#41 Alabama (12-5)

avg: 1600  •  sd: 67.89  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
114 Jacksonville State Win 13-6 1757.08 Jan 18th TTown Throwdown 2020 Open
25 Georgia Tech Loss 6-11 1230.06 Jan 18th TTown Throwdown 2020 Open
172 South Florida Win 9-8 1067.95 Jan 18th TTown Throwdown 2020 Open
164 Illinois State Win 15-8 1538.51 Jan 18th TTown Throwdown 2020 Open
62 Florida Win 15-12 1719.35 Jan 19th TTown Throwdown 2020 Open
57 Illinois Loss 12-13 1327.91 Jan 19th TTown Throwdown 2020 Open
107 Ohio Win 13-7 1745.59 Feb 8th Queen City Tune Up 2020 Open
83 Penn State Win 12-10 1547.54 Feb 8th Queen City Tune Up 2020 Open
22 Georgia Loss 7-13 1259.42 Feb 8th Queen City Tune Up 2020 Open
23 William & Mary Loss 9-12 1442.86 Feb 9th Queen City Tune Up 2020 Open
111 Maryland Win 9-7 1449.31 Feb 29th Easterns Qualifier 2020
73 Carnegie Mellon Win 10-9 1491.46 Feb 29th Easterns Qualifier 2020
92 Duke Win 12-4 1862.44 Feb 29th Easterns Qualifier 2020
32 Dartmouth Loss 7-9 1404.1 Feb 29th Easterns Qualifier 2020
45 Notre Dame Win 15-10 2027.17 Mar 1st Easterns Qualifier 2020
24 Vermont Win 15-12 2083.51 Mar 1st Easterns Qualifier 2020
26 South Carolina Win 13-12 1869.93 Mar 1st Easterns Qualifier 2020
**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)