#191 NYU (10-16)

avg: 1156.8  •  sd: 44.48  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
162 Rutgers Loss 7-9 1003.21 Feb 5th New Jersey Warmup
163 Columbia Win 10-9 1400.42 Feb 10th New Jersey Warmup
97 Lehigh Loss 6-10 1030.18 Feb 10th New Jersey Warmup
56 Temple Loss 5-13 1143.67 Feb 10th New Jersey Warmup
120 Syracuse Loss 7-12 882.75 Feb 11th New Jersey Warmup
92 Pennsylvania Loss 9-13 1120.8 Feb 11th New Jersey Warmup
162 Rutgers Loss 10-12 1044.42 Feb 11th New Jersey Warmup
80 Bates Loss 8-10 1328.3 Mar 2nd No Sleep till Brooklyn 2024
300 SUNY-Stony Brook Win 13-3 1314.83 Mar 2nd No Sleep till Brooklyn 2024
55 Williams Loss 7-12 1229.06 Mar 2nd No Sleep till Brooklyn 2024
141 Boston University Loss 2-13 742.31 Mar 3rd No Sleep till Brooklyn 2024
283 Hofstra Win 13-3 1409.27 Mar 3rd No Sleep till Brooklyn 2024
131 Yale Loss 8-10 1116.71 Mar 3rd No Sleep till Brooklyn 2024
283 Hofstra Win 11-10 934.27 Apr 13th Metro NY D I Mens Conferences 2024
300 SUNY-Stony Brook Win 10-6 1210.99 Apr 13th Metro NY D I Mens Conferences 2024
93 Princeton Loss 8-12 1096.88 Apr 13th Metro NY D I Mens Conferences 2024
272 Rowan Win 12-5 1456.78 Apr 13th Metro NY D I Mens Conferences 2024
162 Rutgers Win 10-8 1545.21 Apr 14th Metro NY D I Mens Conferences 2024
93 Princeton Loss 6-13 938.03 Apr 14th Metro NY D I Mens Conferences 2024
162 Rutgers Win 15-14 1407.55 Apr 14th Metro NY D I Mens Conferences 2024
163 Columbia Loss 9-14 801.56 Apr 27th Metro East D I College Mens Regionals 2024
102 Connecticut Loss 11-13 1266.28 Apr 27th Metro East D I College Mens Regionals 2024
272 Rowan Win 15-11 1237.95 Apr 27th Metro East D I College Mens Regionals 2024
103 SUNY-Binghamton Loss 3-15 891.64 Apr 27th Metro East D I College Mens Regionals 2024
166 RIT Loss 11-15 886.66 Apr 28th Metro East D I College Mens Regionals 2024
240 SUNY-Albany Win 15-8 1552.59 Apr 28th Metro East D I 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)