#112 Carnegie Mellon (4-19)

avg: 1134.03  •  sd: 66.19  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
56 North Carolina State Loss 4-15 943.77 Feb 24th Commonwealth Cup Weekend 2 2024
12 Michigan** Loss 2-15 1599.69 Ignored Feb 24th Commonwealth Cup Weekend 2 2024
39 SUNY-Binghamton Loss 12-14 1499.4 Feb 24th Commonwealth Cup Weekend 2 2024
23 Notre Dame** Loss 1-13 1331.53 Ignored Feb 25th Commonwealth Cup Weekend 2 2024
49 Ohio Loss 5-10 1032.81 Feb 25th Commonwealth Cup Weekend 2 2024
99 Chicago Win 10-3 1819.77 Feb 25th Commonwealth Cup Weekend 2 2024
21 Northeastern** Loss 2-8 1359.88 Ignored Mar 30th East Coast Invite 2024
71 Columbia Win 7-6 1532.82 Mar 30th East Coast Invite 2024
58 Cornell Loss 6-10 1030.13 Mar 30th East Coast Invite 2024
49 Ohio Loss 6-13 1006.71 Mar 30th East Coast Invite 2024
39 SUNY-Binghamton Loss 6-12 1141.04 Mar 31st East Coast Invite 2024
51 Virginia Loss 4-12 977.69 Mar 31st East Coast Invite 2024
16 Pennsylvania** Loss 4-12 1499.13 Ignored Apr 13th Pennsylvania D I Womens Conferences 2024
108 West Chester Loss 6-13 579.47 Apr 13th Pennsylvania D I Womens Conferences 2024
121 Temple Win 11-10 1203.5 Apr 13th Pennsylvania D I Womens Conferences 2024
16 Pennsylvania** Loss 6-14 1499.13 Ignored Apr 14th Pennsylvania D I Womens Conferences 2024
64 Penn State Loss 6-10 980.82 Apr 14th Pennsylvania D I Womens Conferences 2024
24 Ohio State** Loss 4-10 1293.87 Ignored Apr 27th Ohio Valley D I College Womens Regionals 2024
22 Pittsburgh** Loss 4-13 1334.21 Ignored Apr 27th Ohio Valley D I College Womens Regionals 2024
121 Temple Loss 6-7 953.5 Apr 27th Ohio Valley D I College Womens Regionals 2024
80 Cincinnati Win 9-6 1773.08 Apr 27th Ohio Valley D I College Womens Regionals 2024
64 Penn State Loss 5-9 947.92 Apr 28th Ohio Valley D I College Womens Regionals 2024
108 West Chester Loss 9-10 1054.47 Apr 28th Ohio Valley 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)