#35 Vermont-B (12-3)

avg: 1502.88  •  sd: 88.03  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
157 Amherst Win 8-6 1229.02 Feb 11th UMass Invite 2023
29 Connecticut College Loss 6-10 1088.99 Feb 11th UMass Invite 2023
316 Harvard-B** Win 12-3 600 Ignored Feb 11th UMass Invite 2023
83 Wesleyan Win 7-6 1382.68 Feb 11th UMass Invite 2023
45 Massachusetts-B Win 13-9 1858.79 Feb 12th UMass Invite 2023
114 Massachusetts-Lowell Win 15-9 1613.99 Feb 12th UMass Invite 2023
29 Connecticut College Win 13-11 1813.99 Feb 12th UMass Invite 2023
72 Army Win 13-7 1875.28 Feb 25th Bring The Huckus1
45 Massachusetts-B Loss 8-10 1177.56 Feb 25th Bring The Huckus1
199 Swarthmore** Win 13-1 1273.91 Ignored Feb 25th Bring The Huckus1
98 Penn State-B Win 11-10 1314.37 Feb 25th Bring The Huckus1
72 Army Win 12-10 1555.87 Feb 26th Bring The Huckus1
29 Connecticut College Loss 8-10 1322.48 Feb 26th Bring The Huckus1
103 Ithaca Win 13-1 1750.89 Feb 26th Bring The Huckus1
98 Penn State-B Win 10-8 1452.03 Feb 26th Bring The Huckus1
**Blowout Eligible

FAQ

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
  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
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)