#85 Wellesley (19-7)

avg: 1143.84  •  sd: 55.26  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
174 New Hampshire Win 6-3 1128.62 Mar 1st Garden State 2025
56 Rochester Win 5-4 1546.74 Mar 1st Garden State 2025
234 Cornell-B** Win 3-0 720.39 Ignored Mar 2nd Garden State 2025
91 NYU Win 6-4 1477.35 Mar 2nd Garden State 2025
56 Rochester Loss 3-4 1296.74 Mar 2nd Garden State 2025
103 Yale Win 3-2 1166.01 Mar 2nd Garden State 2025
157 Bates Win 11-2 1271.73 Mar 9th Too Hot to Handle
161 Colby Win 10-6 1156.04 Mar 9th Too Hot to Handle
47 McGill Loss 3-9 908.53 Mar 9th Too Hot to Handle
174 New Hampshire Win 8-3 1181.93 Mar 9th Too Hot to Handle
136 Massachusetts Win 11-4 1398.22 Mar 29th Northeast Classic 2025
51 Middlebury Loss 6-8 1181.19 Mar 29th Northeast Classic 2025
112 SUNY-Binghamton Win 12-4 1570.18 Mar 29th Northeast Classic 2025
86 Vermont-B Loss 7-9 860.45 Mar 29th Northeast Classic 2025
104 Amherst Win 13-7 1597.83 Mar 30th Northeast Classic 2025
94 Ithaca Loss 9-10 969.08 Mar 30th Northeast Classic 2025
162 SUNY-Geneseo Win 11-5 1246.31 Mar 30th Northeast Classic 2025
176 Bentley Win 13-4 1177.28 Apr 13th Metro Boston D III Womens Conferences 2025
190 Brandeis** Win 13-0 1088.54 Ignored Apr 13th Metro Boston D III Womens Conferences 2025
201 Stonehill** Win 13-5 1007.13 Ignored Apr 13th Metro Boston D III Womens Conferences 2025
157 Bates Win 9-4 1271.73 Apr 26th New England D III College Womens Regionals 2025
201 Stonehill** Win 14-4 1007.13 Ignored Apr 26th New England D III College Womens Regionals 2025
165 Smith Win 13-6 1228.4 Apr 26th New England D III College Womens Regionals 2025
89 Williams Loss 3-13 518.74 Apr 26th New England D III College Womens Regionals 2025
104 Amherst Loss 7-14 457.41 Apr 27th New England D III College Womens Regionals 2025
157 Bates Win 12-6 1251.04 Apr 27th New England D III College Womens Regionals 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)