#300 High Point (11-9)

avg: 677.14  •  sd: 70.23  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
366 Central Florida-B Loss 8-10 137.44 Feb 8th Florida Warm Up 2019
255 Boston College-B Win 10-9 957.55 Feb 8th Florida Warm Up 2019
355 Northwestern-B Win 9-7 738.24 Feb 8th Florida Warm Up 2019
295 Embry-Riddle (Florida) Win 11-9 945.19 Feb 9th Florida Warm Up 2019
227 Florida State-B Loss 6-12 336.02 Feb 9th Florida Warm Up 2019
415 Florida Tech-B Win 13-7 686.28 Feb 9th Florida Warm Up 2019
295 Embry-Riddle (Florida) Loss 10-12 457.86 Feb 10th Florida Warm Up 2019
366 Central Florida-B Win 14-8 936.14 Feb 10th Florida Warm Up 2019
292 Navy Loss 7-13 145.37 Mar 2nd FCS D III Tune Up 2019
208 Berry Win 13-12 1083.78 Mar 2nd FCS D III Tune Up 2019
247 Xavier Win 13-10 1202.89 Mar 2nd FCS D III Tune Up 2019
253 Anderson Loss 11-13 614.25 Mar 3rd FCS D III Tune Up 2019
234 Florida Tech Win 11-10 1031.26 Mar 3rd FCS D III Tune Up 2019
183 Oberlin Loss 7-13 484.43 Mar 3rd FCS D III Tune Up 2019
334 James Madison-B Loss 9-10 422.25 Mar 30th I 85 Rodeo 2019
260 South Carolina-B Loss 6-10 331.33 Mar 30th I 85 Rodeo 2019
365 Virginia-B Win 11-10 535.94 Mar 30th I 85 Rodeo 2019
260 South Carolina-B Loss 11-12 702.49 Mar 31st I 85 Rodeo 2019
279 Maryland-B Win 11-8 1123.7 Mar 31st I 85 Rodeo 2019
338 Wake Forest Win 12-11 658.6 Mar 31st I 85 Rodeo 2019
**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)