#50 Alabama (11-9)

avg: 1501.57  •  sd: 71.35  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
137 Union (Tennessee) Win 11-7 1557.4 Feb 10th Golden Triangle Invitational
139 LSU Win 9-7 1363.94 Feb 10th Golden Triangle Invitational
222 Mississippi State -B** Win 13-2 1336.33 Ignored Feb 10th Golden Triangle Invitational
264 Jacksonville State** Win 15-2 1141.75 Ignored Feb 10th Golden Triangle Invitational
87 Tennessee-Chattanooga Loss 12-13 1184.98 Feb 11th Golden Triangle Invitational
52 Virginia Tech Loss 10-13 1147.38 Feb 24th Easterns Qualifier 2024
38 Duke Loss 10-13 1262.61 Feb 24th Easterns Qualifier 2024
16 Penn State Loss 6-13 1321.23 Feb 24th Easterns Qualifier 2024
58 Maryland Win 13-5 2042.96 Feb 24th Easterns Qualifier 2024
76 Purdue Loss 10-11 1232.22 Feb 25th Easterns Qualifier 2024
60 Temple Win 14-9 1908.89 Feb 25th Easterns Qualifier 2024
74 Cincinnati Win 15-6 1961.2 Feb 25th Easterns Qualifier 2024
27 Georgia Tech Loss 9-15 1224.66 Feb 25th Easterns Qualifier 2024
49 St Olaf Loss 9-13 1084.6 Mar 30th Huck Finn 2024
19 Washington University Loss 7-12 1344.66 Mar 30th Huck Finn 2024
66 Virginia Win 10-7 1783.83 Mar 30th Huck Finn 2024
76 Purdue Win 12-8 1798.37 Mar 30th Huck Finn 2024
83 Northwestern Loss 10-12 1097.36 Mar 31st Huck Finn 2024
91 Indiana Win 13-3 1870.81 Mar 31st Huck Finn 2024
66 Virginia Win 12-9 1739.53 Mar 31st Huck Finn 2024
**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)