#263 NYU (4-7)

avg: 548.42  •  sd: 83.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
201 American Loss 9-10 705.17 Feb 22nd Oak Creek Challenge 2020
278 Salisbury Win 12-10 698.74 Feb 22nd Oak Creek Challenge 2020
207 Drexel Loss 9-12 470.83 Feb 22nd Oak Creek Challenge 2020
296 Slippery Rock Win 13-7 917.47 Feb 23rd Oak Creek Challenge 2020
244 Christopher Newport Loss 11-13 438.24 Feb 23rd Oak Creek Challenge 2020
287 Columbia Loss 9-11 175.66 Mar 7th No Sleep Till Brooklyn 2020
268 Brown-B Win 8-6 808.87 Mar 7th No Sleep Till Brooklyn 2020
188 Williams Loss 6-11 336.66 Mar 7th No Sleep Till Brooklyn 2020
307 SUNY-Cortland Win 11-6 832.37 Mar 8th No Sleep Till Brooklyn 2020
206 Colby Loss 11-12 692.1 Mar 8th No Sleep Till Brooklyn 2020
269 MIT Loss 6-10 1.67 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)