#31 LSU (10-10)

avg: 1699.56  •  sd: 49.85  •  top 16/20: 1.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
6 Brown Loss 7-13 1489.18 Feb 16th Warm Up A Florida Affair 2018
10 Virginia Tech Loss 8-11 1557.69 Feb 16th Warm Up A Florida Affair 2018
81 Florida State Win 10-6 1904.88 Feb 16th Warm Up A Florida Affair 2018
36 Michigan Win 13-10 1966.45 Feb 16th Warm Up A Florida Affair 2018
52 Harvard Win 15-12 1836.51 Feb 17th Warm Up A Florida Affair 2018
160 Oklahoma Win 12-8 1533.75 Feb 17th Warm Up A Florida Affair 2018
41 Northeastern Win 13-9 2021.93 Feb 17th Warm Up A Florida Affair 2018
10 Virginia Tech Loss 6-15 1323.3 Feb 17th Warm Up A Florida Affair 2018
14 Florida Loss 13-15 1672.64 Feb 18th Warm Up A Florida Affair 2018
13 Wisconsin Loss 9-11 1667.91 Feb 18th Warm Up A Florida Affair 2018
40 Iowa Loss 9-10 1499.81 Mar 10th Mens Centex 2018
39 Northwestern Win 11-8 1994.31 Mar 10th Mens Centex 2018
112 Texas Tech Win 12-6 1864.39 Mar 10th Mens Centex 2018
27 Texas State Win 9-8 1846.16 Mar 10th Mens Centex 2018
17 Colorado State Win 11-10 1994.76 Mar 11th Mens Centex 2018
14 Florida Loss 10-15 1433.21 Mar 11th Mens Centex 2018
12 North Carolina State Loss 13-15 1704.68 Mar 31st Easterns 2018
4 Minnesota Loss 8-15 1505.11 Mar 31st Easterns 2018
8 Massachusetts Loss 6-15 1363.77 Mar 31st Easterns 2018
42 Connecticut Win 15-12 1896.05 Mar 31st Easterns 2018
**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)