#227 Florida State-B (15-8)

avg: 915.33  •  sd: 61.59  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
377 Stetson Win 13-7 910.91 Feb 8th Florida Warm Up 2019
294 Florida Gulf Coast Loss 9-11 448.33 Feb 8th Florida Warm Up 2019
207 North Florida Loss 10-13 637.37 Feb 8th Florida Warm Up 2019
418 South Florida-B** Win 13-4 700.43 Ignored Feb 9th Florida Warm Up 2019
300 High Point Win 12-6 1256.45 Feb 9th Florida Warm Up 2019
246 Florida-B Win 11-7 1342.31 Feb 9th Florida Warm Up 2019
294 Florida Gulf Coast Win 8-3 1297.54 Feb 10th Florida Warm Up 2019
246 Florida-B Loss 7-15 275.42 Feb 10th Florida Warm Up 2019
350 Sam Houston State Win 11-7 949.37 Mar 2nd Mardi Gras XXXII
161 Sul Ross State Loss 7-11 651.01 Mar 2nd Mardi Gras XXXII
103 Georgia State Loss 5-11 748.38 Mar 2nd Mardi Gras XXXII
277 Texas-San Antonio Win 9-8 894.49 Mar 2nd Mardi Gras XXXII
27 LSU Loss 6-13 1177.74 Mar 2nd Mardi Gras XXXII
207 North Florida Loss 7-11 498.62 Mar 3rd Mardi Gras XXXII
161 Sul Ross State Loss 9-13 699.34 Mar 3rd Mardi Gras XXXII
418 South Florida-B Win 11-5 700.43 Mar 16th Tally Classic XIV
377 Stetson Win 11-1 953.38 Mar 16th Tally Classic XIV
221 North Georgia Win 11-9 1170.98 Mar 16th Tally Classic XIV
369 Notre Dame-B Win 11-2 981.85 Mar 16th Tally Classic XIV
429 Columbus State** Win 11-2 571.58 Ignored Mar 16th Tally Classic XIV
366 Central Florida-B Win 15-6 1000.1 Mar 17th Tally Classic XIV
246 Florida-B Win 14-11 1188.76 Mar 17th Tally Classic XIV
263 Georgia Tech-B Win 12-7 1334.46 Mar 17th Tally Classic XIV
**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)