#58 Cornell (15-8)

avg: 1526.29  •  sd: 56.82  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
110 Rutgers Win 8-6 1465.61 Mar 2nd No Sleep till Brooklyn 2024
180 SUNY-Buffalo** Win 12-1 1220.02 Ignored Mar 2nd No Sleep till Brooklyn 2024
105 Mount Holyoke Win 9-7 1467.95 Mar 2nd No Sleep till Brooklyn 2024
71 Columbia Win 8-5 1861.42 Mar 3rd No Sleep till Brooklyn 2024
111 NYU Win 10-7 1530.79 Mar 3rd No Sleep till Brooklyn 2024
86 Williams Win 10-7 1723.5 Mar 3rd No Sleep till Brooklyn 2024
112 Carnegie Mellon Win 10-6 1630.19 Mar 30th East Coast Invite 2024
108 West Chester Win 11-5 1779.47 Mar 30th East Coast Invite 2024
2 Vermont** Loss 3-15 2077.17 Ignored Mar 30th East Coast Invite 2024
49 Ohio Loss 5-8 1153.1 Mar 30th East Coast Invite 2024
27 Brown Loss 6-11 1322.45 Mar 31st East Coast Invite 2024
39 SUNY-Binghamton Loss 9-10 1595.35 Mar 31st East Coast Invite 2024
54 Ottawa Win 6-5 1679.91 Apr 20th Western NY D I Womens Conferences 2024
180 SUNY-Buffalo** Win 13-1 1220.02 Ignored Apr 20th Western NY D I Womens Conferences 2024
39 SUNY-Binghamton Loss 6-13 1120.35 Apr 20th Western NY D I Womens Conferences 2024
54 Ottawa Loss 4-11 954.91 Apr 20th Western NY D I Womens Conferences 2024
57 Connecticut Win 11-10 1654.14 Apr 27th Metro East D I College Womens Regionals 2024
44 Yale Loss 9-11 1397.28 Apr 27th Metro East D I College Womens Regionals 2024
150 RIT** Win 13-2 1468.05 Ignored Apr 27th Metro East D I College Womens Regionals 2024
111 NYU Win 13-3 1741.12 Apr 27th Metro East D I College Womens Regionals 2024
71 Columbia Win 12-10 1645.94 Apr 28th Metro East D I College Womens Regionals 2024
54 Ottawa Win 10-7 1944.57 Apr 28th Metro East D I College Womens Regionals 2024
39 SUNY-Binghamton Loss 12-15 1419.86 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)