#50 Tulane (14-11)

avg: 1708.09  •  sd: 57.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
28 Minnesota Loss 9-13 1448.58 Jan 31st Florida Warm Up 2025
53 Purdue Loss 7-10 1303.22 Jan 31st Florida Warm Up 2025
19 Vermont Win 11-10 2123.44 Jan 31st Florida Warm Up 2025
7 Brigham Young Loss 8-13 1716.35 Feb 1st Florida Warm Up 2025
47 Emory Loss 11-12 1610.48 Feb 1st Florida Warm Up 2025
71 Florida Win 12-8 2032.12 Feb 1st Florida Warm Up 2025
39 McGill Win 10-8 2041.3 Feb 2nd Florida Warm Up 2025
49 Chicago Loss 8-12 1288.17 Mar 15th Mens Centex 2025
57 Oklahoma Christian Loss 7-8 1531.39 Mar 15th Mens Centex 2025
14 Texas Loss 3-13 1442.23 Mar 15th Mens Centex 2025
25 Utah Valley Loss 10-13 1615.86 Mar 15th Mens Centex 2025
86 Colorado-B Win 15-12 1793.9 Mar 16th Mens Centex 2025
70 Dartmouth Loss 8-9 1470.96 Mar 16th Mens Centex 2025
97 Missouri Win 14-13 1584.97 Mar 16th Mens Centex 2025
67 Auburn Win 11-7 2088.69 Apr 12th Gulf Coast D I Mens Conferences 2025
190 Vanderbilt** Win 13-4 1689.21 Ignored Apr 12th Gulf Coast D I Mens Conferences 2025
130 LSU Win 11-7 1790.05 Apr 12th Gulf Coast D I Mens Conferences 2025
102 Alabama Win 14-9 1921.17 Apr 13th Gulf Coast D I Mens Conferences 2025
68 Alabama-Huntsville Win 12-7 2130.77 Apr 13th Gulf Coast D I Mens Conferences 2025
172 Alabama-Birmingham Win 13-12 1299.88 Apr 26th Southeast D I College Mens Regionals 2025
67 Auburn Win 15-10 2075.4 Apr 26th Southeast D I College Mens Regionals 2025
47 Emory Win 13-11 1964.32 Apr 26th Southeast D I College Mens Regionals 2025
67 Auburn Loss 10-15 1168.19 Apr 27th Southeast D I College Mens Regionals 2025
128 Central Florida Win 15-8 1892.48 Apr 27th Southeast D I College Mens Regionals 2025
17 Georgia Loss 10-15 1561 Apr 27th Southeast D I College Mens Regionals 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)