#69 Emory (10-12)

avg: 1508.46  •  sd: 66.92  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
25 South Carolina Win 10-8 2049.36 Jan 26th Carolina Kickoff 2019
119 Clemson Win 12-6 1862.86 Jan 26th Carolina Kickoff 2019
52 Notre Dame Loss 9-13 1208.1 Jan 26th Carolina Kickoff 2019
81 Georgia Tech Win 13-10 1775.46 Jan 27th Carolina Kickoff 2019
11 North Carolina State Loss 9-15 1512.09 Jan 27th Carolina Kickoff 2019
26 North Carolina-Wilmington Loss 9-15 1265.49 Jan 27th Carolina Kickoff 2019
80 Oklahoma Win 12-8 1893.12 Feb 8th Florida Warm Up 2019
7 Carleton College-CUT Loss 8-13 1622.48 Feb 8th Florida Warm Up 2019
20 Tufts Loss 10-11 1739.15 Feb 8th Florida Warm Up 2019
18 Michigan Loss 6-13 1308.77 Feb 9th Florida Warm Up 2019
2 Brown** Loss 5-13 1629.16 Ignored Feb 9th Florida Warm Up 2019
127 Boston College Win 10-8 1537.39 Feb 9th Florida Warm Up 2019
55 Florida State Win 14-8 2147.71 Feb 9th Florida Warm Up 2019
80 Oklahoma Win 13-11 1680.81 Feb 10th Florida Warm Up 2019
13 Wisconsin Loss 5-15 1400.97 Feb 10th Florida Warm Up 2019
259 Florida Atlantic Win 13-6 1430.05 Mar 23rd College Southerns XVIII
89 Luther Loss 10-12 1158.43 Mar 23rd College Southerns XVIII
207 North Florida Win 13-3 1565.51 Mar 23rd College Southerns XVIII
131 Chicago Win 13-8 1762.65 Mar 23rd College Southerns XVIII
25 South Carolina Loss 8-15 1221.88 Mar 24th College Southerns XVIII
56 California-San Diego Loss 10-12 1354.64 Mar 24th College Southerns XVIII
89 Luther Loss 4-13 796.55 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)