#55 Florida State (15-14)

avg: 1611.67  •  sd: 55.41  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
94 Appalachian State Win 13-9 1791 Jan 26th Carolina Kickoff 2019
73 Temple Win 11-9 1730.08 Jan 26th Carolina Kickoff 2019
11 North Carolina State Loss 2-13 1427.57 Jan 26th Carolina Kickoff 2019
1 North Carolina Loss 9-15 1716.44 Jan 27th Carolina Kickoff 2019
78 Carleton College-GoP Win 15-12 1758.21 Jan 27th Carolina Kickoff 2019
52 Notre Dame Win 15-11 2007.83 Jan 27th Carolina Kickoff 2019
17 Minnesota Loss 10-13 1622.91 Feb 8th Florida Warm Up 2019
54 Virginia Tech Win 13-9 2038.01 Feb 8th Florida Warm Up 2019
68 Cincinnati Win 13-8 2011.53 Feb 8th Florida Warm Up 2019
25 South Carolina Loss 4-13 1186.69 Feb 9th Florida Warm Up 2019
69 Emory Loss 8-14 972.42 Feb 9th Florida Warm Up 2019
127 Boston College Win 13-7 1832.25 Feb 9th Florida Warm Up 2019
31 Texas A&M Loss 7-13 1190.88 Feb 9th Florida Warm Up 2019
49 Northwestern Loss 6-13 1037.69 Feb 10th Florida Warm Up 2019
106 Illinois State Loss 11-12 1202.34 Feb 10th Florida Warm Up 2019
4 Pittsburgh Loss 6-15 1584.92 Mar 9th Classic City Invite 2019
48 Kennesaw State Win 13-11 1875.33 Mar 9th Classic City Invite 2019
9 Massachusetts Loss 11-12 1940.5 Mar 9th Classic City Invite 2019
20 Tufts Loss 8-13 1367.99 Mar 9th Classic City Invite 2019
57 Carnegie Mellon Win 9-8 1712.38 Mar 10th Classic City Invite 2019
28 Northeastern Loss 9-12 1430.47 Mar 10th Classic City Invite 2019
143 Minnesota-Duluth Win 13-9 1617.63 Mar 16th Tally Classic XIV
88 Tennessee-Chattanooga Win 13-11 1648.03 Mar 16th Tally Classic XIV
24 Auburn Loss 10-13 1468.63 Mar 16th Tally Classic XIV
79 Tulane Win 15-4 2056.42 Mar 16th Tally Classic XIV
72 Alabama-Huntsville Win 15-8 2048.8 Mar 17th Tally Classic XIV
15 Central Florida Loss 10-15 1536.71 Mar 17th Tally Classic XIV
164 Arizona State Win 7-5 1431.37 Mar 30th Huck Finn XXIII
131 Chicago Win 8-7 1391.49 Mar 30th Huck Finn XXIII
**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)