#342 Kentucky-B (4-8)

avg: -3.03  •  sd: 81.75  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
194 Kennesaw State Loss 6-13 258.55 Jan 25th Clutch Classic 2020
300 Belmont University Loss 3-13 -260.03 Jan 25th Clutch Classic 2020
190 Berry** Loss 3-13 278.38 Ignored Jan 25th Clutch Classic 2020
176 Xavier Loss 7-12 406.55 Jan 25th Clutch Classic 2020
301 North Florida Win 8-7 443.87 Jan 26th Clutch Classic 2020
215 Saint Louis** Loss 0-11 171.7 Ignored Jan 26th Clutch Classic 2020
304 North Carolina State-B Loss 4-13 -290.33 Mar 7th Mash Up 2020
274 Haverford Loss 5-12 -115.96 Mar 7th Mash Up 2020
283 Georgia-B Loss 4-13 -155.84 Mar 7th Mash Up 2020
370 Towson -B Win 10-7 -317.79 Mar 7th Mash Up 2020
373 Oberlin-B** Win 13-3 -254.52 Ignored Mar 8th Mash Up 2020
361 George Washington-B Win 12-6 261.07 Mar 8th Mash Up 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)