#113 West Chester (4-16)

avg: 951.97  •  sd: 65.43  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
64 Connecticut Loss 7-10 930.15 Feb 22nd 2025 Commonwealth Cup Weekend 2
26 Georgetown** Loss 6-14 1201 Ignored Feb 22nd 2025 Commonwealth Cup Weekend 2
129 Harvard Win 9-6 1251.84 Feb 22nd 2025 Commonwealth Cup Weekend 2
57 James Madison Loss 9-10 1281.04 Feb 23rd 2025 Commonwealth Cup Weekend 2
76 Columbia Loss 7-9 940.11 Mar 29th East Coast Invite 2025
64 Connecticut Loss 4-8 755.01 Mar 29th East Coast Invite 2025
60 South Carolina Loss 5-12 784.98 Mar 29th East Coast Invite 2025
55 St Olaf Loss 6-14 841.22 Mar 29th East Coast Invite 2025
25 Northeastern Loss 6-12 1223.22 Mar 30th East Coast Invite 2025
74 Penn State Loss 6-10 763.57 Mar 30th East Coast Invite 2025
70 Carnegie Mellon Loss 5-9 750.09 Apr 12th Pennsylvania D I Womens Conferences 2025
74 Penn State Loss 5-7 931.59 Apr 12th Pennsylvania D I Womens Conferences 2025
244 Pennsylvania-B** Win 12-0 635.03 Ignored Apr 12th Pennsylvania D I Womens Conferences 2025
244 Pennsylvania-B** Win 13-0 635.03 Ignored Apr 13th Pennsylvania D I Womens Conferences 2025
109 Temple Win 7-3 1621.69 Apr 13th Pennsylvania D I Womens Conferences 2025
80 Case Western Reserve Loss 6-9 782.99 Apr 26th Ohio Valley D I College Womens Regionals 2025
22 Ohio State** Loss 2-11 1293.06 Ignored Apr 26th Ohio Valley D I College Womens Regionals 2025
74 Penn State Loss 2-9 659.73 Apr 26th Ohio Valley D I College Womens Regionals 2025
31 Pittsburgh Loss 6-8 1393.72 Apr 26th Ohio Valley D I College Womens Regionals 2025
109 Temple Loss 5-8 568.08 Apr 27th Ohio Valley D I College Womens Regionals 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)