#330 SUNY-Cortland (4-17)

avg: 571.4  •  sd: 62.77  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
79 Case Western Reserve** Loss 0-13 945.72 Ignored Mar 1st Oak Creek Challenge 2025
115 RIT** Loss 3-10 767 Ignored Mar 1st Oak Creek Challenge 2025
208 Towson Loss 1-13 424.8 Mar 1st Oak Creek Challenge 2025
111 Liberty Loss 5-11 788.97 Mar 2nd Oak Creek Challenge 2025
213 SUNY-Albany Loss 11-13 783.58 Mar 2nd Oak Creek Challenge 2025
355 Army Win 9-7 693.18 Mar 29th Northeast Classic 2025
243 College of New Jersey Loss 4-13 293.37 Mar 29th Northeast Classic 2025
196 Haverford Loss 6-13 468.39 Mar 29th Northeast Classic 2025
209 Penn State-B Loss 4-13 424.7 Mar 29th Northeast Classic 2025
337 Pennsylvania Western Loss 10-13 222.96 Mar 30th Northeast Classic 2025
235 Skidmore Loss 4-13 315.2 Mar 30th Northeast Classic 2025
255 Colgate Loss 11-13 622.27 Apr 12th Western NY D III Mens Conferences 2025
81 Rochester Loss 8-15 973.69 Apr 12th Western NY D III Mens Conferences 2025
176 Hamilton Loss 3-14 557.41 Apr 13th Western NY D III Mens Conferences 2025
267 SUNY-Geneseo Loss 7-13 242.18 Apr 13th Western NY D III Mens Conferences 2025
362 SUNY-Oneonta Win 15-4 982.31 Apr 13th Western NY D III Mens Conferences 2025
243 College of New Jersey Win 10-8 1156.03 Apr 26th Metro East D III College Mens Regionals 2025
415 New Haven** Win 12-1 257.1 Ignored Apr 26th Metro East D III College Mens Regionals 2025
235 Skidmore Loss 9-10 790.2 Apr 26th Metro East D III College Mens Regionals 2025
267 SUNY-Geneseo Loss 5-12 199.71 Apr 26th Metro East D III College Mens Regionals 2025
235 Skidmore Loss 4-15 315.2 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)