#115 Vermont-B (12-10)

avg: 1194.71  •  sd: 75.18  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
206 Christopher Newport Win 13-1 1378.26 Jan 25th Mid Atlantic Warm Up 2025
298 Navy** Win 13-2 988.51 Ignored Jan 25th Mid Atlantic Warm Up 2025
179 Pennsylvania Win 12-7 1432.41 Jan 25th Mid Atlantic Warm Up 2025
52 William & Mary Loss 7-13 993.96 Jan 25th Mid Atlantic Warm Up 2025
39 Cincinnati Loss 10-14 1231.34 Jan 26th Mid Atlantic Warm Up 2025
66 Dartmouth Loss 10-13 1124.11 Jan 26th Mid Atlantic Warm Up 2025
60 Michigan State Loss 10-14 1087.65 Jan 26th Mid Atlantic Warm Up 2025
101 Yale Win 14-8 1798.52 Jan 26th Mid Atlantic Warm Up 2025
120 Connecticut Win 9-7 1458.36 Mar 1st UMass Invite 2025
46 Middlebury Loss 7-10 1208.13 Mar 1st UMass Invite 2025
152 Tufts-B Loss 9-10 921.42 Mar 1st UMass Invite 2025
68 Wesleyan Win 10-8 1693.98 Mar 1st UMass Invite 2025
108 Columbia Win 9-8 1344.19 Mar 2nd UMass Invite 2025
120 Connecticut Win 7-4 1675.18 Mar 2nd UMass Invite 2025
73 Williams Loss 5-12 785.1 Mar 2nd UMass Invite 2025
388 American-B** Win 15-1 179.27 Ignored Mar 22nd Atlantic Coast Open 2025
96 Appalachian State Loss 8-13 778.26 Mar 22nd Atlantic Coast Open 2025
139 Florida State Win 9-8 1208.69 Mar 22nd Atlantic Coast Open 2025
163 Messiah Win 9-7 1260.93 Mar 22nd Atlantic Coast Open 2025
171 Dickinson Win 14-12 1174.16 Mar 23rd Atlantic Coast Open 2025
97 Duke Loss 11-15 892.26 Mar 23rd Atlantic Coast Open 2025
81 North Carolina-Charlotte Loss 11-15 941.09 Mar 23rd Atlantic Coast Open 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)