#370 LSU-B (2-10)

avg: 358.46  •  sd: 100.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
98 Georgia State** Loss 0-15 920.82 Ignored Jan 20th Starkville Qualifiers
231 Harding** Loss 4-15 415.31 Ignored Jan 20th Starkville Qualifiers
242 Mississippi State -B** Loss 0-15 386.06 Ignored Jan 20th Starkville Qualifiers
303 Alabama-B Loss 2-15 111.11 Jan 21st Starkville Qualifiers
250 Mississippi State-C Loss 1-15 350.27 Jan 21st Starkville Qualifiers
403 Southern Mississippi Win 11-4 636.96 Jan 21st Starkville Qualifiers
133 Arizona State** Loss 4-10 775.09 Ignored Feb 24th Mardi Gras XXXVI college
82 Mississippi State** Loss 0-13 982.9 Ignored Feb 24th Mardi Gras XXXVI college
203 Spring Hill** Loss 1-13 513.28 Ignored Feb 24th Mardi Gras XXXVI college
99 Tennessee-Chattanooga** Loss 0-13 918.93 Ignored Feb 24th Mardi Gras XXXVI college
167 Texas-San Antonio** Loss 4-13 661.11 Ignored Feb 25th Mardi Gras XXXVI college
410 Tulane-B Win 12-6 358.45 Feb 25th Mardi Gras XXXVI college
**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)