#120 NYU (7-5)

avg: 1064.2  •  sd: 68.52  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
51 Princeton Loss 7-10 993.64 Feb 18th Blue Hen Open
224 Drexel Win 13-4 1128.33 Feb 18th Blue Hen Open
197 Villanova Win 10-8 940.53 Feb 18th Blue Hen Open
133 Virginia Commonwealth Win 10-5 1581.77 Feb 18th Blue Hen Open
85 Delaware Loss 10-11 1125.14 Feb 19th Blue Hen Open
224 Drexel Win 11-5 1128.33 Feb 19th Blue Hen Open
133 Virginia Commonwealth Loss 8-10 745.21 Feb 19th Blue Hen Open
108 Syracuse Win 9-8 1242.12 Mar 4th No Sleep Till Brooklyn 2023
237 Hofstra Win 13-6 1057.35 Mar 4th No Sleep Till Brooklyn 2023
277 Cornell-B** Win 13-2 765.07 Ignored Mar 4th No Sleep Till Brooklyn 2023
84 Bates Loss 9-10 1126.75 Mar 5th No Sleep Till Brooklyn 2023
80 MIT Loss 5-11 678.13 Mar 5th No Sleep Till Brooklyn 2023
**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)