#383 Florida State-B (4-14)

avg: 278.94  •  sd: 53.78  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
73 Ave Maria** Loss 0-13 1014.71 Ignored Feb 24th Florida Warm Up 2024 Weekend 2
412 Central Florida-B Win 13-5 308.31 Feb 24th Florida Warm Up 2024 Weekend 2
206 Embry-Riddle** Loss 5-13 498.9 Ignored Feb 24th Florida Warm Up 2024 Weekend 2
309 Florida Gulf Coast Loss 5-13 81.37 Feb 24th Florida Warm Up 2024 Weekend 2
248 Florida-B Loss 6-13 363.86 Feb 25th Florida Warm Up 2024 Weekend 2
287 Florida Tech Loss 6-13 187.8 Feb 25th Florida Warm Up 2024 Weekend 2
287 Florida Tech Loss 2-13 187.8 Mar 16th Tally Classic XVIII
384 Notre Dame-B Loss 9-10 133.56 Mar 16th Tally Classic XVIII
406 South Florida-B Win 10-4 549.16 Mar 16th Tally Classic XVIII
309 Florida Gulf Coast Loss 8-11 315.76 Mar 17th Tally Classic XVIII
309 Florida Gulf Coast Loss 10-12 443.25 Mar 17th Tally Classic XVIII
406 South Florida-B Win 13-4 549.16 Mar 17th Tally Classic XVIII
303 Alabama-B Loss 7-15 111.11 Apr 13th Southeast Dev Mens Conferences 2024
388 Ave Maria-B Loss 13-14 101.28 Apr 13th Southeast Dev Mens Conferences 2024
261 Georgia Tech-B Loss 7-15 304.28 Apr 13th Southeast Dev Mens Conferences 2024
303 Alabama-B Loss 10-14 312.41 Apr 14th Southeast Dev Mens Conferences 2024
412 Central Florida-B Win 15-6 308.31 Apr 14th Southeast Dev Mens Conferences 2024
224 Georgia-B** Loss 4-15 439.36 Ignored Apr 14th Southeast Dev Mens Conferences 2024
**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)