#189 LSU (7-6)

avg: 655.82  •  sd: 97.7  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
156 Alabama Loss 3-11 338.62 Feb 22nd Mardi Gras XXXVII
232 Alabama-Birmingham Win 8-3 845.82 Feb 22nd Mardi Gras XXXVII
222 Auburn Win 10-8 630.47 Feb 22nd Mardi Gras XXXVII
240 Mississippi State Win 6-4 319.38 Feb 22nd Mardi Gras XXXVII
67 Florida** Loss 3-12 1002.63 Ignored Mar 15th Tally Classic XIX
163 Florida State Loss 7-8 722.3 Mar 15th Tally Classic XIX
203 Florida Tech Win 8-5 991.93 Mar 15th Tally Classic XIX
223 Minnesota-Duluth Win 9-7 645.16 Mar 15th Tally Classic XIX
46 Texas-Dallas** Loss 2-13 1222.28 Ignored Mar 22nd Womens Centex 2025
26 Texas** Loss 1-13 1528.98 Ignored Mar 22nd Womens Centex 2025
93 Rice Loss 5-10 771.73 Mar 22nd Womens Centex 2025
195 Texas A&M Win 9-8 719.04 Mar 23rd Womens Centex 2025
238 Texas-B Win 9-6 500.15 Mar 23rd Womens Centex 2025
**Blowout Eligible

FAQ

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
  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
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)