#141 LSU (15-13)

avg: 1006.58  •  sd: 45.45  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
84 Alabama Loss 10-11 1147.18 Jan 28th T Town Throwdown1
253 Georgia Southern Win 13-5 1092.01 Jan 28th T Town Throwdown1
330 North Florida** Win 13-1 492.01 Ignored Jan 28th T Town Throwdown1
117 Georgia State Win 8-7 1235.57 Jan 28th T Town Throwdown1
36 Alabama-Huntsville Loss 6-13 976.17 Jan 29th T Town Throwdown1
87 Mississippi State Loss 5-13 660.75 Jan 29th T Town Throwdown1
225 Spring Hill Win 13-10 929.77 Jan 29th T Town Throwdown1
225 Spring Hill Win 13-8 1097.78 Feb 25th Mardi Gras XXXV
169 Sam Houston Win 11-7 1347.95 Feb 25th Mardi Gras XXXV
289 Houston** Win 13-4 859.54 Ignored Feb 25th Mardi Gras XXXV
56 Indiana Loss 9-13 989.95 Feb 25th Mardi Gras XXXV
87 Mississippi State Loss 7-8 1135.75 Feb 26th Mardi Gras XXXV
233 Jacksonville State Win 12-4 1182.54 Feb 26th Mardi Gras XXXV
169 Sam Houston Win 12-6 1460.37 Feb 26th Mardi Gras XXXV
253 Georgia Southern Win 13-4 1092.01 Mar 11th Tally Classic XVII
106 Florida State Loss 8-13 664.29 Mar 11th Tally Classic XVII
70 Notre Dame Loss 9-10 1230.69 Mar 11th Tally Classic XVII
61 Harvard Loss 5-8 934.85 Mar 11th Tally Classic XVII
94 Tulane Loss 11-14 905.5 Mar 12th Tally Classic XVII
106 Florida State Win 13-11 1389.29 Mar 12th Tally Classic XVII
113 Clemson Loss 6-8 819.9 Mar 12th Tally Classic XVII
84 Alabama Loss 5-13 672.18 Mar 25th Magic City Invite 2023
242 Alabama-B Win 11-4 1131.97 Mar 25th Magic City Invite 2023
87 Mississippi State Loss 4-13 660.75 Mar 25th Magic City Invite 2023
242 Alabama-B Win 9-5 1061.02 Mar 26th Magic City Invite 2023
205 Alabama-Birmingham Win 13-10 1041.48 Mar 26th Magic City Invite 2023
87 Mississippi State Loss 6-13 660.75 Mar 26th Magic City Invite 2023
227 Samford Win 13-10 926.05 Mar 26th Magic City Invite 2023
**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)