#309 Florida Gulf Coast (9-7)

avg: 681.37  •  sd: 70.59  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
206 Embry-Riddle Loss 6-10 602.74 Feb 24th Florida Warm Up 2024 Weekend 2
372 North Florida Win 13-5 955.74 Feb 24th Florida Warm Up 2024 Weekend 2
383 Florida State-B Win 13-5 878.94 Feb 24th Florida Warm Up 2024 Weekend 2
406 South Florida-B** Win 13-4 549.16 Ignored Feb 24th Florida Warm Up 2024 Weekend 2
73 Ave Maria** Loss 1-13 1014.71 Ignored Feb 25th Florida Warm Up 2024 Weekend 2
330 Florida Polytechnic Win 13-8 1093.7 Feb 25th Florida Warm Up 2024 Weekend 2
406 South Florida-B** Win 13-0 549.16 Ignored Mar 16th Tally Classic XVIII
287 Florida Tech Loss 10-13 459.66 Mar 16th Tally Classic XVIII
383 Florida State-B Win 11-8 644.55 Mar 17th Tally Classic XVIII
384 Notre Dame-B Win 11-5 858.56 Mar 17th Tally Classic XVIII
384 Notre Dame-B Loss 9-12 -86.8 Mar 17th Tally Classic XVIII
383 Florida State-B Win 12-10 517.06 Mar 17th Tally Classic XVIII
89 Florida State** Loss 6-15 951.28 Ignored Apr 13th Florida D I Mens Conferences 2024
372 North Florida Win 14-7 938.63 Apr 13th Florida D I Mens Conferences 2024
183 South Florida Loss 9-15 671.04 Apr 13th Florida D I Mens Conferences 2024
186 Miami (Florida) Loss 7-11 709.47 Apr 14th Florida D I 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)