#248 NYU (6-15)

avg: 888.02  •  sd: 81.69  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
123 Connecticut Loss 5-11 760.44 Feb 8th NJ Warmup 2025
167 Pennsylvania Loss 4-13 609.59 Feb 8th NJ Warmup 2025
99 Syracuse Win 11-9 1717.66 Feb 8th NJ Warmup 2025
149 Rutgers Win 10-7 1668.33 Feb 8th NJ Warmup 2025
64 Georgetown** Loss 3-15 1045.71 Ignored Mar 29th East Coast Invite 2025
154 Johns Hopkins Loss 8-9 1131.34 Mar 29th East Coast Invite 2025
167 Pennsylvania Loss 5-8 755.98 Mar 29th East Coast Invite 2025
97 SUNY-Buffalo Loss 6-12 895.32 Mar 29th East Coast Invite 2025
230 Harvard Loss 4-15 357.31 Mar 30th East Coast Invite 2025
149 Rutgers Loss 5-13 678.67 Mar 30th East Coast Invite 2025
207 Towson Loss 1-15 439.2 Mar 30th East Coast Invite 2025
108 Columbia Loss 6-15 843.45 Apr 12th Metro NY D I Mens Conferences 2025
331 Hofstra Loss 10-12 336.33 Apr 12th Metro NY D I Mens Conferences 2025
386 SUNY-Stony Brook** Win 15-6 795.2 Ignored Apr 12th Metro NY D I Mens Conferences 2025
219 Princeton Win 11-8 1375.44 Apr 12th Metro NY D I Mens Conferences 2025
149 Rutgers Loss 5-13 678.67 Apr 13th Metro NY D I Mens Conferences 2025
313 Rowan Win 10-5 1202.66 Apr 13th Metro NY D I Mens Conferences 2025
123 Connecticut Loss 8-13 864.28 Apr 26th Metro East D I College Mens Regionals 2025
30 Ottawa** Loss 0-15 1272.88 Ignored Apr 26th Metro East D I College Mens Regionals 2025
115 RIT Loss 10-15 930.61 Apr 26th Metro East D I College Mens Regionals 2025
313 Rowan Win 13-8 1124.92 Apr 26th Metro East D I 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)