#83 Clemson (12-6)

avg: 1163.67  •  sd: 73.5  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
203 Tennessee-Chattanooga** Win 13-1 995.01 Ignored Mar 8th The Only Tenn I See 2025
81 Tennessee Loss 8-9 1071.93 Mar 8th The Only Tenn I See 2025
203 Tennessee-Chattanooga Win 12-7 915.52 Mar 9th The Only Tenn I See 2025
81 Tennessee Loss 6-11 650.23 Mar 9th The Only Tenn I See 2025
80 Case Western Reserve Loss 4-9 601.55 Mar 29th Needle in a Ho Stack 2025
126 North Carolina-Wilmington Win 7-6 972.29 Mar 29th Needle in a Ho Stack 2025
99 Emory Loss 8-9 950.33 Mar 29th Needle in a Ho Stack 2025
212 Georgia-B** Win 11-3 872.62 Ignored Mar 29th Needle in a Ho Stack 2025
115 Cedarville Win 11-10 1072.08 Mar 30th Needle in a Ho Stack 2025
219 Florida Tech** Win 15-5 816.79 Ignored Mar 30th Needle in a Ho Stack 2025
149 Davidson Win 10-5 1278.42 Mar 30th Needle in a Ho Stack 2025
78 Appalachian State Win 12-8 1656.32 Apr 12th Carolina D I Womens Conferences 2025
159 Charleston Win 13-2 1265 Apr 12th Carolina D I Womens Conferences 2025
126 North Carolina-Wilmington Win 10-5 1421.18 Apr 12th Carolina D I Womens Conferences 2025
9 North Carolina** Loss 3-13 1522.91 Ignored Apr 12th Carolina D I Womens Conferences 2025
60 South Carolina Loss 9-13 966.41 Apr 13th Carolina D I Womens Conferences 2025
69 North Carolina State Win 13-12 1419.32 Apr 13th Carolina D I Womens Conferences 2025
60 South Carolina Win 12-10 1623.1 Apr 13th Carolina D I Womens Conferences 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)