#331 Kenyon (3-9)

avg: 559.98  •  sd: 71.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
154 Syracuse Loss 4-13 550.57 Mar 23rd CWRUL Memorial 2019
347 Wright State Win 13-4 1091.16 Mar 23rd CWRUL Memorial 2019
183 Oberlin Loss 6-13 441.96 Mar 23rd CWRUL Memorial 2019
231 Knox Loss 11-13 681.68 Mar 23rd CWRUL Memorial 2019
391 John Carroll Loss 13-14 147.41 Mar 24th CWRUL Memorial 2019
355 Northwestern-B Win 12-10 697.03 Mar 24th CWRUL Memorial 2019
405 Jefferson Win 13-5 789.23 Mar 30th Layout Pigout 2019
96 Bowdoin Loss 6-13 767.81 Mar 30th Layout Pigout 2019
243 Haverford Loss 7-13 324.86 Mar 30th Layout Pigout 2019
163 SUNY-Geneseo Loss 3-13 506.58 Mar 30th Layout Pigout 2019
315 Muhlenberg Loss 11-13 378.13 Mar 31st Layout Pigout 2019
228 Swarthmore Loss 7-13 357.33 Mar 31st Layout Pigout 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)