#139 LSU (7-11)

avg: 1084.6  •  sd: 65.07  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
50 Alabama Loss 7-9 1222.23 Feb 10th Golden Triangle Invitational
119 Berry Loss 7-13 614.79 Feb 10th Golden Triangle Invitational
158 Kennesaw State Win 11-8 1376.33 Feb 10th Golden Triangle Invitational
105 Mississippi State Loss 11-12 1085.79 Feb 10th Golden Triangle Invitational
264 Jacksonville State Win 13-8 1037.91 Feb 11th Golden Triangle Invitational
41 Florida Loss 6-9 1152.46 Feb 24th Mardi Gras XXXVI college
91 Indiana Loss 8-11 905.2 Feb 24th Mardi Gras XXXVI college
230 Texas State Win 10-7 1104.18 Feb 24th Mardi Gras XXXVI college
261 Texas Tech Win 13-3 1155.11 Feb 24th Mardi Gras XXXVI college
132 Arkansas Loss 9-10 986.85 Feb 25th Mardi Gras XXXVI college
97 Florida State Win 11-5 1847.77 Feb 25th Mardi Gras XXXVI college
220 Sam Houston Win 11-5 1343.05 Feb 25th Mardi Gras XXXVI college
128 Colorado College Loss 9-13 717.43 Mar 16th College Mens Centex Tier 1
55 Michigan State Loss 9-12 1120.39 Mar 16th College Mens Centex Tier 1
48 Missouri Loss 5-11 914.77 Mar 16th College Mens Centex Tier 1
37 Texas A&M Loss 5-13 990.94 Mar 16th College Mens Centex Tier 1
67 Chicago Loss 3-13 787.02 Mar 17th College Mens Centex Tier 1
98 Dartmouth Win 9-8 1370.64 Mar 17th College Mens Centex Tier 1
**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)