#253 Anderson (4-8)

avg: 843.09  •  sd: 83.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
257 Charleston Win 10-9 955.33 Feb 23rd Chucktown Throwdown XVI
94 Appalachian State Loss 1-13 772.43 Feb 23rd Chucktown Throwdown XVI
330 Wingate Win 10-8 823.74 Feb 23rd Chucktown Throwdown XVI
94 Appalachian State Loss 9-10 1247.43 Feb 24th Chucktown Throwdown XVI
256 Georgia-B Win 12-7 1351.66 Feb 24th Chucktown Throwdown XVI
138 Missouri S&T Loss 1-13 630.09 Mar 2nd FCS D III Tune Up 2019
182 Messiah Loss 5-13 442.84 Mar 2nd FCS D III Tune Up 2019
208 Berry Loss 9-11 709.57 Mar 2nd FCS D III Tune Up 2019
107 Franciscan Loss 6-10 829.36 Mar 2nd FCS D III Tune Up 2019
251 Samford Loss 8-13 355.16 Mar 3rd FCS D III Tune Up 2019
91 Mary Washington Loss 10-12 1144.39 Mar 3rd FCS D III Tune Up 2019
300 High Point Win 13-11 905.98 Mar 3rd FCS D III Tune Up 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)