#46 Florida State (12-7)

avg: 1473.72  •  sd: 134.44  •  top 16/20: 1.2%

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# Opponent Result Game Rating Status Date Event
216 Florida-B** Win 12-1 336.49 Ignored Jan 28th Florida Winter Classic 2023
10 Northeastern Loss 5-10 1560.44 Jan 28th Florida Winter Classic 2023
203 Miami (Florida)** Win 13-1 645.72 Ignored Jan 28th Florida Winter Classic 2023
210 Florida Tech** Win 13-0 512.58 Ignored Jan 28th Florida Winter Classic 2023
82 Central Florida Win 7-3 1730.12 Jan 29th Florida Winter Classic 2023
47 Florida Win 8-7 1592.9 Jan 29th Florida Winter Classic 2023
10 Northeastern** Loss 1-12 1534.33 Ignored Jan 29th Florida Winter Classic 2023
27 Minnesota Loss 8-9 1560.25 Feb 11th Queen City Tune Up1
26 Notre Dame Loss 8-9 1563.33 Feb 11th Queen City Tune Up1
1 North Carolina** Loss 3-13 2337.91 Ignored Feb 11th Queen City Tune Up1
62 William & Mary Loss 8-9 1152.48 Feb 11th Queen City Tune Up1
64 Appalachian State Win 8-7 1399.41 Feb 12th Queen City Tune Up1
202 North Carolina-Wilmington** Win 11-2 645.79 Ignored Feb 12th Queen City Tune Up1
38 Chicago Win 8-7 1692.22 Mar 11th Tally Classic XVII
214 Notre Dame-B** Win 13-0 395.29 Ignored Mar 11th Tally Classic XVII
195 Georgia Tech-B** Win 10-3 756.73 Ignored Mar 11th Tally Classic XVII
166 Tulane** Win 11-0 1049.82 Ignored Mar 11th Tally Classic XVII
38 Chicago Loss 10-11 1442.22 Mar 12th Tally Classic XVII
86 Clemson Win 11-10 1211.5 Mar 12th Tally Classic XVII
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)