#155 Berry (8-9)

avg: 677.43  •  sd: 76.58  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
163 Alabama Win 10-6 1137.04 Jan 25th T Town Throwdown XX
209 Vanderbilt Win 8-5 734.42 Jan 25th T Town Throwdown XX
163 Alabama Win 10-6 1137.04 Jan 26th T Town Throwdown XX
163 Alabama Win 8-7 765.88 Mar 22nd Moxie Madness 2025
233 Auburn Win 10-4 724.76 Mar 22nd Moxie Madness 2025
32 Ohio Loss 7-11 1204.87 Mar 22nd Moxie Madness 2025
203 Tennessee-Chattanooga Win 10-5 968.9 Mar 23rd Moxie Madness 2025
71 Union (Tennessee) Loss 3-11 670.35 Mar 23rd Moxie Madness 2025
154 Xavier Loss 6-9 258.91 Mar 23rd Moxie Madness 2025
78 Appalachian State Loss 2-10 615.16 Mar 29th Needle in a Ho Stack 2025
219 Florida Tech Win 9-6 635.36 Mar 29th Needle in a Ho Stack 2025
81 Tennessee Loss 3-12 596.93 Mar 29th Needle in a Ho Stack 2025
258 Emory-B** Win 13-1 286.02 Ignored Mar 29th Needle in a Ho Stack 2025
126 North Carolina-Wilmington Loss 7-8 722.29 Mar 30th Needle in a Ho Stack 2025
159 Charleston Loss 2-7 65 Mar 30th Needle in a Ho Stack 2025
71 Union (Tennessee) Loss 8-15 705.55 Apr 12th Southeast D III Womens Conferences 2025
125 Georgia College Loss 5-14 256.15 Apr 12th Southeast D III Womens Conferences 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)