#110 Rutgers (11-13)

avg: 1165.12  •  sd: 70.72  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
58 Cornell Loss 6-8 1225.79 Mar 2nd No Sleep till Brooklyn 2024
105 Mount Holyoke Loss 6-8 888.12 Mar 2nd No Sleep till Brooklyn 2024
73 Wellesley Loss 6-10 895.6 Mar 3rd No Sleep till Brooklyn 2024
105 Mount Holyoke Win 6-4 1554.22 Mar 3rd No Sleep till Brooklyn 2024
111 NYU Loss 6-7 1016.12 Mar 3rd No Sleep till Brooklyn 2024
121 Temple Win 8-3 1678.5 Mar 30th Northeast Classic 2024
105 Mount Holyoke Win 9-4 1788.61 Mar 30th Northeast Classic 2024
192 Connecticut College** Win 10-2 1151.06 Ignored Mar 30th Northeast Classic 2024
73 Wellesley Loss 5-11 791.76 Mar 31st Northeast Classic 2024
150 RIT Win 6-4 1233.66 Mar 31st Northeast Classic 2024
130 Boston University Win 8-4 1585.74 Mar 31st Northeast Classic 2024
105 Mount Holyoke Win 7-2 1788.61 Mar 31st Northeast Classic 2024
239 SUNY-Albany** Win 12-0 584.99 Ignored Apr 13th Eastern Metro East D I Womens Conferences 2024
164 SUNY-Stony Brook Win 7-3 1406.94 Apr 13th Eastern Metro East D I Womens Conferences 2024
57 Connecticut Loss 3-9 929.14 Apr 13th Eastern Metro East D I Womens Conferences 2024
111 NYU Win 9-5 1670.18 Apr 13th Eastern Metro East D I Womens Conferences 2024
44 Yale Loss 10-13 1318.34 Apr 14th Eastern Metro East D I Womens Conferences 2024
111 NYU Loss 6-8 840.63 Apr 14th Eastern Metro East D I Womens Conferences 2024
71 Columbia Loss 6-13 807.82 Apr 27th Metro East D I College Womens Regionals 2024
39 SUNY-Binghamton Loss 6-11 1173.66 Apr 27th Metro East D I College Womens Regionals 2024
180 SUNY-Buffalo Win 7-5 948.16 Apr 27th Metro East D I College Womens Regionals 2024
54 Ottawa Loss 3-13 954.91 Apr 27th Metro East D I College Womens Regionals 2024
44 Yale Loss 7-15 1046.48 Apr 28th Metro East D I College Womens Regionals 2024
111 NYU Loss 10-13 812.98 Apr 28th Metro East 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)