#157 Drexel (8-13)

avg: 1129.41  •  sd: 52.5  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
87 Case Western Reserve Loss 6-11 875.86 Feb 2nd Mid Atlantic Warmup 2019
88 Tennessee-Chattanooga Loss 9-11 1169.98 Feb 2nd Mid Atlantic Warmup 2019
32 William & Mary** Loss 5-13 1146.68 Ignored Feb 2nd Mid Atlantic Warmup 2019
39 Vermont Loss 4-13 1105.77 Feb 2nd Mid Atlantic Warmup 2019
120 James Madison Loss 9-15 767.32 Feb 3rd Mid Atlantic Warmup 2019
195 George Washington Win 15-12 1304.3 Feb 3rd Mid Atlantic Warmup 2019
166 Virginia Commonwealth Win 15-13 1306.01 Feb 3rd Mid Atlantic Warmup 2019
137 North Carolina-B Loss 11-15 851.99 Feb 3rd Mid Atlantic Warmup 2019
248 Shippensburg Win 13-6 1466.33 Feb 23rd Oak Creek Challenge 2019
142 Princeton Loss 9-10 1084.71 Feb 23rd Oak Creek Challenge 2019
250 Maryland-Baltimore County Win 13-6 1455.26 Feb 23rd Oak Creek Challenge 2019
299 Towson Win 11-5 1282.65 Feb 23rd Oak Creek Challenge 2019
114 Liberty Loss 10-15 846.51 Feb 24th Oak Creek Challenge 2019
174 Cedarville Loss 12-13 942.46 Feb 24th Oak Creek Challenge 2019
206 West Chester Win 11-6 1512.94 Feb 24th Oak Creek Challenge 2019
47 Maryland Loss 3-13 1056.33 Mar 16th Oak Creek Invite 2019
150 Cornell Win 13-10 1506.23 Mar 16th Oak Creek Invite 2019
73 Temple Loss 7-13 923.34 Mar 16th Oak Creek Invite 2019
197 George Mason Win 13-11 1230.23 Mar 16th Oak Creek Invite 2019
120 James Madison Loss 12-13 1157.8 Mar 17th Oak Creek Invite 2019
110 Williams Loss 6-15 715.82 Mar 17th Oak Creek Invite 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)