#81 Rochester (16-5)

avg: 1538.5  •  sd: 68.28  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
159 Brandeis Win 11-10 1342.2 Mar 1st D III River City Showdown 2025
33 Elon Loss 5-13 1225.66 Mar 1st D III River City Showdown 2025
132 Kenyon Win 13-3 1915.76 Mar 1st D III River City Showdown 2025
241 Xavier Win 11-7 1366.45 Mar 1st D III River City Showdown 2025
33 Elon Loss 8-12 1384.51 Mar 2nd D III River City Showdown 2025
101 North Carolina-Asheville Loss 11-12 1323.36 Mar 2nd D III River City Showdown 2025
77 Richmond Win 13-9 1982.77 Mar 2nd D III River City Showdown 2025
161 Ithaca Win 13-7 1772.57 Mar 22nd Salt City Classic
349 Rensselaer Polytech** Win 12-2 1054.88 Ignored Mar 22nd Salt City Classic
147 SUNY-Binghamton Win 8-7 1391.17 Mar 22nd Salt City Classic
80 Williams Win 8-7 1664.38 Mar 22nd Salt City Classic
31 Ottawa Loss 6-15 1254.8 Mar 23rd Salt City Classic
98 SUNY-Buffalo Win 12-10 1695.87 Mar 23rd Salt City Classic
255 Colgate** Win 15-6 1451.11 Ignored Apr 12th Western NY D III Mens Conferences 2025
330 SUNY-Cortland Win 15-8 1136.21 Apr 12th Western NY D III Mens Conferences 2025
161 Ithaca Win 15-4 1815.04 Apr 13th Western NY D III Mens Conferences 2025
176 Hamilton Win 11-5 1757.41 Apr 26th Metro East D III College Mens Regionals 2025
161 Ithaca Win 11-8 1580.65 Apr 26th Metro East D III College Mens Regionals 2025
389 Stevens Tech** Win 13-1 786.02 Ignored Apr 26th Metro East D III College Mens Regionals 2025
161 Ithaca Win 15-6 1815.04 Apr 27th Metro East D III College Mens Regionals 2025
73 Wesleyan Loss 11-15 1206.13 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)