#155 Xavier (10-11)

avg: 677.89  •  sd: 60.58  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
198 Indiana Win 9-4 1056.08 Mar 1st Huckleberry Flick 2025
49 Kenyon** Loss 3-9 898.7 Ignored Mar 1st Huckleberry Flick 2025
236 Miami (Ohio) Win 11-3 709.99 Mar 1st Huckleberry Flick 2025
254 Purdue-B** Win 10-1 490.29 Ignored Mar 1st Huckleberry Flick 2025
130 Butler Loss 3-9 245.61 Mar 2nd Huckleberry Flick 2025
124 Cincinnati Loss 9-12 566.6 Mar 2nd Huckleberry Flick 2025
199 Oberlin Win 10-4 1041.28 Mar 2nd Huckleberry Flick 2025
203 Tennessee-Chattanooga Win 11-5 995.46 Mar 22nd Moxie Madness 2025
72 Union (Tennessee) Loss 4-13 671.09 Mar 22nd Moxie Madness 2025
210 Vanderbilt Win 8-6 582.05 Mar 22nd Moxie Madness 2025
164 Alabama Win 10-8 903.98 Mar 23rd Moxie Madness 2025
156 Berry Win 9-6 1096.27 Mar 23rd Moxie Madness 2025
32 Ohio** Loss 2-13 1071.08 Ignored Mar 23rd Moxie Madness 2025
116 Cedarville Loss 3-14 347.19 Apr 12th Ohio D III Womens Conferences 2025
49 Kenyon** Loss 4-15 898.7 Ignored Apr 12th Ohio D III Womens Conferences 2025
199 Oberlin Win 11-9 690.49 Apr 12th Ohio D III Womens Conferences 2025
116 Cedarville Loss 4-15 347.19 Apr 26th Ohio Valley D III College Womens Regionals 2025
49 Kenyon** Loss 1-15 898.7 Ignored Apr 26th Ohio Valley D III College Womens Regionals 2025
119 Swarthmore Loss 5-9 410.12 Apr 26th Ohio Valley D III College Womens Regionals 2025
101 Scranton Loss 6-13 458.24 Apr 26th Ohio Valley D III College Womens Regionals 2025
199 Oberlin Win 12-5 1041.28 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)