#73 Wellesley (17-7)

avg: 1391.76  •  sd: 58.7  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
87 Bates Win 12-2 1932.33 Mar 2nd No Sleep till Brooklyn 2024
201 Columbia-B** Win 12-1 1042.28 Ignored Mar 2nd No Sleep till Brooklyn 2024
81 Wesleyan Win 10-5 1927.21 Mar 2nd No Sleep till Brooklyn 2024
110 Rutgers Win 10-6 1661.28 Mar 3rd No Sleep till Brooklyn 2024
71 Columbia Loss 4-5 1282.82 Mar 3rd No Sleep till Brooklyn 2024
86 Williams Loss 8-9 1208.84 Mar 3rd No Sleep till Brooklyn 2024
109 Brandeis Win 13-11 1394.75 Mar 9th Live Free or Sky 2024
146 New Hampshire Win 15-2 1491.89 Mar 9th Live Free or Sky 2024
68 Vermont-B Loss 5-9 908.37 Mar 30th Northeast Classic 2024
82 Rochester Win 7-6 1469.48 Mar 30th Northeast Classic 2024
81 Wesleyan Loss 6-8 1052.82 Mar 30th Northeast Classic 2024
110 Rutgers Win 11-5 1765.12 Mar 31st Northeast Classic 2024
87 Bates Loss 6-10 836.17 Mar 31st Northeast Classic 2024
146 New Hampshire Win 9-6 1310.45 Mar 31st Northeast Classic 2024
82 Rochester Loss 6-8 1043.99 Mar 31st Northeast Classic 2024
172 Stonehill Win 12-7 1270.93 Apr 20th Metro Boston D III Womens Conferences 2024
173 Bentley** Win 15-1 1329.06 Ignored Apr 20th Metro Boston D III Womens Conferences 2024
109 Brandeis Win 13-10 1494.05 Apr 20th Metro Boston D III Womens Conferences 2024
172 Stonehill** Win 15-3 1350.41 Ignored May 4th New England D III College Womens Regionals 2024
87 Bates Loss 9-10 1207.33 May 4th New England D III College Womens Regionals 2024
173 Bentley** Win 11-3 1329.06 Ignored May 4th New England D III College Womens Regionals 2024
92 Middlebury Win 10-9 1429.95 May 4th New England D III College Womens Regionals 2024
109 Brandeis Win 8-3 1765.91 May 5th New England D III College Womens Regionals 2024
105 Mount Holyoke Win 7-5 1516.75 May 5th New England D III 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)