#102 LSU (16-8)

avg: 1119.43  •  sd: 75.55  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
144 Tennessee Loss 6-8 595.56 Jan 26th Clutch Classic 2019
139 Tennessee-Chattanooga Win 9-4 1531.07 Jan 26th Clutch Classic 2019
261 Emory-B** Win 8-3 657.5 Ignored Jan 26th Clutch Classic 2019
18 South Carolina** Loss 0-13 1371.42 Ignored Jan 26th Clutch Classic 2019
139 Tennessee-Chattanooga Loss 7-9 651.74 Jan 27th Clutch Classic 2019
189 Tulane Loss 5-7 265.84 Jan 27th Clutch Classic 2019
113 Oklahoma Win 9-7 1331.98 Feb 16th Big D in lil d Women
195 Texas A&M Win 11-1 1168.44 Feb 16th Big D in lil d Women
120 Arizona State Win 8-7 1152.17 Feb 16th Big D in lil d Women
113 Oklahoma Win 8-5 1506.25 Feb 17th Big D in lil d Women
110 Dallas Win 6-5 1186.85 Feb 17th Big D in lil d Women
72 Texas-Dallas Loss 8-12 885.25 Feb 17th Big D in lil d Women
201 Indiana Win 14-4 1138.64 Mar 2nd Mardi Gras XXXII
214 Mississippi Win 11-7 912.53 Mar 2nd Mardi Gras XXXII
112 Central Florida Win 12-11 1179.26 Mar 2nd Mardi Gras XXXII
143 Alabama Win 13-6 1497.51 Mar 3rd Mardi Gras XXXII
195 Texas A&M Win 6-5 693.44 Mar 23rd Womens College Centex 2019
217 Minnesota-B** Win 12-4 1027.03 Ignored Mar 23rd Womens College Centex 2019
99 MIT Loss 6-13 527.48 Mar 23rd Womens College Centex 2019
168 Rice Win 10-4 1366.54 Mar 23rd Womens College Centex 2019
90 Colorado State Loss 12-13 1092.13 Mar 24th Womens College Centex 2019
101 Trinity Win 15-4 1725.74 Mar 24th Womens College Centex 2019
80 St Olaf Loss 9-10 1146.04 Mar 24th Womens College Centex 2019
108 Southern California Win 7-4 1568.61 Mar 24th Womens College Centex 2019
**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)