#189 Tulane (5-14)

avg: 593.98  •  sd: 86.73  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
87 Auburn Loss 6-8 934.77 Jan 26th Clutch Classic 2019
98 Mississippi State Loss 4-12 528.15 Jan 26th Clutch Classic 2019
104 Boston College Loss 5-10 521.08 Jan 26th Clutch Classic 2019
254 Georgia Tech-B Win 12-1 715.46 Jan 26th Clutch Classic 2019
102 LSU Win 7-5 1447.57 Jan 27th Clutch Classic 2019
180 Georgia State Loss 7-8 505.73 Jan 27th Clutch Classic 2019
109 Texas State Loss 5-11 464.56 Feb 16th Big D in lil d Women
120 Arizona State Loss 7-10 637.51 Feb 16th Big D in lil d Women
237 North Texas Win 10-2 859.68 Feb 16th Big D in lil d Women
72 Texas-Dallas** Loss 2-11 726.4 Ignored Feb 16th Big D in lil d Women
195 Texas A&M Loss 6-7 443.44 Feb 17th Big D in lil d Women
191 Texas Christian Win 5-2 1178.39 Feb 17th Big D in lil d Women
260 Hendrix Win 11-1 670.91 Feb 17th Big D in lil d Women
25 Clemson** Loss 2-13 1272.28 Ignored Mar 16th Tally Classic XIV
51 Florida State** Loss 1-13 908.35 Ignored Mar 16th Tally Classic XIV
93 Kennesaw State Loss 6-13 588.04 Mar 16th Tally Classic XIV
41 Harvard** Loss 3-13 967.65 Ignored Mar 16th Tally Classic XIV
114 Minnesota-Duluth Loss 6-14 452.38 Mar 17th Tally Classic XIV
220 Florida Tech Loss 0-15 -207.93 Mar 17th Tally Classic XIV
**Blowout Eligible

FAQ

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
  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
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)