#103 Texas A&M (11-15)

avg: 1240.66  •  sd: 67.14  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
119 Central Florida Win 11-8 1546.99 Jan 31st Florida Warm Up 2025
44 Emory Loss 6-10 1112.03 Jan 31st Florida Warm Up 2025
40 Wisconsin Loss 8-13 1131.85 Jan 31st Florida Warm Up 2025
79 Florida Loss 10-12 1109.76 Feb 1st Florida Warm Up 2025
47 McGill Loss 10-13 1263.3 Feb 1st Florida Warm Up 2025
36 Michigan Loss 10-11 1527.78 Feb 1st Florida Warm Up 2025
51 Purdue Win 9-5 2084.92 Feb 2nd Florida Warm Up 2025
134 South Florida Win 11-10 1237.4 Feb 2nd Florida Warm Up 2025
139 Florida State Loss 11-12 958.69 Feb 22nd Mardi Gras XXXVII
135 Mississippi State Win 13-4 1711.28 Feb 22nd Mardi Gras XXXVII
94 Tennessee-Chattanooga Loss 10-12 1041.44 Feb 22nd Mardi Gras XXXVII
289 Texas-San Antonio** Win 13-5 1015.06 Ignored Feb 22nd Mardi Gras XXXVII
93 Colorado-B Loss 1-13 680.27 Mar 15th Mens Centex 2025
107 Iowa Loss 7-9 943.02 Mar 15th Mens Centex 2025
100 Missouri Loss 5-11 665.35 Mar 15th Mens Centex 2025
74 Oklahoma Christian Loss 10-12 1139.97 Mar 15th Mens Centex 2025
98 Boston College Win 14-12 1492.66 Mar 16th Mens Centex 2025
107 Iowa Loss 13-15 1008.18 Mar 16th Mens Centex 2025
188 Oklahoma State Loss 12-13 738.82 Mar 16th Mens Centex 2025
135 Mississippi State Win 10-9 1236.28 Mar 29th Huck Finn 2025
100 Missouri Win 12-10 1503.48 Mar 29th Huck Finn 2025
84 Ohio State Loss 9-12 974.31 Mar 29th Huck Finn 2025
154 Macalester Win 13-10 1371.08 Mar 29th Huck Finn 2025
44 Emory Loss 11-13 1379.35 Mar 30th Huck Finn 2025
84 Ohio State Win 8-6 1620.16 Mar 30th Huck Finn 2025
74 Oklahoma Christian Win 14-10 1776.79 Mar 30th Huck Finn 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)