#115 South Florida (7-10)

avg: 1050.61  •  sd: 59.27  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
128 North Georgia Loss 8-9 880.34 Jan 19th Florida Winter Classic 2019
49 Emory Loss 3-11 918.42 Jan 19th Florida Winter Classic 2019
220 Florida Tech** Win 11-1 992.07 Ignored Jan 19th Florida Winter Classic 2019
225 Florida-B** Win 11-0 962.25 Ignored Jan 19th Florida Winter Classic 2019
128 North Georgia Win 9-8 1130.34 Jan 20th Florida Winter Classic 2019
49 Emory Loss 4-12 918.42 Jan 20th Florida Winter Classic 2019
220 Florida Tech Win 10-5 965.96 Jan 20th Florida Winter Classic 2019
143 Alabama Loss 7-9 618.17 Feb 2nd Royal Crown Classic 2019
25 Clemson Loss 5-9 1343.22 Feb 2nd Royal Crown Classic 2019
261 Emory-B** Win 11-0 657.5 Ignored Feb 2nd Royal Crown Classic 2019
225 Florida-B** Win 11-1 962.25 Ignored Feb 2nd Royal Crown Classic 2019
69 Notre Dame Loss 5-13 728.85 Mar 16th Tally Classic XIV
25 Clemson** Loss 2-13 1272.28 Ignored Mar 16th Tally Classic XIV
38 Florida Loss 3-11 1011.11 Mar 16th Tally Classic XIV
41 Harvard Loss 2-13 967.65 Mar 16th Tally Classic XIV
137 Illinois Win 14-7 1519.53 Mar 17th Tally Classic XIV
51 Florida State Loss 10-11 1383.35 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)