#85 Alabama (14-12)

avg: 1446.99  •  sd: 57.54  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
368 North Florida** Win 13-0 600 Ignored Jan 28th T Town Throwdown1
268 Georgia Southern** Win 13-3 1251.41 Ignored Jan 28th T Town Throwdown1
141 LSU Win 11-10 1308.87 Jan 28th T Town Throwdown1
238 Spring Hill Win 13-8 1259.43 Jan 29th T Town Throwdown1
88 Central Florida Win 13-11 1663.06 Jan 29th T Town Throwdown1
61 Emory Loss 10-11 1451.98 Jan 29th T Town Throwdown1
45 Georgetown Win 13-12 1821.69 Feb 25th Easterns Qualifier 2023
24 North Carolina-Charlotte Loss 9-13 1475.91 Feb 25th Easterns Qualifier 2023
59 Cincinnati Win 13-12 1703.71 Feb 25th Easterns Qualifier 2023
41 William & Mary Loss 6-13 1118.88 Feb 25th Easterns Qualifier 2023
49 Notre Dame Loss 11-15 1262.09 Feb 26th Easterns Qualifier 2023
37 McGill Win 14-13 1898.37 Feb 26th Easterns Qualifier 2023
69 Maryland Loss 12-13 1414.96 Feb 26th Easterns Qualifier 2023
242 Samford** Win 13-2 1356.32 Ignored Mar 25th Magic City Invite 2023
259 Jacksonville State** Win 13-5 1296.22 Ignored Mar 25th Magic City Invite 2023
141 LSU Win 13-5 1783.87 Mar 25th Magic City Invite 2023
251 Alabama-B Win 13-6 1332.69 Mar 26th Magic City Invite 2023
89 Mississippi State Loss 7-11 967.16 Mar 26th Magic City Invite 2023
65 Indiana Loss 8-9 1440.84 Apr 1st Huck Finn1
48 Iowa State Win 7-6 1771.34 Apr 1st Huck Finn1
35 Missouri Loss 5-9 1257.77 Apr 1st Huck Finn1
38 Purdue Loss 5-7 1444.96 Apr 1st Huck Finn1
68 Wisconsin-Milwaukee Win 11-10 1674.93 Apr 2nd Huck Finn1
22 Washington University Loss 8-12 1464.17 Apr 2nd Huck Finn1
59 Cincinnati Loss 8-10 1316.04 Apr 2nd Huck Finn1
64 St. Olaf Loss 3-7 968 Apr 2nd Huck Finn1
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)