mar 5th Smoky Mountain Invite
19 Georgia Win 14-10 2349.56 Mar 5th Smoky Mountain Invite
21 Northeastern Loss 13-15 1692.99 Mar 5th Smoky Mountain Invite
12 Minnesota Loss 7-13 1513.38 Apr 1st Easterns 2023
1 North Carolina Loss 2-13 1792.65 Apr 1st Easterns 2023
25 North Carolina-Wilmington Loss 9-11 1635.05 Apr 1st Easterns 2023
21 Northeastern Loss 7-12 1386.66 Apr 1st Easterns 2023
72 Auburn Win 15-3 2097.8 Apr 2nd Easterns 2023
34 Michigan Win 11-9 2038.03 Apr 2nd Easterns 2023
19 Georgia Loss 6-14 1350.85 Apr 2nd Easterns 2023
**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)