#111 Vermont-B (17-10)

avg: 1418.32  •  sd: 78.16  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
221 Christopher Newport Win 13-1 1595.31 Jan 25th Mid Atlantic Warm Up 2025
282 Navy** Win 13-2 1363.93 Ignored Jan 25th Mid Atlantic Warm Up 2025
167 Pennsylvania Win 12-7 1730.1 Jan 25th Mid Atlantic Warm Up 2025
53 William & Mary Loss 7-13 1152.76 Jan 25th Mid Atlantic Warm Up 2025
39 Cincinnati Loss 10-14 1412.92 Jan 26th Mid Atlantic Warm Up 2025
70 Dartmouth Loss 10-13 1290.36 Jan 26th Mid Atlantic Warm Up 2025
61 Michigan State Loss 10-14 1259.94 Jan 26th Mid Atlantic Warm Up 2025
92 Yale Win 14-8 2026.26 Jan 26th Mid Atlantic Warm Up 2025
123 Connecticut Win 9-7 1639.78 Mar 1st UMass Invite 2025
36 Middlebury Loss 7-10 1429.31 Mar 1st UMass Invite 2025
206 Tufts-B Loss 9-10 917.31 Mar 1st UMass Invite 2025
75 Wesleyan Win 10-8 1862.03 Mar 1st UMass Invite 2025
108 Columbia Win 9-8 1568.45 Mar 2nd UMass Invite 2025
123 Connecticut Win 7-4 1856.6 Mar 2nd UMass Invite 2025
76 Williams Loss 5-12 992.52 Mar 2nd UMass Invite 2025
413 American-B** Win 15-1 299.41 Ignored Mar 22nd Atlantic Coast Open 2025
106 Appalachian State Loss 8-13 953.33 Mar 22nd Atlantic Coast Open 2025
132 Florida State Win 9-8 1452.53 Mar 22nd Atlantic Coast Open 2025
170 Messiah Win 9-7 1474.4 Mar 22nd Atlantic Coast Open 2025
179 Dickinson Win 14-12 1387.21 Mar 23rd Atlantic Coast Open 2025
63 Duke Loss 11-15 1267.62 Mar 23rd Atlantic Coast Open 2025
69 North Carolina-Charlotte Loss 11-15 1237.81 Mar 23rd Atlantic Coast Open 2025
411 Boston University-B** Win 15-1 389.91 Ignored Apr 12th New England Dev Mens Conferences 2025
262 Brown-B Win 12-7 1349.51 Apr 12th New England Dev Mens Conferences 2025
375 Harvard-B** Win 15-2 867.44 Ignored Apr 12th New England Dev Mens Conferences 2025
215 Northeastern-B Win 11-7 1482.67 Apr 12th New England Dev Mens Conferences 2025
164 Massachusetts -B Win 9-6 1639.26 Apr 13th New England Dev Mens Conferences 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)