#234 Florida Tech (8-11)

avg: 906.26  •  sd: 78.7  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
285 Troy Loss 6-10 233.68 Feb 2nd Royal Crown Classic 2019
208 Berry Win 11-5 1558.78 Feb 2nd Royal Crown Classic 2019
429 Columbus State Win 14-8 507.62 Feb 2nd Royal Crown Classic 2019
165 Georgia Southern Loss 11-13 863.07 Feb 3rd Royal Crown Classic 2019
285 Troy Win 15-14 854.84 Feb 3rd Royal Crown Classic 2019
75 Air Force Loss 8-13 981.38 Mar 2nd FCS D III Tune Up 2019
113 Davidson Loss 11-12 1176.9 Mar 2nd FCS D III Tune Up 2019
208 Berry Loss 7-12 438.27 Mar 2nd FCS D III Tune Up 2019
91 Mary Washington Loss 12-14 1161.55 Mar 2nd FCS D III Tune Up 2019
155 Elon Loss 8-13 653.42 Mar 3rd FCS D III Tune Up 2019
300 High Point Loss 10-11 552.14 Mar 3rd FCS D III Tune Up 2019
310 Campbell Win 13-8 1124.34 Mar 3rd FCS D III Tune Up 2019
35 Middlebury** Loss 5-13 1126.5 Ignored Mar 23rd College Southerns XVIII
240 Wisconsin-Eau Claire Win 13-7 1447.37 Mar 23rd College Southerns XVIII
78 Carleton College-GoP Loss 4-13 857.72 Mar 23rd College Southerns XVIII
246 Florida-B Win 11-9 1124.63 Mar 23rd College Southerns XVIII
173 Georgia College Loss 8-15 504.3 Mar 24th College Southerns XVIII
256 Georgia-B Win 12-11 956.15 Mar 24th College Southerns XVIII
240 Wisconsin-Eau Claire Win 15-13 1104.02 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)