#93 Rice (12-6)

avg: 1281.27  •  sd: 74.02  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
232 North Texas** Win 9-0 699.18 Ignored Feb 17th Antifreeze 2024
196 Texas-San Antonio Win 9-4 1086.27 Feb 17th Antifreeze 2024
66 Trinity Win 7-6 1581.11 Feb 17th Antifreeze 2024
153 Texas State Win 9-2 1461.23 Feb 18th Antifreeze 2024
66 Trinity Win 8-7 1581.11 Feb 18th Antifreeze 2024
32 Central Florida Loss 4-15 1217.19 Mar 16th Womens Centex 2024
90 MIT Win 13-9 1724.96 Mar 16th Womens Centex 2024
206 Texas-B** Win 13-1 986.61 Ignored Mar 16th Womens Centex 2024
153 Texas State Win 13-1 1461.23 Mar 16th Womens Centex 2024
66 Trinity Loss 8-12 1014.96 Mar 16th Womens Centex 2024
221 LSU** Win 13-0 856.64 Ignored Mar 17th Womens Centex 2024
66 Trinity Loss 4-12 856.11 Mar 17th Womens Centex 2024
48 Colorado College Loss 2-13 1013.36 Apr 13th South Central D III Womens Conferences 2024
143 John Brown Win 8-6 1219.95 Apr 13th South Central D III Womens Conferences 2024
133 Truman State Win 11-3 1581.98 Apr 13th South Central D III Womens Conferences 2024
66 Trinity Loss 4-5 1331.11 Apr 13th South Central D III Womens Conferences 2024
48 Colorado College Loss 5-10 1039.47 Apr 14th South Central D III Womens Conferences 2024
223 Colorado College-B** Win 9-2 826.21 Ignored Apr 14th South Central D III Womens Conferences 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)