#367 Florida Atlantic (2-6)

avg: 315.73  •  sd: 56.69  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
180 Pittsburgh-B** Loss 3-13 411.89 Ignored Feb 16th Warm Up A Florida Affair 2018
216 North Florida Loss 3-13 297.6 Feb 16th Warm Up A Florida Affair 2018
406 South Florida-B Win 13-7 515.05 Feb 16th Warm Up A Florida Affair 2018
230 Florida State-B Loss 3-13 228.17 Feb 16th Warm Up A Florida Affair 2018
207 Florida-B Loss 6-13 320.75 Feb 17th Warm Up A Florida Affair 2018
410 Florida Tech-B Win 13-4 525.76 Feb 17th Warm Up A Florida Affair 2018
180 Pittsburgh-B** Loss 6-15 411.89 Ignored Feb 18th Warm Up A Florida Affair 2018
292 Central Florida-B Loss 7-15 7.04 Feb 18th Warm Up A Florida Affair 2018
**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)