#101 Cornell (10-11)

avg: 1224.57  •  sd: 64.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
12 Alabama-Huntsville Loss 8-13 1497.51 Feb 2nd Florida Warm Up 2024
10 Carleton College Loss 8-13 1514.18 Feb 2nd Florida Warm Up 2024
185 South Florida Win 13-5 1466.3 Feb 2nd Florida Warm Up 2024
74 Cincinnati Win 13-11 1590.04 Feb 3rd Florida Warm Up 2024
20 Northeastern Loss 6-13 1230.32 Feb 3rd Florida Warm Up 2024
41 Florida Loss 12-13 1446.02 Feb 4th Florida Warm Up 2024
19 Washington University Loss 11-12 1740.17 Feb 4th Florida Warm Up 2024
90 SUNY-Buffalo Loss 1-13 675.01 Mar 2nd Oak Creek Challenge 2024
280 Drexel Win 13-6 1073.63 Mar 2nd Oak Creek Challenge 2024
156 Johns Hopkins Win 13-4 1619.03 Mar 2nd Oak Creek Challenge 2024
84 Appalachian State Loss 9-13 908.24 Mar 3rd Oak Creek Challenge 2024
85 Carnegie Mellon Win 13-12 1443.33 Mar 3rd Oak Creek Challenge 2024
130 Towson Loss 11-13 887.99 Mar 3rd Oak Creek Challenge 2024
167 Columbia Win 10-9 1083.25 Mar 30th East Coast Invite 2024
146 Yale Loss 7-10 670.39 Mar 30th East Coast Invite 2024
123 Pennsylvania Loss 10-11 1022.48 Mar 30th East Coast Invite 2024
154 Harvard Win 11-10 1148.19 Mar 30th East Coast Invite 2024
150 Navy Win 13-9 1452.86 Mar 31st East Coast Invite 2024
107 Princeton Win 11-10 1333.6 Mar 31st East Coast Invite 2024
25 McGill Loss 4-13 1174.26 Mar 31st East Coast Invite 2024
123 Pennsylvania Win 7-6 1272.48 Mar 31st East Coast Invite 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)