#54 Florida State (13-4)

avg: 1856.06  •  sd: 106.81  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
134 Tennessee Win 15-2 1876.04 Jan 13th Florida Winter Classic 2018
207 Miami** Win 15-0 1402.34 Ignored Jan 13th Florida Winter Classic 2018
89 Iowa Win 10-3 2170.48 Jan 13th Florida Winter Classic 2018
13 Ohio State Loss 3-15 1804.92 Jan 14th Florida Winter Classic 2018
62 Central Florida Win 9-8 1923.88 Jan 14th Florida Winter Classic 2018
32 Florida Win 8-6 2380.73 Jan 14th Florida Winter Classic 2018
231 Tulane** Win 11-1 1239.41 Ignored Mar 10th Tally Classic XIII
40 Kennesaw State Loss 11-13 1788.75 Mar 10th Tally Classic XIII
237 Georgia Tech-B** Win 11-3 1174.53 Ignored Mar 10th Tally Classic XIII
19 Vermont Loss 9-10 2138.48 Mar 10th Tally Classic XIII
262 Notre Dame-B** Win 15-4 712.35 Ignored Mar 11th Tally Classic XIII
107 LSU Win 12-5 2066.19 Mar 11th Tally Classic XIII
180 South Florida Win 13-6 1600.57 Mar 11th Tally Classic XIII
108 Wisconsin-Eau Claire Win 9-4 2047.12 Mar 17th College Southerns 2018
180 South Florida Win 9-5 1529.63 Mar 17th College Southerns 2018
124 Carleton College-Eclipse Win 8-6 1653.02 Mar 18th College Southerns 2018
62 Central Florida Loss 4-6 1433.27 Mar 18th College Southerns 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)