#48 Texas (6-14)

avg: 1459.95  •  sd: 77.46  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
87 Southern California Win 9-3 1686.09 Feb 18th President’s Day Invite
17 California-San Diego Loss 7-11 1357.68 Feb 18th President’s Day Invite
12 California-Santa Barbara Loss 7-10 1668.76 Feb 18th President’s Day Invite
28 Duke Win 9-7 1961.37 Feb 18th President’s Day Invite
24 Carleton College-Eclipse Loss 7-13 1175.3 Feb 19th President’s Day Invite
3 Colorado Loss 5-10 1893.97 Feb 19th President’s Day Invite
18 Colorado State Win 9-8 1936.5 Feb 20th President’s Day Invite
29 UCLA Win 10-9 1789.62 Feb 20th President’s Day Invite
36 Brown Loss 10-11 1455.07 Mar 18th Womens Centex1
35 Michigan Loss 10-13 1291.43 Mar 18th Womens Centex1
14 Virginia Loss 4-13 1330.66 Mar 18th Womens Centex1
42 Wisconsin Loss 5-12 906.49 Mar 18th Womens Centex1
54 Georgia Tech Loss 8-13 856.7 Mar 19th Womens Centex1
75 Boston University Win 15-7 1821.57 Mar 19th Womens Centex1
45 Washington University Loss 5-7 1151.12 Mar 19th Womens Centex1
6 Brigham Young Loss 7-13 1724.19 Mar 25th Northwest Challenge1
2 British Columbia** Loss 3-13 1948.3 Ignored Mar 25th Northwest Challenge1
9 Washington Loss 8-13 1687.36 Mar 25th Northwest Challenge1
29 UCLA Loss 7-13 1107.09 Mar 26th Northwest Challenge1
74 Utah Win 11-10 1349.4 Mar 26th Northwest Challenge1
**Blowout Eligible


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
  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
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)