#75 Ave Maria (16-3)

avg: 1359.58  •  sd: 101.16  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
366 Florida State-B** Win 13-0 523.4 Ignored Feb 24th Florida Warm Up 2024 Weekend 2
380 South Florida-B** Win 13-0 119.6 Ignored Feb 24th Florida Warm Up 2024 Weekend 2
177 Miami (Florida) Win 13-7 1474.08 Feb 24th Florida Warm Up 2024 Weekend 2
226 Embry-Riddle Win 13-7 1287.53 Feb 24th Florida Warm Up 2024 Weekend 2
311 Florida Gulf Coast** Win 13-1 889.7 Ignored Feb 25th Florida Warm Up 2024 Weekend 2
177 Miami (Florida) Win 13-7 1474.08 Feb 25th Florida Warm Up 2024 Weekend 2
200 Spring Hill Win 13-7 1385.77 Mar 16th Tally Classic XVIII
82 Central Florida Win 11-9 1586.48 Mar 16th Tally Classic XVIII
106 Notre Dame Loss 8-9 1085.32 Mar 16th Tally Classic XVIII
57 Auburn Win 14-11 1760.53 Mar 17th Tally Classic XVIII
185 South Florida Win 13-1 1466.3 Mar 17th Tally Classic XVIII
97 Florida State Win 12-11 1372.77 Mar 17th Tally Classic XVIII
82 Central Florida Loss 14-15 1212.27 Mar 17th Tally Classic XVIII
358 Texas-Arlington** Win 13-1 579.67 Ignored Mar 23rd Huckfest 2024
221 Baylor** Win 13-4 1339.74 Ignored Mar 23rd Huckfest 2024
261 Texas Tech** Win 13-2 1155.11 Ignored Mar 23rd Huckfest 2024
243 North Texas** Win 13-5 1254.35 Ignored Mar 23rd Huckfest 2024
218 Texas-Dallas** Win 15-2 1357.85 Ignored Mar 24th Huckfest 2024
89 Tarleton State Loss 10-14 888.4 Mar 24th Huckfest 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)