#109 Williams (11-9)

avg: 1296.21  •  sd: 57.93  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
44 Illinois Loss 9-13 1170.46 Feb 3rd Mid Atlantic Warmup 2018
145 Drexel Win 11-8 1514.92 Feb 3rd Mid Atlantic Warmup 2018
227 Syracuse Win 13-9 1254.82 Feb 3rd Mid Atlantic Warmup 2018
34 William & Mary Loss 3-13 1048.2 Feb 3rd Mid Atlantic Warmup 2018
161 Boston University Win 14-10 1486.5 Feb 4th Mid Atlantic Warmup 2018
107 Rutgers Win 15-10 1767.97 Feb 4th Mid Atlantic Warmup 2018
126 Elon Win 10-8 1474.79 Feb 4th Mid Atlantic Warmup 2018
117 Pennsylvania Loss 9-12 925.95 Feb 24th Oak Creek Challenge 2018
80 Amherst Loss 10-12 1171.81 Feb 24th Oak Creek Challenge 2018
243 Rowan Win 13-6 1384.01 Feb 24th Oak Creek Challenge 2018
250 Maryland-Baltimore County Win 13-7 1326.23 Feb 24th Oak Creek Challenge 2018
103 Delaware Loss 13-15 1110 Feb 25th Oak Creek Challenge 2018
250 Maryland-Baltimore County Win 13-5 1368.7 Feb 25th Oak Creek Challenge 2018
194 George Washington Loss 11-13 735.59 Feb 25th Oak Creek Challenge 2018
61 James Madison Loss 10-13 1144.38 Mar 17th Oak Creek Invite 2018
60 Cornell Loss 10-13 1145.09 Mar 17th Oak Creek Invite 2018
149 Davidson Win 13-9 1559.43 Mar 17th Oak Creek Invite 2018
169 Johns Hopkins Win 15-10 1514.28 Mar 18th Oak Creek Invite 2018
103 Delaware Loss 11-13 1095.34 Mar 18th Oak Creek Invite 2018
115 Villanova Win 14-9 1750.54 Mar 18th Oak Creek Invite 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)