#180 South Florida (4-15)

avg: 1000.57  •  sd: 75.42  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
62 Central Florida** Loss 0-11 1198.88 Ignored Jan 13th Florida Winter Classic 2018
174 Florida-B Loss 4-6 671.94 Jan 13th Florida Winter Classic 2018
48 Georgia** Loss 2-11 1355.58 Ignored Jan 13th Florida Winter Classic 2018
141 North Georgia Loss 3-6 695.96 Jan 13th Florida Winter Classic 2018
174 Florida-B Loss 3-6 490.86 Jan 14th Florida Winter Classic 2018
207 Miami Win 8-3 1402.34 Jan 14th Florida Winter Classic 2018
107 LSU Loss 6-11 919.5 Mar 10th Tally Classic XIII
41 Georgia Tech** Loss 2-11 1409.42 Ignored Mar 10th Tally Classic XIII
46 North Carolina-Wilmington Loss 7-11 1511.32 Mar 10th Tally Classic XIII
231 Tulane Win 14-4 1239.41 Mar 11th Tally Classic XIII
54 Florida State Loss 6-13 1256.06 Mar 11th Tally Classic XIII
245 George Mason University Win 14-2 1094.17 Mar 11th Tally Classic XIII
108 Wisconsin-Eau Claire Loss 5-12 847.12 Mar 17th College Southerns 2018
54 Florida State Loss 5-9 1327 Mar 17th College Southerns 2018
174 Florida-B Loss 7-8 912.55 Mar 17th College Southerns 2018
108 Wisconsin-Eau Claire Loss 0-9 847.12 Mar 18th College Southerns 2018
124 Carleton College-Eclipse Loss 2-13 752.52 Mar 18th College Southerns 2018
271 Charleston** Win 9-3 600 Ignored Mar 18th College Southerns 2018
174 Florida-B Loss 0-5 437.55 Mar 18th College Southerns 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)