#100 Vermont-B (17-5)

avg: 1393.49  •  sd: 53.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
195 Amherst Win 8-6 1258.39 Feb 11th UMass Invite 2023
80 Connecticut College Loss 6-10 976.11 Feb 11th UMass Invite 2023
315 Harvard-B** Win 12-3 943.5 Ignored Feb 11th UMass Invite 2023
160 Wesleyan Win 7-6 1237.09 Feb 11th UMass Invite 2023
95 Massachusetts-B Win 13-9 1840.78 Feb 12th UMass Invite 2023
154 Massachusetts-Lowell Win 15-9 1652.31 Feb 12th UMass Invite 2023
80 Connecticut College Win 13-11 1701.11 Feb 12th UMass Invite 2023
144 Army Win 13-7 1733.03 Feb 25th Bring The Huckus1
95 Massachusetts-B Loss 8-10 1159.55 Feb 25th Bring The Huckus1
263 Swarthmore** Win 13-1 1281.29 Ignored Feb 25th Bring The Huckus1
165 Penn State-B Win 11-10 1223.45 Feb 25th Bring The Huckus1
144 Army Win 12-10 1413.62 Feb 26th Bring The Huckus1
80 Connecticut College Loss 8-10 1209.6 Feb 26th Bring The Huckus1
170 Ithaca Win 13-1 1678.23 Feb 26th Bring The Huckus1
165 Penn State-B Win 10-8 1361.12 Feb 26th Bring The Huckus1
95 Massachusetts-B Loss 6-9 1003.65 Apr 1st Fuego2
208 Rhode Island Win 12-5 1505.27 Apr 1st Fuego2
175 Rowan Win 9-5 1577.22 Apr 1st Fuego2
185 West Chester Win 8-7 1129.65 Apr 1st Fuego2
66 Bowdoin Loss 9-10 1439.7 Apr 2nd Fuego2
119 College of New Jersey Win 11-10 1422.25 Apr 2nd Fuego2
160 Wesleyan Win 12-10 1350.21 Apr 2nd Fuego2
**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)