#307 SUNY-Cortland (2-10)

avg: 285.68  •  sd: 65.13  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
232 Stevens Tech Loss 7-12 198.39 Feb 22nd Bring The Huckus 10 2020
274 Haverford Loss 5-6 359.04 Feb 22nd Bring The Huckus 10 2020
317 Rutgers-B Win 9-6 641.13 Feb 22nd Bring The Huckus 10 2020
110 Villanova** Loss 1-11 576.86 Ignored Feb 22nd Bring The Huckus 10 2020
269 MIT Loss 6-8 197.33 Feb 23rd Bring The Huckus 10 2020
183 Yale** Loss 5-13 311.15 Ignored Feb 23rd Bring The Huckus 10 2020
182 Amherst College** Loss 3-12 316.74 Ignored Mar 7th No Sleep Till Brooklyn 2020
269 MIT Loss 4-13 -102.17 Mar 7th No Sleep Till Brooklyn 2020
206 Colby Loss 3-11 217.1 Mar 7th No Sleep Till Brooklyn 2020
115 Marist Loss 6-13 553.44 Mar 7th No Sleep Till Brooklyn 2020
287 Columbia Win 10-9 549.87 Mar 8th No Sleep Till Brooklyn 2020
263 NYU Loss 6-11 1.72 Mar 8th No Sleep Till Brooklyn 2020
**Blowout Eligible

FAQ

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
  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
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)