#146 Yale (9-12)

avg: 1060.05  •  sd: 70.02  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
183 Connecticut College Loss 8-10 626.03 Feb 10th UMass Invite 2024
62 Massachusetts -B Loss 5-11 831.78 Feb 10th UMass Invite 2024
153 Rhode Island Win 10-9 1151.41 Feb 10th UMass Invite 2024
148 Rochester Loss 7-8 911.81 Feb 10th UMass Invite 2024
153 Rhode Island Loss 5-8 572.8 Feb 11th UMass Invite 2024
270 Rowan Win 12-5 1111.96 Feb 11th UMass Invite 2024
162 Wesleyan Win 13-9 1404.21 Feb 11th UMass Invite 2024
46 Williams Loss 6-10 1028.8 Feb 11th UMass Invite 2024
167 Columbia Loss 7-10 568.59 Mar 2nd No Sleep till Brooklyn 2024
282 Hofstra** Win 6-0 1049.47 Ignored Mar 2nd No Sleep till Brooklyn 2024
236 MIT Win 12-5 1280.57 Mar 2nd No Sleep till Brooklyn 2024
86 Bates Loss 9-10 1192.07 Mar 3rd No Sleep till Brooklyn 2024
196 NYU Win 10-8 1103.86 Mar 3rd No Sleep till Brooklyn 2024
46 Williams Loss 9-13 1106.4 Mar 3rd No Sleep till Brooklyn 2024
167 Columbia Loss 8-13 462.09 Mar 30th East Coast Invite 2024
101 Cornell Win 10-7 1614.23 Mar 30th East Coast Invite 2024
154 Harvard Win 13-8 1519.35 Mar 30th East Coast Invite 2024
123 Pennsylvania Win 10-7 1537.15 Mar 30th East Coast Invite 2024
70 Case Western Reserve Loss 9-11 1117.51 Mar 31st East Coast Invite 2024
96 Connecticut Loss 7-12 728.89 Mar 31st East Coast Invite 2024
98 Dartmouth Loss 9-11 996.43 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)