#246 Florida-B (12-10)

avg: 875.42  •  sd: 68.16  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
295 Embry-Riddle (Florida) Win 13-5 1295.98 Feb 8th Florida Warm Up 2019
415 Florida Tech-B** Win 13-5 728.75 Ignored Feb 8th Florida Warm Up 2019
255 Boston College-B Win 13-6 1432.55 Feb 8th Florida Warm Up 2019
227 Florida State-B Loss 7-11 448.44 Feb 9th Florida Warm Up 2019
366 Central Florida-B Win 13-7 957.64 Feb 9th Florida Warm Up 2019
355 Northwestern-B Win 11-5 1058.9 Feb 9th Florida Warm Up 2019
227 Florida State-B Win 15-7 1515.33 Feb 10th Florida Warm Up 2019
207 North Florida Loss 8-11 599.9 Feb 10th Florida Warm Up 2019
295 Embry-Riddle (Florida) Win 13-6 1295.98 Mar 16th Tally Classic XIV
415 Florida Tech-B** Win 13-1 728.75 Ignored Mar 16th Tally Classic XIV
366 Central Florida-B Win 13-8 896.26 Mar 16th Tally Classic XIV
263 Georgia Tech-B Loss 11-13 585.1 Mar 16th Tally Classic XIV
377 Stetson Win 14-7 936.27 Mar 17th Tally Classic XIV
227 Florida State-B Loss 11-14 602 Mar 17th Tally Classic XIV
221 North Georgia Loss 12-15 621.28 Mar 17th Tally Classic XIV
35 Middlebury** Loss 3-13 1126.5 Ignored Mar 23rd College Southerns XVIII
234 Florida Tech Loss 9-11 657.05 Mar 23rd College Southerns XVIII
78 Carleton College-GoP Loss 6-13 857.72 Mar 23rd College Southerns XVIII
240 Wisconsin-Eau Claire Loss 10-11 764.84 Mar 23rd College Southerns XVIII
257 Charleston Loss 8-15 265.53 Mar 24th College Southerns XVIII
321 Carleton Hot Karls Win 9-6 1008.06 Mar 24th College Southerns XVIII
207 North Florida Win 15-13 1179.69 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)