#12 Minnesota (13-9)

avg: 2070.91  •  sd: 42.4  •  top 16/20: 99.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
3 Massachusetts Loss 11-13 2082.57 Feb 3rd Florida Warm Up 2023
5 Vermont Win 13-10 2538.21 Feb 3rd Florida Warm Up 2023
104 Florida State** Win 13-4 1945.01 Ignored Feb 3rd Florida Warm Up 2023
72 Auburn Win 13-6 2097.8 Feb 4th Florida Warm Up 2023
67 Virginia Tech Win 13-5 2153.29 Feb 4th Florida Warm Up 2023
23 Wisconsin Win 15-13 2108.7 Feb 4th Florida Warm Up 2023
3 Massachusetts Loss 13-15 2097.23 Feb 5th Florida Warm Up 2023
19 Georgia Win 13-12 2075.85 Feb 5th Florida Warm Up 2023
21 Northeastern Win 12-10 2145.29 Mar 4th Smoky Mountain Invite
15 UCLA Win 12-11 2153.29 Mar 4th Smoky Mountain Invite
8 Pittsburgh Loss 9-13 1736.61 Mar 4th Smoky Mountain Invite
4 Texas Loss 11-13 1985.91 Mar 4th Smoky Mountain Invite
11 Brown Loss 12-15 1774.23 Mar 5th Smoky Mountain Invite
19 Georgia Win 15-12 2251.35 Mar 5th Smoky Mountain Invite
20 North Carolina State Win 14-12 2166.36 Mar 5th Smoky Mountain Invite
30 Ohio State Win 13-7 2393.5 Apr 1st Easterns 2023
21 Northeastern Win 13-9 2325.74 Apr 1st Easterns 2023
25 North Carolina-Wilmington Win 13-11 2113.1 Apr 1st Easterns 2023
1 North Carolina Loss 7-13 1835.12 Apr 1st Easterns 2023
11 Brown Loss 12-14 1853.76 Apr 2nd Easterns 2023
8 Pittsburgh Loss 11-15 1774.01 Apr 2nd Easterns 2023
9 Oregon Loss 13-14 2012.14 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)