#81 Florida State (8-12)

avg: 1408.72  •  sd: 73.21  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
31 LSU Loss 6-10 1203.4 Feb 16th Warm Up A Florida Affair 2018
160 Oklahoma Loss 10-12 854.48 Feb 16th Warm Up A Florida Affair 2018
41 Northeastern Loss 3-13 1003.36 Feb 16th Warm Up A Florida Affair 2018
10 Virginia Tech Loss 10-13 1595.16 Feb 16th Warm Up A Florida Affair 2018
29 Texas Loss 8-11 1345.49 Feb 17th Warm Up A Florida Affair 2018
36 Michigan Loss 9-10 1513.31 Feb 17th Warm Up A Florida Affair 2018
45 Illinois State Win 15-13 1800.33 Feb 18th Warm Up A Florida Affair 2018
36 Michigan Loss 7-11 1171.41 Feb 18th Warm Up A Florida Affair 2018
42 Connecticut Win 13-12 1720.56 Feb 18th Warm Up A Florida Affair 2018
120 Mississippi State Win 15-9 1776.77 Mar 10th Tally Classic XIII
231 Alabama-Birmingham Win 13-4 1421.08 Mar 10th Tally Classic XIII
50 Notre Dame Win 13-11 1768.12 Mar 10th Tally Classic XIII
46 South Carolina Loss 10-13 1251.22 Mar 10th Tally Classic XIII
23 Georgia Tech Loss 12-13 1618.96 Mar 10th Tally Classic XIII
97 Alabama Win 14-11 1661.26 Mar 11th Tally Classic XIII
37 Central Florida Win 15-12 1935.25 Mar 11th Tally Classic XIII
49 Marquette Loss 6-15 948.36 Mar 31st Huck Finn 2018
91 Penn State Loss 13-14 1248.9 Mar 31st Huck Finn 2018
162 Saint Louis Win 15-9 1595.23 Mar 31st Huck Finn 2018
95 Purdue Loss 9-15 837.45 Apr 1st Huck Finn 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)