#97 Tulane (7-6)

avg: 273.53  •  sd: 105.73  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
66 George Washington** Loss 4-11 285.01 Jan 25th Clutch Classic 2020
98 Georgia College Win 12-1 634.44 Jan 25th Clutch Classic 2020
12 Georgia Tech** Loss 1-13 1140.54 Ignored Jan 25th Clutch Classic 2020
70 Emory Loss 3-9 156.12 Jan 25th Clutch Classic 2020
98 Georgia College Win 6-5 159.44 Jan 26th Clutch Classic 2020
110 Emory-B** Win 11-0 -251.07 Ignored Jan 26th Clutch Classic 2020
88 Tennessee Loss 6-8 72.03 Jan 26th Clutch Classic 2020
111 Houston** Win 13-0 -260.2 Ignored Feb 8th Antifreeze 2020
91 Rice Win 10-8 612.81 Feb 8th Antifreeze 2020
99 Texas-B Win 8-1 549.54 Feb 8th Antifreeze 2020
91 Rice Loss 7-10 -39.52 Feb 9th Antifreeze 2020
109 Sam Houston State** Win 10-4 193.41 Ignored Feb 9th Antifreeze 2020
96 Trinity Loss 6-8 -16.36 Feb 9th Antifreeze 2020
**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)