#189 Baylor (14-10)

avg: 1107.7  •  sd: 64.51  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
280 Texas-San Antonio Win 15-7 1377.27 Feb 1st Big D in Little D 2025
323 Rice Win 15-8 1163.68 Feb 1st Big D in Little D 2025
283 Texas State Win 14-5 1362.57 Feb 1st Big D in Little D 2025
275 Texas Tech Win 13-11 1017.06 Feb 2nd Big D in Little D 2025
130 North Texas Loss 10-15 876.62 Feb 2nd Big D in Little D 2025
220 Texas-B Win 12-9 1346.03 Mar 15th Mens Centex 2025
183 Tarleton State Loss 10-11 1023.38 Mar 15th Mens Centex 2025
85 Boston College Loss 5-11 916.2 Mar 15th Mens Centex 2025
284 Harding Loss 5-13 161.43 Mar 15th Mens Centex 2025
332 Texas A&M-B Win 15-11 954.15 Mar 16th Mens Centex 2025
245 Texas-Dallas Win 15-11 1282.54 Mar 16th Mens Centex 2025
283 Texas State Win 14-9 1236.43 Mar 16th Mens Centex 2025
305 Sam Houston Win 13-1 1267.7 Mar 29th Huckfest 2025
332 Texas A&M-B Win 13-3 1172.98 Mar 29th Huckfest 2025
394 Stephen F Austin** Win 13-2 737.93 Ignored Mar 29th Huckfest 2025
272 Oklahoma Win 13-6 1405.29 Mar 29th Huckfest 2025
183 Tarleton State Loss 12-15 847.88 Mar 30th Huckfest 2025
183 Tarleton State Loss 10-12 910.25 Apr 12th North Texas D I Mens Conferences 2025
394 Stephen F Austin** Win 15-5 737.93 Ignored Apr 12th North Texas D I Mens Conferences 2025
130 North Texas Loss 7-10 940.56 Apr 13th North Texas D I Mens Conferences 2025
245 Texas-Dallas Loss 8-9 776.38 Apr 13th North Texas D I Mens Conferences 2025
14 Texas Loss 7-15 1465.09 Apr 26th South Central D I College Mens Regionals 2025
191 Oklahoma State Win 15-7 1700.4 Apr 26th South Central D I College Mens Regionals 2025
159 Kansas Loss 10-12 994.01 Apr 27th South Central 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)