#370 Kentucky-B (4-8)

avg: 370.93  •  sd: 99.02  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
308 Alabama-B Loss 4-6 274.21 Jan 26th T Town Throwdown
404 Alabama-Huntsville-B Win 13-5 790.11 Jan 26th T Town Throwdown
296 LSU-B Loss 4-11 95.81 Jan 26th T Town Throwdown
309 Illinois State-B Loss 5-9 104.16 Jan 26th T Town Throwdown
308 Alabama-B Loss 12-15 339.33 Jan 27th T Town Throwdown
404 Alabama-Huntsville-B Win 15-5 790.11 Jan 27th T Town Throwdown
372 Rutgers-B Win 11-8 728.2 Mar 2nd G Dub Fyre Fest 2019
318 Virginia Tech-B Loss 5-13 -3.5 Mar 2nd G Dub Fyre Fest 2019
360 Illinois-Chicago Win 8-5 884.8 Mar 30th Black Penguins Classic 2019
- Iowa State-B Loss 2-8 -22.15 Mar 30th Black Penguins Classic 2019
128 Saint Louis** Loss 1-11 672.58 Ignored Mar 30th Black Penguins Classic 2019
369 Notre Dame-B Loss 4-6 16.24 Mar 30th Black Penguins Classic 2019
**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)