#237 Georgia Tech-B (3-7)

avg: 574.53  •  sd: 184.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
231 Tulane Win 9-3 1239.41 Jan 27th Clutch Classic 2018
40 Kennesaw State** Loss 4-13 1417.59 Ignored Jan 27th Clutch Classic 2018
267 Emory-B** Win 13-2 506.06 Ignored Jan 27th Clutch Classic 2018
130 Mississippi State Loss 5-8 838.84 Jan 27th Clutch Classic 2018
231 Tulane Loss 5-6 514.41 Mar 10th Tally Classic XIII
107 LSU** Loss 4-15 866.19 Ignored Mar 10th Tally Classic XIII
54 Florida State** Loss 3-11 1256.06 Ignored Mar 10th Tally Classic XIII
19 Vermont** Loss 1-11 1663.48 Ignored Mar 10th Tally Classic XIII
262 Notre Dame-B Win 12-4 712.35 Mar 11th Tally Classic XIII
243 Georgia State Loss 6-12 -74.21 Mar 11th Tally Classic XIII
**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)