#179 LSU (5-13)

avg: 347.6  •  sd: 62.88  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
136 Alabama Loss 7-8 597.36 Feb 11th 2023 TOTS The Only Tenn I See
54 Georgia Tech** Loss 0-11 752.86 Ignored Feb 11th 2023 TOTS The Only Tenn I See
206 Vanderbilt Win 7-5 349.86 Feb 11th 2023 TOTS The Only Tenn I See
56 Tennessee** Loss 1-11 740.42 Ignored Feb 11th 2023 TOTS The Only Tenn I See
195 Georgia Tech-B Win 6-3 703.42 Feb 12th 2023 TOTS The Only Tenn I See
206 Vanderbilt Win 8-3 621.72 Feb 12th 2023 TOTS The Only Tenn I See
136 Alabama Loss 6-10 226.2 Feb 25th Mardi Gras XXXV
167 Jacksonville State Loss 4-9 -150.67 Feb 25th Mardi Gras XXXV
109 Texas State Loss 2-9 346.78 Feb 25th Mardi Gras XXXV
147 Sam Houston Loss 6-8 322.68 Feb 26th Mardi Gras XXXV
203 Miami (Florida) Win 12-2 645.72 Feb 26th Mardi Gras XXXV
171 Illinois Loss 7-10 19.32 Mar 18th Womens Centex1
104 Iowa** Loss 4-13 366.86 Ignored Mar 18th Womens Centex1
190 Colorado-B Loss 8-9 104.18 Mar 18th Womens Centex1
112 Rice Loss 6-13 307.62 Mar 18th Womens Centex1
171 Illinois Loss 7-9 129.65 Mar 19th Womens Centex1
109 Texas State Loss 10-13 618.64 Mar 19th Womens Centex1
198 North Texas Win 10-6 584.17 Mar 19th Womens Centex1
**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)