#104 Florida State (12-17)

avg: 1345.01  •  sd: 54.11  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
12 Minnesota** Loss 4-13 1470.91 Ignored Feb 3rd Florida Warm Up 2023
79 Texas A&M Win 10-8 1736.35 Feb 3rd Florida Warm Up 2023
21 Northeastern Loss 3-13 1307.17 Feb 3rd Florida Warm Up 2023
2 Brigham Young** Loss 4-13 1718.3 Ignored Feb 4th Florida Warm Up 2023
72 Auburn Loss 8-13 1001.64 Feb 4th Florida Warm Up 2023
112 Illinois Win 8-7 1440.98 Feb 4th Florida Warm Up 2023
11 Brown** Loss 6-15 1474.72 Ignored Feb 5th Florida Warm Up 2023
77 Temple Win 10-9 1605.32 Feb 5th Florida Warm Up 2023
69 Maryland Loss 11-12 1414.96 Feb 25th Easterns Qualifier 2023
33 Duke Loss 9-12 1445.3 Feb 25th Easterns Qualifier 2023
49 Notre Dame Loss 10-13 1315.11 Feb 25th Easterns Qualifier 2023
25 North Carolina-Wilmington Loss 7-13 1326.73 Feb 25th Easterns Qualifier 2023
59 Cincinnati Loss 8-15 1013.9 Feb 26th Easterns Qualifier 2023
77 Temple Loss 7-13 922.79 Feb 26th Easterns Qualifier 2023
150 George Washington Win 14-13 1273.25 Feb 26th Easterns Qualifier 2023
88 Central Florida Win 13-8 1930.38 Mar 11th Tally Classic XVII
141 LSU Win 13-8 1680.02 Mar 11th Tally Classic XVII
49 Notre Dame Loss 6-10 1147.1 Mar 11th Tally Classic XVII
268 Georgia Southern** Win 13-5 1251.41 Ignored Mar 11th Tally Classic XVII
148 Minnesota-Duluth Win 14-8 1697.05 Mar 12th Tally Classic XVII
141 LSU Loss 11-13 955.02 Mar 12th Tally Classic XVII
62 Harvard Loss 12-13 1443.9 Mar 12th Tally Classic XVII
90 Chicago Win 5-4 1558.78 Apr 1st Huck Finn1
116 John Brown Loss 3-6 759.28 Apr 1st Huck Finn1
75 Grinnell Loss 3-5 1068.2 Apr 1st Huck Finn1
68 Wisconsin-Milwaukee Win 7-5 1878.07 Apr 1st Huck Finn1
163 Boston University Win 6-5 1226.13 Apr 2nd Huck Finn1
92 Missouri S&T Loss 8-11 1064.21 Apr 2nd Huck Finn1
131 Georgia State Win 8-7 1367.58 Apr 2nd Huck Finn1
**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)