#199 Oberlin (1-16)

avg: 441.28  •  sd: 81.34  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
130 Butler Loss 6-9 427.05 Mar 1st Huckleberry Flick 2025
124 Cincinnati Loss 3-9 311.96 Mar 1st Huckleberry Flick 2025
198 Indiana Loss 4-6 90.47 Mar 1st Huckleberry Flick 2025
236 Miami (Ohio) Win 12-3 709.99 Mar 2nd Huckleberry Flick 2025
155 Xavier Loss 4-10 77.89 Mar 2nd Huckleberry Flick 2025
58 Davenport Loss 4-5 1278.44 Mar 15th Davenport Spring Skirmish
135 Grand Valley Loss 4-8 250.45 Mar 15th Davenport Spring Skirmish
110 Michigan State Loss 6-8 705.45 Mar 15th Davenport Spring Skirmish
88 Michigan Tech** Loss 2-6 535.42 Ignored Mar 15th Davenport Spring Skirmish
116 Cedarville Loss 2-15 347.19 Apr 12th Ohio D III Womens Conferences 2025
49 Kenyon** Loss 0-15 898.7 Ignored Apr 12th Ohio D III Womens Conferences 2025
155 Xavier Loss 9-11 428.69 Apr 12th Ohio D III Womens Conferences 2025
116 Cedarville Loss 9-11 697.98 Apr 26th Ohio Valley D III College Womens Regionals 2025
36 Haverford/Bryn Mawr** Loss 1-15 1018.48 Ignored Apr 26th Ohio Valley D III College Womens Regionals 2025
98 Lehigh** Loss 2-15 490.18 Ignored Apr 26th Ohio Valley D III College Womens Regionals 2025
101 Scranton** Loss 2-9 458.24 Ignored Apr 26th Ohio Valley D III College Womens Regionals 2025
155 Xavier Loss 5-12 77.89 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)