#68 Vermont-B (19-5)

avg: 1437.43  •  sd: 71.64  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
230 Clark** Win 13-0 755.08 Ignored Mar 23rd New England Open 2024
78 Harvard Win 8-4 1929.71 Mar 23rd New England Open 2024
101 Rhode Island Win 11-10 1338.69 Mar 23rd New England Open 2024
126 Massachusetts Win 13-3 1642.94 Mar 23rd New England Open 2024
173 Bentley Win 9-4 1329.06 Mar 24th New England Open 2024
57 Connecticut Loss 5-13 929.14 Mar 24th New England Open 2024
126 Massachusetts Win 11-5 1642.94 Mar 24th New England Open 2024
73 Wellesley Win 9-5 1920.82 Mar 30th Northeast Classic 2024
81 Wesleyan Win 7-6 1478.32 Mar 30th Northeast Classic 2024
82 Rochester Win 9-8 1469.48 Mar 30th Northeast Classic 2024
144 Skidmore Win 8-6 1212.78 Mar 31st Northeast Classic 2024
52 Haverford/Bryn Mawr Loss 6-8 1269.38 Mar 31st Northeast Classic 2024
101 Rhode Island Win 9-4 1813.69 Mar 31st Northeast Classic 2024
81 Wesleyan Loss 8-12 912.16 Mar 31st Northeast Classic 2024
208 Brown-B** Win 15-2 968.51 Ignored Apr 20th New England Dev Womens Conferences 2024
181 Vermont-C** Win 10-2 1210.94 Ignored Apr 20th New England Dev Womens Conferences 2024
216 Northeastern-B** Win 15-3 905.25 Ignored Apr 20th New England Dev Womens Conferences 2024
181 Vermont-C Win 11-9 860.14 Apr 21st New England Dev Womens Conferences 2024
122 Boston College Win 11-4 1675.76 May 4th New England D I College Womens Regionals 2024
27 Brown Loss 5-14 1269.14 May 4th New England D I College Womens Regionals 2024
181 Vermont-C** Win 14-5 1210.94 Ignored May 4th New England D I College Womens Regionals 2024
101 Rhode Island Win 12-9 1559.06 May 4th New England D I College Womens Regionals 2024
21 Northeastern Loss 9-15 1444.4 May 5th New England D I College Womens Regionals 2024
90 MIT Win 11-7 1773.29 May 5th New England D I College Womens Regionals 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)