#6 Vermont (14-4)

avg: 2671.2  •  sd: 87.49  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
140 North Carolina-Wilmington** Win 13-1 1613.23 Ignored Feb 15th Queen City Tune Up 2025
30 Wisconsin** Win 13-2 2622.97 Ignored Feb 15th Queen City Tune Up 2025
49 William & Mary** Win 13-4 2413.92 Ignored Feb 15th Queen City Tune Up 2025
2 Carleton College Loss 6-11 2451.87 Feb 16th Queen City Tune Up 2025
9 North Carolina Loss 3-8 1922.64 Feb 16th Queen City Tune Up 2025
37 American** Win 15-4 2496.84 Ignored Feb 22nd 2025 Commonwealth Cup Weekend 2
29 Georgia Tech** Win 14-5 2658.72 Ignored Feb 22nd 2025 Commonwealth Cup Weekend 2
21 Ohio State Win 13-6 2866.62 Feb 22nd 2025 Commonwealth Cup Weekend 2
34 Ohio** Win 14-4 2558.56 Ignored Feb 23rd 2025 Commonwealth Cup Weekend 2
21 Ohio State Win 14-7 2849.5 Feb 23rd 2025 Commonwealth Cup Weekend 2
20 Virginia Win 15-7 2868.93 Feb 23rd 2025 Commonwealth Cup Weekend 2
1 British Columbia Loss 7-13 2444.76 Mar 22nd Northwest Challenge 2025
10 California-San Diego Win 13-12 2620.97 Mar 22nd Northwest Challenge 2025
41 Southern California Win 13-6 2442.16 Mar 22nd Northwest Challenge 2025
2 Carleton College Loss 5-12 2398.57 Mar 23rd Northwest Challenge 2025
7 Michigan Win 11-6 3115.31 Mar 23rd Northwest Challenge 2025
9 North Carolina Win 8-5 2976.24 Mar 23rd Northwest Challenge 2025
15 Victoria Win 11-6 2917.84 Mar 23rd Northwest Challenge 2025
**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)