#203 Oberlin (4-9)

avg: 426.88  •  sd: 66.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
224 Butler Win 11-5 820.15 Mar 2nd Huckleberry Flick
140 Indiana Loss 6-9 510.25 Mar 2nd Huckleberry Flick
159 Kenyon Loss 3-11 225.76 Mar 2nd Huckleberry Flick
209 Miami (Ohio) Win 10-9 474.33 Mar 2nd Huckleberry Flick
243 Dayton Win 8-4 369.94 Mar 3rd Huckleberry Flick
49 Ohio** Loss 3-12 1006.71 Ignored Mar 3rd Huckleberry Flick
132 Cedarville Loss 2-15 402.53 Apr 20th Ohio D III Womens Conferences 2024
159 Kenyon Loss 4-14 225.76 Apr 20th Ohio D III Womens Conferences 2024
199 Xavier Win 10-9 579.1 Apr 20th Ohio D III Womens Conferences 2024
94 Lehigh Loss 6-13 675.7 Apr 27th Ohio Valley D III College Womens Regionals 2024
168 Swarthmore Loss 3-10 177.22 Apr 27th Ohio Valley D III College Womens Regionals 2024
132 Cedarville Loss 7-14 419.64 Apr 28th Ohio Valley D III College Womens Regionals 2024
168 Swarthmore Loss 7-11 310.32 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)