#81 Wesleyan (15-6)

avg: 1353.32  •  sd: 76.23  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
87 Bates Win 9-6 1750.89 Mar 2nd No Sleep till Brooklyn 2024
201 Columbia-B** Win 11-2 1042.28 Ignored Mar 2nd No Sleep till Brooklyn 2024
73 Wellesley Loss 5-10 817.86 Mar 2nd No Sleep till Brooklyn 2024
105 Mount Holyoke Win 9-7 1467.95 Mar 3rd No Sleep till Brooklyn 2024
111 NYU Loss 5-7 812.98 Mar 3rd No Sleep till Brooklyn 2024
86 Williams Loss 8-9 1208.84 Mar 3rd No Sleep till Brooklyn 2024
68 Vermont-B Loss 6-7 1312.43 Mar 30th Northeast Classic 2024
82 Rochester Loss 9-10 1219.48 Mar 30th Northeast Classic 2024
73 Wellesley Win 8-6 1692.25 Mar 30th Northeast Classic 2024
87 Bates Loss 4-8 767.52 Mar 31st Northeast Classic 2024
68 Vermont-B Win 12-8 1878.59 Mar 31st Northeast Classic 2024
95 McGill Win 11-7 1722.83 Mar 31st Northeast Classic 2024
146 New Hampshire Win 11-6 1438.58 Mar 31st Northeast Classic 2024
144 Skidmore Win 10-8 1174.95 Apr 14th Eastern Metro East D III Womens Conferences 2024
192 Connecticut College Win 7-3 1151.06 Apr 14th Eastern Metro East D III Womens Conferences 2024
215 Vassar** Win 13-0 915.55 Ignored Apr 14th Eastern Metro East D III Womens Conferences 2024
228 Rensselaer Polytech** Win 13-0 781.36 Ignored Apr 14th Eastern Metro East D III Womens Conferences 2024
144 Skidmore Win 11-7 1379.18 Apr 27th Metro East D III College Womens Regionals 2024
215 Vassar** Win 14-0 915.55 Ignored Apr 27th Metro East D III College Womens Regionals 2024
142 Ithaca Win 8-5 1373.8 Apr 28th Metro East D III College Womens Regionals 2024
82 Rochester Win 11-10 1469.48 Apr 28th Metro East 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)