#187 College of New Jersey (13-6)

avg: 1163.42  •  sd: 80.09  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
359 Bentley** Win 9-1 1053.96 Ignored Mar 2nd Philly Special 2024
259 Brandeis Win 7-2 1514.93 Mar 2nd Philly Special 2024
240 SUNY-Albany Win 3-1 1587.78 Mar 2nd Philly Special 2024
115 Bowdoin Win 11-9 1686.14 Mar 3rd Philly Special 2024
188 Brown-B Win 9-8 1288.41 Mar 3rd Philly Special 2024
272 Rowan Win 12-9 1202.15 Mar 3rd Philly Special 2024
130 Penn State-B Loss 9-11 1130.21 Mar 3rd Philly Special 2024
213 Ithaca Win 8-6 1376.13 Mar 30th Northeast Classic 2024
181 SUNY-Cortland Loss 5-8 739.61 Mar 30th Northeast Classic 2024
159 Rhode Island Win 9-8 1413.15 Mar 31st Northeast Classic 2024
136 Wesleyan Loss 8-9 1243.91 Mar 31st Northeast Classic 2024
108 Vermont-B Win 11-9 1724.24 Mar 31st Northeast Classic 2024
- Manhattan** Win 15-0 758.3 Ignored Apr 13th Metro NY D III Mens Conferences 2024
310 Stevens Tech Win 15-5 1278.83 Apr 13th Metro NY D III Mens Conferences 2024
252 Hamilton Win 15-10 1384.45 Apr 27th Metro East D III College Mens Regionals 2024
364 Rensselaer Polytech Win 14-7 1015.13 Apr 27th Metro East D III College Mens Regionals 2024
199 Connecticut College Loss 11-12 1000.75 Apr 27th Metro East D III College Mens Regionals 2024
245 Skidmore Loss 8-15 413.26 Apr 27th Metro East D III College Mens Regionals 2024
267 SUNY-Geneseo Loss 12-14 650.53 Apr 28th Metro East D III College Mens Regionals 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)