#300 SUNY-Stony Brook (2-15)

avg: 714.83  •  sd: 74.97  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
80 Bates** Loss 2-12 990.96 Ignored Mar 2nd No Sleep till Brooklyn 2024
191 NYU Loss 3-13 556.8 Mar 2nd No Sleep till Brooklyn 2024
55 Williams** Loss 1-13 1149.57 Ignored Mar 2nd No Sleep till Brooklyn 2024
283 Hofstra Win 10-7 1198.94 Mar 3rd No Sleep till Brooklyn 2024
201 MIT Loss 6-10 620.55 Mar 3rd No Sleep till Brooklyn 2024
102 Connecticut** Loss 4-11 895.12 Ignored Mar 30th East Coast Invite 2024
26 McGill** Loss 3-13 1397.12 Ignored Mar 30th East Coast Invite 2024
176 Navy Loss 7-11 745.53 Mar 30th East Coast Invite 2024
120 Syracuse** Loss 5-13 803.26 Ignored Mar 30th East Coast Invite 2024
163 Columbia Loss 8-9 1150.42 Mar 31st East Coast Invite 2024
202 George Mason Loss 3-12 516.33 Mar 31st East Coast Invite 2024
283 Hofstra Loss 6-10 313.11 Apr 13th Metro NY D I Mens Conferences 2024
191 NYU Loss 6-10 660.65 Apr 13th Metro NY D I Mens Conferences 2024
93 Princeton Loss 5-10 964.13 Apr 13th Metro NY D I Mens Conferences 2024
283 Hofstra Loss 8-11 443.66 Apr 14th Metro NY D I Mens Conferences 2024
374 New Jersey Tech Win 12-5 923.7 Apr 14th Metro NY D I Mens Conferences 2024
272 Rowan Loss 9-11 607.58 Apr 14th Metro NY D I Mens Conferences 2024
**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)