#231 Pennsylvania-B (8-12)

avg: 317.18  •  sd: 73.31  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
61 James Madison** Loss 0-12 835.16 Ignored Feb 23rd Commonwealth Cup 2019
262 Michigan-B Win 13-6 634.6 Feb 23rd Commonwealth Cup 2019
51 Florida State** Loss 0-9 908.35 Ignored Feb 23rd Commonwealth Cup 2019
135 Princeton** Loss 3-12 342.48 Ignored Feb 23rd Commonwealth Cup 2019
161 Drexel Loss 5-12 210.99 Feb 24th Commonwealth Cup 2019
93 Kennesaw State** Loss 1-9 588.04 Ignored Feb 24th Commonwealth Cup 2019
248 Johns Hopkins University Loss 8-9 37.52 Mar 23rd Jersey Devil 8
256 College of New Jersey Win 7-6 234.93 Mar 23rd Jersey Devil 8
241 Cornell-B Win 7-6 353.44 Mar 23rd Jersey Devil 8
253 Wellesley-B Win 8-7 244.54 Mar 23rd Jersey Devil 8
142 Amherst Loss 5-12 298.03 Mar 24th Jersey Devil 8
256 College of New Jersey Win 10-6 606.09 Mar 24th Jersey Devil 8
106 Lehigh** Loss 2-11 479.64 Ignored Mar 24th Jersey Devil 8
253 Wellesley-B Win 5-4 244.54 Mar 24th Jersey Devil 8
52 Columbia** Loss 0-15 903.27 Ignored Mar 30th West Chester Ram Jam 2019
257 Millersville Win 7-5 427.22 Mar 30th West Chester Ram Jam 2019
31 West Chester** Loss 3-11 1114.02 Ignored Mar 30th West Chester Ram Jam 2019
76 Rensselaer Polytech** Loss 1-14 685.87 Ignored Mar 30th West Chester Ram Jam 2019
133 Haverford** Loss 4-10 378.63 Ignored Mar 31st West Chester Ram Jam 2019
257 Millersville Win 9-8 224.08 Mar 31st West Chester Ram Jam 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)