#89 Trinity (15-5)

avg: 1294.64  •  sd: 79.95  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
213 North Texas** Win 7-2 653.19 Ignored Feb 17th Antifreeze 2024
107 Rice Loss 6-7 1022.85 Feb 17th Antifreeze 2024
186 Texas-San Antonio** Win 8-2 1007.02 Ignored Feb 17th Antifreeze 2024
107 Rice Loss 7-8 1022.85 Feb 18th Antifreeze 2024
153 Texas A&M Win 13-4 1365.22 Feb 18th Antifreeze 2024
153 Texas A&M Win 8-3 1365.22 Feb 18th Antifreeze 2024
33 Central Florida Loss 3-13 1219.58 Feb 24th Mardi Gras XXXVI college
136 Florida State Win 12-4 1510.04 Feb 24th Mardi Gras XXXVI college
184 Jacksonville State** Win 11-4 1022.56 Ignored Feb 24th Mardi Gras XXXVI college
211 LSU** Win 13-0 681.9 Ignored Feb 24th Mardi Gras XXXVI college
142 Boston College Win 10-8 1117.97 Feb 25th Mardi Gras XXXVI college
33 Central Florida Loss 6-8 1519.09 Feb 25th Mardi Gras XXXVI college
185 Tulane** Win 13-3 1020.75 Ignored Feb 25th Mardi Gras XXXVI college
60 Colorado College Loss 7-11 1051.53 Mar 16th Womens Centex 2024
211 LSU** Win 13-1 681.9 Ignored Mar 16th Womens Centex 2024
134 MIT Win 12-9 1257.62 Mar 16th Womens Centex 2024
107 Rice Win 12-8 1589.01 Mar 16th Womens Centex 2024
158 Texas State Win 10-8 972.77 Mar 16th Womens Centex 2024
107 Rice Win 12-4 1747.85 Mar 17th Womens Centex 2024
214 Texas-B** Win 10-4 650.33 Ignored Mar 17th Womens Centex 2024
**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)