#417 West Chester-B (1-7)

avg: -145.68  •  sd: 130.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
220 Dartmouth-B** Loss 4-13 273.7 Ignored Feb 24th Oak Creek Challenge 2018
215 Lancaster Bible** Loss 2-13 301.73 Feb 24th Oak Creek Challenge 2018
421 American-B Loss 6-9 -599.46 Feb 24th Oak Creek Challenge 2018
324 Georgetown-B** Loss 0-13 -110.56 Ignored Feb 24th Oak Creek Challenge 2018
302 Salisbury Loss 8-13 78.37 Feb 25th Oak Creek Challenge 2018
389 Hofstra Loss 3-10 -439.05 Mar 4th Atlantic City 7 2018
166 MIT** Loss 1-13 466.51 Ignored Mar 4th Atlantic City 7 2018
434 MIT-B Win 7-3 -141.44 Mar 4th Atlantic City 7 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)