#14 Texas (13-6)

avg: 1936.64  •  sd: 51.25  •  top 16/20: 98.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
82 Central Florida Win 13-3 1937.27 Feb 2nd Florida Warm Up 2024
74 Cincinnati Win 11-9 1610.4 Feb 2nd Florida Warm Up 2024
4 Massachusetts Loss 12-13 2109.96 Feb 2nd Florida Warm Up 2024
2 Georgia Loss 9-15 1757.33 Feb 3rd Florida Warm Up 2024
33 Wisconsin Win 12-3 2245.5 Feb 3rd Florida Warm Up 2024
21 Tufts Win 13-8 2324.86 Feb 3rd Florida Warm Up 2024
12 Alabama-Huntsville Win 10-8 2256.34 Feb 4th Florida Warm Up 2024
9 Brown Loss 10-13 1696.93 Mar 2nd Smoky Mountain Invite 2024
15 California Loss 12-13 1799.22 Mar 2nd Smoky Mountain Invite 2024
92 Tennessee Win 13-6 1867.11 Mar 2nd Smoky Mountain Invite 2024
8 Vermont Loss 11-15 1657.43 Mar 2nd Smoky Mountain Invite 2024
11 Minnesota Loss 14-15 1877.24 Mar 3rd Smoky Mountain Invite 2024
23 UCLA Win 15-10 2262.05 Mar 3rd Smoky Mountain Invite 2024
21 Tufts Win 15-11 2209.87 Mar 3rd Smoky Mountain Invite 2024
17 Brigham Young Win 13-12 2000.44 Mar 15th College Mens Centex Tier 1
98 Dartmouth Win 13-7 1803.17 Mar 16th College Mens Centex Tier 1
121 Iowa State** Win 13-3 1755.06 Ignored Mar 16th College Mens Centex Tier 1
31 Middlebury Win 8-7 1782.12 Mar 16th College Mens Centex Tier 1
37 Texas A&M Win 11-9 1840.15 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)