#251 Samford (5-7)

avg: 851.32  •  sd: 89.85  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
223 Rensselaer Polytech Win 13-11 1145.45 Mar 2nd FCS D III Tune Up 2019
85 Richmond Loss 5-13 829.7 Mar 2nd FCS D III Tune Up 2019
208 Berry Loss 11-12 833.78 Mar 2nd FCS D III Tune Up 2019
310 Campbell Win 9-7 907.52 Mar 2nd FCS D III Tune Up 2019
253 Anderson Win 13-8 1339.25 Mar 3rd FCS D III Tune Up 2019
182 Messiah Loss 10-12 804.71 Mar 3rd FCS D III Tune Up 2019
146 North Carolina-Asheville Loss 11-13 959.32 Mar 3rd FCS D III Tune Up 2019
221 North Georgia Loss 7-13 364.24 Mar 23rd Magic City Invite 2019
159 Mississippi State Loss 8-13 629.65 Mar 23rd Magic City Invite 2019
285 Troy Win 13-8 1226 Mar 23rd Magic City Invite 2019
322 Mississippi Loss 10-13 259.07 Mar 24th Magic City Invite 2019
285 Troy Win 13-10 1057.98 Mar 24th Magic City Invite 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)