#149 Rutgers (14-12)

avg: 1278.67  •  sd: 76.41  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
108 Columbia Loss 8-11 1077.84 Feb 8th NJ Warmup 2025
94 Lehigh Loss 6-10 984.66 Feb 8th NJ Warmup 2025
248 NYU Loss 7-10 498.36 Feb 8th NJ Warmup 2025
219 Princeton Loss 11-12 884.83 Feb 8th NJ Warmup 2025
154 Johns Hopkins Win 13-3 1856.34 Mar 1st Oak Creek Challenge 2025
94 Lehigh Win 10-8 1743.48 Mar 1st Oak Creek Challenge 2025
97 SUNY-Buffalo Win 10-1 2074.63 Mar 1st Oak Creek Challenge 2025
80 Case Western Reserve Win 10-9 1687.39 Mar 2nd Oak Creek Challenge 2025
51 Cornell Win 9-8 1848.89 Mar 2nd Oak Creek Challenge 2025
207 Towson Win 10-7 1428.87 Mar 2nd Oak Creek Challenge 2025
80 Case Western Reserve Loss 8-9 1437.39 Mar 29th East Coast Invite 2025
207 Towson Win 10-8 1301.87 Mar 29th East Coast Invite 2025
146 SUNY-Binghamton Loss 7-8 1158.61 Mar 29th East Coast Invite 2025
99 Syracuse Loss 5-15 868.46 Mar 29th East Coast Invite 2025
123 Connecticut Loss 7-9 1081.1 Mar 30th East Coast Invite 2025
154 Johns Hopkins Loss 6-9 837.77 Mar 30th East Coast Invite 2025
248 NYU Win 13-5 1488.02 Mar 30th East Coast Invite 2025
388 New Jersey Tech** Win 15-2 788.9 Ignored Apr 12th Metro NY D I Mens Conferences 2025
219 Princeton Win 12-6 1589.15 Apr 12th Metro NY D I Mens Conferences 2025
313 Rowan** Win 15-4 1228.76 Ignored Apr 12th Metro NY D I Mens Conferences 2025
108 Columbia Win 13-12 1568.45 Apr 13th Metro NY D I Mens Conferences 2025
248 NYU Win 13-5 1488.02 Apr 13th Metro NY D I Mens Conferences 2025
335 Connecticut-B** Win 15-3 1158.9 Ignored Apr 26th Metro East D I College Mens Regionals 2025
99 Syracuse Loss 7-14 885.57 Apr 26th Metro East D I College Mens Regionals 2025
92 Yale Loss 9-15 974.74 Apr 26th Metro East D I College Mens Regionals 2025
147 Toronto Loss 11-15 898.94 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)