#86 Florida State (4-8)

avg: 1128.57  •  sd: 81.04  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
55 Michigan Loss 10-13 1026.85 Feb 2nd Florida Warm Up 2024
65 Virginia Tech Win 12-11 1383.91 Feb 2nd Florida Warm Up 2024
43 Wisconsin Loss 11-12 1319.39 Feb 2nd Florida Warm Up 2024
5 Massachusetts Loss 7-13 1461.38 Feb 3rd Florida Warm Up 2024
31 Georgia Tech Loss 2-13 966.94 Feb 3rd Florida Warm Up 2024
150 South Florida Win 15-7 1323.28 Feb 3rd Florida Warm Up 2024
36 Tulane Loss 7-8 1402.43 Feb 24th Mardi Gras XXXVI college
97 Arkansas Loss 8-9 934.62 Feb 24th Mardi Gras XXXVI college
135 Texas-San Antonio Win 10-8 1104.14 Feb 24th Mardi Gras XXXVI college
74 Indiana Loss 11-13 983.13 Feb 25th Mardi Gras XXXVI college
99 LSU Loss 5-11 432.01 Feb 25th Mardi Gras XXXVI college
139 Spring Hill Win 7-4 1327.3 Feb 25th Mardi Gras XXXVI college
**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)