#244 College of New Jersey (10-7)

avg: 902.61  •  sd: 86.33  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
298 Maryland-Baltimore County Win 13-2 1300.36 Mar 8th First State Invite
277 Salisbury Loss 7-9 500.25 Mar 8th First State Invite
161 Delaware Loss 2-13 630.36 Mar 8th First State Invite
352 Army Win 13-4 1017.21 Mar 29th Northeast Classic 2025
210 Penn State-B Loss 10-11 902.54 Mar 29th Northeast Classic 2025
197 Haverford Win 11-9 1327.68 Mar 29th Northeast Classic 2025
328 SUNY-Cortland Win 13-4 1181.81 Mar 29th Northeast Classic 2025
192 Vassar Loss 7-11 626.18 Mar 30th Northeast Classic 2025
212 SUNY-Albany Win 13-7 1580.97 Mar 30th Northeast Classic 2025
197 Haverford Loss 5-13 478.47 Mar 30th Northeast Classic 2025
- Manhattan** Win 15-1 369.03 Ignored Apr 13th Metro NY D III Mens Conferences 2025
389 Stevens Tech** Win 15-4 787.46 Ignored Apr 13th Metro NY D III Mens Conferences 2025
347 Rensselaer Polytech Win 12-5 1061.28 Apr 26th Metro East D III College Mens Regionals 2025
192 Vassar Loss 5-8 639.46 Apr 26th Metro East D III College Mens Regionals 2025
328 SUNY-Cortland Loss 8-10 319.15 Apr 26th Metro East D III College Mens Regionals 2025
389 Stevens Tech** Win 12-2 787.46 Ignored Apr 26th Metro East D III College Mens Regionals 2025
347 Rensselaer Polytech Win 14-5 1061.28 Apr 27th Metro East D III College Mens Regionals 2025
**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)