#212 Auburn (4-13)

avg: 328.01  •  sd: 92.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
31 Alabama-Huntsville** Loss 0-9 1232.1 Ignored Feb 24th The Only Tenn I See Memorial Tournament
211 Vanderbilt Win 12-7 852.71 Feb 24th The Only Tenn I See Memorial Tournament
174 Kentucky Loss 3-10 118.59 Feb 24th The Only Tenn I See Memorial Tournament
33 Union (Tennessee)** Loss 3-15 1201.17 Ignored Feb 25th The Only Tenn I See Memorial Tournament
129 Illinois** Loss 2-13 437.5 Ignored Feb 25th The Only Tenn I See Memorial Tournament
194 Georgia College Win 10-9 644.2 Mar 16th Southerns 2024
127 Wisconsin-Eau Claire** Loss 3-10 441.07 Ignored Mar 16th Southerns 2024
218 Georgia-B Loss 8-11 -81.24 Mar 16th Southerns 2024
123 East Carolina Loss 4-9 473.86 Mar 16th Southerns 2024
225 North Carolina-Wilmington Win 12-11 342.16 Mar 17th Southerns 2024
123 East Carolina** Loss 1-12 473.86 Ignored Mar 17th Southerns 2024
233 Alabama-Birmingham Win 9-4 686.05 Apr 13th Gulf Coast D I Womens Conferences 2024
211 Vanderbilt Loss 8-9 207.2 Apr 13th Gulf Coast D I Womens Conferences 2024
170 Jacksonville State Loss 8-12 320.62 Apr 13th Gulf Coast D I Womens Conferences 2024
155 Tulane Loss 5-12 248.55 Apr 13th Gulf Coast D I Womens Conferences 2024
202 Alabama Loss 8-11 68.46 Apr 14th Gulf Coast D I Womens Conferences 2024
211 Vanderbilt Loss 6-7 207.2 Apr 14th Gulf Coast D I Womens Conferences 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)