#40 Haverford/Bryn Mawr (13-7)

avg: 1843.37  •  sd: 91.05  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
143 SUNY-Geneseo** Win 9-1 1600.08 Ignored Feb 22nd Bring The Huckus 2025
90 Ithaca Win 8-5 1829.67 Feb 22nd Bring The Huckus 2025
47 Wesleyan Loss 6-8 1520.47 Feb 22nd Bring The Huckus 2025
102 Lehigh Loss 5-7 948.12 Feb 22nd Bring The Huckus 2025
113 George Washington** Win 10-3 1811.98 Ignored Feb 23rd Bring The Huckus 2025
102 Lehigh Win 10-4 1876.26 Feb 23rd Bring The Huckus 2025
47 Wesleyan Loss 9-11 1571.76 Feb 23rd Bring The Huckus 2025
235 American-B** Win 13-0 812.98 Ignored Mar 1st Cherry Blossom Classic 2025
197 Towson** Win 13-1 1193.74 Ignored Mar 1st Cherry Blossom Classic 2025
168 Miami (Florida)** Win 13-0 1410.09 Ignored Mar 1st Cherry Blossom Classic 2025
37 American Loss 9-10 1771.84 Mar 2nd Cherry Blossom Classic 2025
111 SUNY-Binghamton** Win 9-1 1834.66 Ignored Mar 2nd Cherry Blossom Classic 2025
77 Penn State Win 9-1 2097.26 Mar 2nd Cherry Blossom Classic 2025
38 Duke Loss 7-8 1765.8 Mar 29th East Coast Invite 2025
78 Mount Holyoke Win 10-7 1880.83 Mar 29th East Coast Invite 2025
31 Pittsburgh Win 10-8 2277.18 Mar 29th East Coast Invite 2025
66 St Olaf Win 10-7 2013.45 Mar 29th East Coast Invite 2025
23 Pennsylvania Win 9-8 2370.85 Mar 30th East Coast Invite 2025
19 Notre Dame Loss 8-14 1750.06 Mar 30th East Coast Invite 2025
21 Ohio State Loss 5-10 1692.72 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)