#168 Swarthmore (10-12)

avg: 777.22  •  sd: 59.64  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
144 Skidmore Win 5-1 1512.28 Feb 24th Bring The Huckus 2024
82 Rochester Loss 3-8 744.48 Feb 24th Bring The Huckus 2024
108 West Chester Loss 4-9 579.47 Feb 24th Bring The Huckus 2024
191 Syracuse Win 7-6 677.54 Feb 24th Bring The Huckus 2024
220 Dickinson Win 10-2 873.22 Feb 25th Bring The Huckus 2024
52 Haverford/Bryn Mawr** Loss 2-10 969.87 Ignored Feb 25th Bring The Huckus 2024
193 SUNY-Geneseo Win 8-7 663.01 Feb 25th Bring The Huckus 2024
156 George Washington Loss 7-12 320.06 Mar 30th Atlantic Coast Open 2024
220 Dickinson Win 8-5 726.83 Mar 30th Atlantic Coast Open 2024
114 Richmond Loss 7-11 659.4 Mar 30th Atlantic Coast Open 2024
244 Cornell-B** Win 12-3 317.22 Ignored Mar 31st Atlantic Coast Open 2024
125 Johns Hopkins Loss 7-12 541.66 Mar 31st Atlantic Coast Open 2024
176 Mary Washington Win 13-7 1247.64 Mar 31st Atlantic Coast Open 2024
52 Haverford/Bryn Mawr** Loss 3-13 969.87 Ignored Apr 14th Pennsylvania D III Womens Conferences 2024
84 Scranton Loss 2-13 740.12 Apr 14th Pennsylvania D III Womens Conferences 2024
94 Lehigh Loss 3-7 675.7 Apr 14th Pennsylvania D III Womens Conferences 2024
207 Messiah Win 11-6 924.66 Apr 14th Pennsylvania D III Womens Conferences 2024
132 Cedarville Loss 5-11 402.53 Apr 27th Ohio Valley D III College Womens Regionals 2024
52 Haverford/Bryn Mawr Loss 5-9 1040.81 Apr 27th Ohio Valley D III College Womens Regionals 2024
84 Scranton Loss 3-10 740.12 Apr 27th Ohio Valley D III College Womens Regionals 2024
203 Oberlin Win 10-3 1026.88 Apr 27th Ohio Valley D III College Womens Regionals 2024
203 Oberlin Win 11-7 893.78 Apr 28th Ohio Valley D III 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)