#31 Ottawa (17-1)

avg: 1854.8  •  sd: 69.7  •  top 16/20: 4.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
182 Carleton University** Win 13-2 1738.54 Ignored Mar 22nd Salt City Classic
100 Syracuse Win 13-5 2053.35 Mar 22nd Salt City Classic
98 SUNY-Buffalo Win 13-7 2015.28 Mar 22nd Salt City Classic
148 Rhode Island Win 13-6 1865.72 Mar 22nd Salt City Classic
80 Williams Win 15-10 1992.99 Mar 23rd Salt City Classic
81 Rochester Win 15-6 2138.5 Mar 23rd Salt City Classic
115 RIT Win 15-10 1820.61 Apr 12th Western NY D I Mens Conferences 2025
150 Toronto Win 14-12 1483.55 Apr 12th Western NY D I Mens Conferences 2025
98 SUNY-Buffalo Win 15-7 2057.75 Apr 12th Western NY D I Mens Conferences 2025
147 SUNY-Binghamton Win 15-5 1866.17 Apr 12th Western NY D I Mens Conferences 2025
51 Cornell Loss 8-13 1210 Apr 13th Western NY D I Mens Conferences 2025
98 SUNY-Buffalo Win 9-7 1737.09 Apr 13th Western NY D I Mens Conferences 2025
248 NYU** Win 15-0 1475.07 Ignored Apr 26th Metro East D I College Mens Regionals 2025
314 Rowan** Win 15-3 1223.62 Ignored Apr 26th Metro East D I College Mens Regionals 2025
115 RIT Win 15-8 1931.81 Apr 26th Metro East D I College Mens Regionals 2025
51 Cornell Win 15-8 2270.96 Apr 27th Metro East D I College Mens Regionals 2025
150 Toronto Win 15-3 1862.59 Apr 27th Metro East D I College Mens Regionals 2025
93 Yale Win 11-10 1601.76 Apr 27th 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)