#299 Towson (3-9)

avg: 682.65  •  sd: 98.81  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
248 Shippensburg Loss 2-11 266.33 Feb 23rd Oak Creek Challenge 2019
142 Princeton Loss 4-11 609.71 Feb 23rd Oak Creek Challenge 2019
250 Maryland-Baltimore County Loss 6-10 359.1 Feb 23rd Oak Creek Challenge 2019
157 Drexel Loss 5-11 529.41 Feb 23rd Oak Creek Challenge 2019
292 Navy Loss 3-15 102.9 Feb 24th Oak Creek Challenge 2019
345 American Win 11-7 968.7 Feb 24th Oak Creek Challenge 2019
139 Pennsylvania Loss 10-11 1104.67 Mar 30th Atlantic Coast Open 2019
158 Lehigh Loss 4-12 529.08 Mar 30th Atlantic Coast Open 2019
187 NYU Win 9-8 1155.6 Mar 30th Atlantic Coast Open 2019
137 North Carolina-B Loss 7-12 712.64 Mar 30th Atlantic Coast Open 2019
345 American Win 15-5 1101.81 Mar 31st Atlantic Coast Open 2019
197 George Mason Loss 5-14 401.39 Mar 31st Atlantic Coast Open 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)