#56 Cornell (14-7)

avg: 1523.94  •  sd: 53.73  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
44 Emory Win 10-9 1733.19 Jan 31st Florida Warm Up 2025
134 South Florida Win 9-5 1641.46 Jan 31st Florida Warm Up 2025
36 Michigan Loss 8-11 1287.17 Jan 31st Florida Warm Up 2025
15 Washington University Loss 7-13 1394.1 Feb 1st Florida Warm Up 2025
38 Utah State Loss 9-13 1214.06 Feb 1st Florida Warm Up 2025
139 Florida State Win 13-9 1502.25 Feb 1st Florida Warm Up 2025
79 Florida Win 10-9 1472.88 Feb 2nd Florida Warm Up 2025
43 Virginia Tech Loss 7-13 1054.67 Feb 2nd Florida Warm Up 2025
116 West Chester Win 11-7 1659.06 Mar 1st Oak Creek Challenge 2025
226 SUNY-Albany** Win 10-1 1297.07 Ignored Mar 1st Oak Creek Challenge 2025
105 Liberty Win 13-2 1832.91 Mar 1st Oak Creek Challenge 2025
138 RIT Win 13-7 1650.45 Mar 2nd Oak Creek Challenge 2025
132 Rutgers Loss 8-9 991.55 Mar 2nd Oak Creek Challenge 2025
157 Johns Hopkins Win 13-5 1605.84 Mar 2nd Oak Creek Challenge 2025
98 Boston College Win 11-10 1396.7 Mar 29th East Coast Invite 2025
71 Case Western Reserve Win 9-8 1530.06 Mar 29th East Coast Invite 2025
108 Columbia Win 15-8 1784 Mar 29th East Coast Invite 2025
48 Maryland Loss 10-11 1440.66 Mar 29th East Coast Invite 2025
52 William & Mary Win 14-11 1864.83 Mar 30th East Coast Invite 2025
48 Maryland Loss 8-10 1303 Mar 30th East Coast Invite 2025
102 Syracuse Win 10-5 1834.29 Mar 30th East Coast Invite 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)