#281 Towson-B (1-10)

avg: -411.7  •  sd: 122.62  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
273 American-B Loss 5-7 -526.65 Feb 16th Cherry Blossom Classic 2019
226 Brown-B** Loss 2-13 -243.03 Feb 16th Cherry Blossom Classic 2019
240 Georgetown-B** Loss 1-9 -367.44 Ignored Feb 16th Cherry Blossom Classic 2019
239 Columbia-B** Loss 2-11 -365.18 Ignored Feb 17th Cherry Blossom Classic 2019
263 George Washington-B Loss 3-10 -577.66 Feb 17th Cherry Blossom Classic 2019
165 Temple** Loss 0-13 177.8 Ignored Mar 30th Towsontown Throwdown 2019
258 Maryland-Baltimore County Loss 3-7 -524.08 Mar 30th Towsontown Throwdown 2019
131 Rutgers** Loss 0-13 390.5 Ignored Mar 30th Towsontown Throwdown 2019
117 Catholic** Loss 1-13 443.8 Ignored Mar 30th Towsontown Throwdown 2019
236 SUNY-Buffalo** Loss 0-12 -336.32 Mar 31st Towsontown Throwdown 2019
283 Rhode Island Win 7-6 -342.75 Mar 31st Towsontown Throwdown 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)