#207 North Florida (13-9)

avg: 965.51  •  sd: 54.85  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
227 Florida State-B Win 13-10 1243.48 Feb 8th Florida Warm Up 2019
415 Florida Tech-B** Win 13-5 728.75 Ignored Feb 8th Florida Warm Up 2019
355 Northwestern-B Win 13-0 1058.9 Feb 8th Florida Warm Up 2019
295 Embry-Riddle (Florida) Win 13-6 1295.98 Feb 9th Florida Warm Up 2019
366 Central Florida-B Win 13-3 1000.1 Feb 9th Florida Warm Up 2019
255 Boston College-B Win 12-8 1273.71 Feb 9th Florida Warm Up 2019
294 Florida Gulf Coast Win 15-9 1213.02 Feb 10th Florida Warm Up 2019
246 Florida-B Win 11-8 1241.03 Feb 10th Florida Warm Up 2019
322 Mississippi Win 11-7 1054.11 Mar 2nd Mardi Gras XXXII
103 Georgia State Loss 9-12 1003.01 Mar 2nd Mardi Gras XXXII
363 Texas State -B Win 11-4 1015.34 Mar 2nd Mardi Gras XXXII
212 Texas Christian Win 11-7 1417.52 Mar 2nd Mardi Gras XXXII
209 Trinity Loss 7-11 491 Mar 2nd Mardi Gras XXXII
227 Florida State-B Win 11-7 1382.23 Mar 3rd Mardi Gras XXXII
112 Wisconsin-Whitewater Loss 9-13 887.65 Mar 3rd Mardi Gras XXXII
69 Emory Loss 3-13 908.46 Mar 23rd College Southerns XVIII
259 Florida Atlantic Loss 10-13 501.9 Mar 23rd College Southerns XVIII
131 Chicago Loss 9-13 847.93 Mar 23rd College Southerns XVIII
89 Luther Loss 7-12 876.04 Mar 23rd College Southerns XVIII
257 Charleston Loss 13-15 616.16 Mar 24th College Southerns XVIII
321 Carleton Hot Karls Win 11-9 838.7 Mar 24th College Southerns XVIII
246 Florida-B Loss 13-15 661.24 Mar 24th College Southerns XVIII
**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)