#248 Shippensburg (5-7)

avg: 866.33  •  sd: 81.7  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
250 Maryland-Baltimore County Win 10-9 980.26 Feb 23rd Oak Creek Challenge 2019
142 Princeton Loss 6-11 663.01 Feb 23rd Oak Creek Challenge 2019
157 Drexel Loss 6-13 529.41 Feb 23rd Oak Creek Challenge 2019
299 Towson Win 11-2 1282.65 Feb 23rd Oak Creek Challenge 2019
338 Wake Forest Win 15-7 1133.6 Feb 24th Oak Creek Challenge 2019
158 Lehigh Loss 6-15 529.08 Feb 24th Oak Creek Challenge 2019
166 Virginia Commonwealth Win 14-12 1312.78 Feb 24th Oak Creek Challenge 2019
85 Richmond Loss 7-12 909.19 Mar 30th D3 EASTUR 2019
141 Wesleyan Loss 8-13 719.09 Mar 30th D3 EASTUR 2019
182 Messiah Loss 6-12 463.53 Mar 30th D3 EASTUR 2019
320 Ohio State-B Win 13-8 1088.7 Mar 31st D3 EASTUR 2019
113 Davidson Loss 9-13 883.33 Mar 31st D3 EASTUR 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)