#119 Swarthmore (13-11)

avg: 939.18  •  sd: 54.79  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
225 Dickinson** Win 9-2 805.4 Ignored Feb 22nd Bring The Huckus 2025
140 George Washington Loss 4-8 226.52 Feb 22nd Bring The Huckus 2025
184 Skidmore Win 9-2 1121.35 Feb 22nd Bring The Huckus 2025
161 Syracuse Win 8-6 963.8 Feb 22nd Bring The Huckus 2025
98 Lehigh Loss 7-8 965.18 Feb 23rd Bring The Huckus 2025
184 Skidmore Win 10-4 1121.35 Feb 23rd Bring The Huckus 2025
161 Syracuse Win 8-7 788.31 Feb 23rd Bring The Huckus 2025
151 Boston College Loss 7-8 568.39 Mar 22nd Jersey Devil 2025
61 Brown Loss 4-13 751.12 Mar 22nd Jersey Devil 2025
175 RIT Win 11-7 1048.61 Mar 22nd Jersey Devil 2025
90 Williams Win 8-6 1419.08 Mar 22nd Jersey Devil 2025
151 Boston College Win 9-5 1222.45 Mar 23rd Jersey Devil 2025
90 Williams Loss 10-12 880.47 Mar 23rd Jersey Devil 2025
225 Dickinson** Win 15-2 805.4 Ignored Apr 12th Pennsylvania D III Womens Conferences 2025
98 Lehigh Loss 4-7 594.02 Apr 12th Pennsylvania D III Womens Conferences 2025
202 Messiah Win 11-0 996.88 Apr 12th Pennsylvania D III Womens Conferences 2025
36 Haverford/Bryn Mawr Loss 5-11 1018.48 Apr 13th Pennsylvania D III Womens Conferences 2025
98 Lehigh Win 7-6 1215.18 Apr 13th Pennsylvania D III Womens Conferences 2025
36 Haverford/Bryn Mawr** Loss 1-13 1018.48 Ignored Apr 26th Ohio Valley D III College Womens Regionals 2025
49 Kenyon Loss 7-11 1031.8 Apr 26th Ohio Valley D III College Womens Regionals 2025
101 Scranton Win 10-9 1183.24 Apr 26th Ohio Valley D III College Womens Regionals 2025
155 Xavier Win 9-5 1206.95 Apr 26th Ohio Valley D III College Womens Regionals 2025
116 Cedarville Loss 10-11 822.19 Apr 27th Ohio Valley D III College Womens Regionals 2025
101 Scranton Loss 5-12 458.24 Apr 27th Ohio Valley D III 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)