#2 Vermont (19-1)

avg: 2789.63  •  sd: 72.26  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
38 South Carolina** Win 15-3 2363.12 Ignored Feb 10th Queen City Tune Up 2024
57 William & Mary** Win 15-1 2144.4 Ignored Feb 10th Queen City Tune Up 2024
39 Virginia** Win 15-2 2338.89 Ignored Feb 10th Queen City Tune Up 2024
29 Wisconsin** Win 15-5 2513.61 Ignored Feb 10th Queen City Tune Up 2024
3 Carleton College Loss 11-15 2325.39 Feb 11th Queen City Tune Up 2024
16 Georgia Win 11-5 2754.75 Feb 11th Queen City Tune Up 2024
7 Tufts Win 15-11 2901.34 Feb 11th Queen City Tune Up 2024
24 California-Davis** Win 12-3 2571.09 Ignored Mar 2nd Stanford Invite 2024
9 California-Santa Barbara Win 11-8 2787.41 Mar 2nd Stanford Invite 2024
25 Pittsburgh** Win 10-2 2562.92 Ignored Mar 2nd Stanford Invite 2024
6 Stanford Win 10-7 2948.29 Mar 3rd Stanford Invite 2024
1 British Columbia Win 13-12 3019.15 Mar 3rd Stanford Invite 2024
15 California-San Diego Win 10-7 2552.21 Mar 3rd Stanford Invite 2024
58 Cornell** Win 15-3 2127.53 Ignored Mar 30th East Coast Invite 2024
35 Ohio** Win 15-4 2398.14 Ignored Mar 30th East Coast Invite 2024
17 Pennsylvania Win 15-9 2640.19 Mar 30th East Coast Invite 2024
4 North Carolina Win 15-11 3003.57 Mar 30th East Coast Invite 2024
29 Wisconsin** Win 15-5 2513.61 Ignored Mar 31st East Coast Invite 2024
20 Northeastern** Win 15-3 2701.67 Ignored Mar 31st East Coast Invite 2024
4 North Carolina Win 12-10 2860.53 Mar 31st East Coast Invite 2024
**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)