#13 Texas (15-7)

avg: 1970.85  •  sd: 54.7  •  top 16/20: 98.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
79 Florida Win 12-7 1868.39 Jan 31st Florida Warm Up 2025
43 Virginia Tech Win 13-6 2212.2 Jan 31st Florida Warm Up 2025
20 Vermont Loss 11-13 1629.24 Jan 31st Florida Warm Up 2025
19 Georgia Win 12-9 2237.86 Feb 1st Florida Warm Up 2025
19 Georgia Win 13-10 2220.64 Feb 1st Florida Warm Up 2025
31 Minnesota Win 13-11 1942.31 Feb 1st Florida Warm Up 2025
4 Carleton College Loss 9-13 1784.74 Feb 2nd Florida Warm Up 2025
15 Washington University Win 12-9 2297 Feb 2nd Florida Warm Up 2025
21 Georgia Tech Loss 11-13 1625.95 Mar 1st Smoky Mountain Invite 2025
31 Minnesota Win 13-5 2313.47 Mar 1st Smoky Mountain Invite 2025
18 Northeastern Loss 12-15 1595.21 Mar 1st Smoky Mountain Invite 2025
1 Massachusetts Loss 12-13 2134.1 Mar 1st Smoky Mountain Invite 2025
16 Brown Loss 13-15 1705.55 Mar 2nd Smoky Mountain Invite 2025
25 Penn State Win 15-12 2127.87 Mar 2nd Smoky Mountain Invite 2025
65 Tennessee Win 15-9 1969.02 Mar 2nd Smoky Mountain Invite 2025
35 Chicago Win 11-8 2019.94 Mar 15th Mens Centex 2025
29 Utah Valley Win 13-12 1877.25 Mar 15th Mens Centex 2025
62 Tulane Win 13-3 2062.36 Mar 15th Mens Centex 2025
17 Tufts Win 10-5 2470.91 Mar 15th Mens Centex 2025
74 Oklahoma Christian Win 15-11 1759.26 Mar 16th Mens Centex 2025
40 Wisconsin Win 15-10 2081.61 Mar 16th Mens Centex 2025
17 Tufts Loss 10-13 1568.87 Mar 16th Mens Centex 2025
**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)