#72 Union (Tennessee) (11-1)

avg: 1271.09  •  sd: 69.88  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
172 Florida State Win 8-4 1165.19 Feb 22nd Mardi Gras XXXVII
125 Jacksonville State Win 12-2 1486.38 Feb 22nd Mardi Gras XXXVII
136 Trinity Win 9-5 1335.86 Feb 22nd Mardi Gras XXXVII
136 Trinity Win 10-8 1069.47 Feb 22nd Mardi Gras XXXVII
203 Tennessee-Chattanooga** Win 10-2 995.46 Ignored Mar 22nd Moxie Madness 2025
155 Xavier Win 13-4 1277.89 Mar 22nd Moxie Madness 2025
210 Vanderbilt** Win 13-1 881.56 Ignored Mar 22nd Moxie Madness 2025
234 Auburn** Win 13-1 725.37 Ignored Mar 23rd Moxie Madness 2025
156 Berry Win 11-3 1277.7 Mar 23rd Moxie Madness 2025
32 Ohio Loss 2-11 1071.08 Mar 23rd Moxie Madness 2025
156 Berry Win 15-8 1242.51 Apr 12th Southeast D III Womens Conferences 2025
127 Georgia College Win 15-5 1456.5 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)