#314 Texas Tech (1-10)

avg: 84.21  •  sd: 123.42  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
85 Tennessee-Chattanooga** Loss 0-13 665.17 Ignored Feb 25th Mardi Gras XXXV
233 Jacksonville State Loss 8-13 86.38 Feb 25th Mardi Gras XXXV
320 Trinity Loss 8-10 -219.33 Feb 25th Mardi Gras XXXV
37 Florida** Loss 1-13 941.54 Ignored Feb 25th Mardi Gras XXXV
324 Mississippi Win 13-8 437.51 Feb 26th Mardi Gras XXXV
292 Mississippi State-B Loss 7-13 -312.95 Feb 26th Mardi Gras XXXV
225 Spring Hill Loss 11-13 372.78 Feb 26th Mardi Gras XXXV
93 Arkansas** Loss 4-13 641.06 Ignored Mar 11th Centex Tier 2
213 Texas-Dallas Loss 6-13 72.6 Mar 11th Centex Tier 2
243 Texas-B Loss 8-12 88.74 Mar 11th Centex Tier 2
289 Houston Loss 12-13 134.54 Mar 12th Centex Tier 2
**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)