#280 South Carolina-B (7-4)

avg: 665.42  •  sd: 80.17  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
303 Charleston Win 10-9 696.48 Mar 3rd Cola Classic 2018
242 Samford Win 7-6 911.01 Mar 3rd Cola Classic 2018
174 East Carolina Loss 7-10 642.76 Mar 3rd Cola Classic 2018
295 Georgia Tech-B Win 10-5 1170.49 Mar 3rd Cola Classic 2018
418 Kennesaw State-B** Win 12-2 445.2 Ignored Mar 4th Cola Classic 2018
125 Georgia College Loss 7-13 658.28 Mar 4th Cola Classic 2018
278 James Madison-B Loss 7-10 283.58 Mar 24th JMU Beenanza 2018
393 Shenandoah Win 13-5 728.97 Mar 24th JMU Beenanza 2018
372 Temple-B Win 12-10 531.05 Mar 24th JMU Beenanza 2018
356 Virginia-B Win 13-6 950.83 Mar 25th JMU Beenanza 2018
256 Christopher Newport Loss 8-13 261.76 Mar 25th JMU Beenanza 2018
**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)