#336 SUNY-Cortland (1-10)

avg: 191.65  •  sd: 87.44  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
71 Case Western Reserve** Loss 0-13 805.06 Ignored Mar 1st Oak Creek Challenge 2025
138 RIT** Loss 3-10 492.92 Ignored Mar 1st Oak Creek Challenge 2025
177 Towson** Loss 1-13 325.57 Ignored Mar 1st Oak Creek Challenge 2025
105 Liberty Loss 5-11 632.91 Mar 2nd Oak Creek Challenge 2025
226 SUNY-Albany Loss 11-13 468.23 Mar 2nd Oak Creek Challenge 2025
347 Army Win 9-7 397.63 Mar 29th Northeast Classic 2025
235 College of New Jersey Loss 4-13 55.99 Mar 29th Northeast Classic 2025
217 Haverford Loss 6-13 141.57 Mar 29th Northeast Classic 2025
234 Penn State-B Loss 4-13 56.34 Mar 29th Northeast Classic 2025
321 Pennsylvania Western Loss 10-13 -56.07 Mar 30th Northeast Classic 2025
246 Skidmore Loss 4-13 30.46 Mar 30th Northeast Classic 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)