#169 Rutgers (6-20)

avg: 951.64  •  sd: 51.93  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
196 NYU Win 9-7 1120.53 Feb 5th New Jersey Warmup
123 Pennsylvania Win 12-7 1667.99 Feb 10th New Jersey Warmup
107 Princeton Loss 9-11 959.4 Feb 10th New Jersey Warmup
113 Syracuse Loss 10-13 860.69 Feb 10th New Jersey Warmup
167 Columbia Loss 11-14 644.91 Feb 11th New Jersey Warmup
196 NYU Win 12-10 1079.31 Feb 11th New Jersey Warmup
126 Lehigh Loss 11-14 832.06 Feb 11th New Jersey Warmup
57 Auburn Loss 7-9 1167.86 Feb 24th Easterns Qualifier 2024
74 Cincinnati Loss 7-13 803.66 Feb 24th Easterns Qualifier 2024
56 Emory Loss 6-11 900.88 Feb 24th Easterns Qualifier 2024
68 James Madison Loss 6-9 958.33 Feb 24th Easterns Qualifier 2024
28 North Carolina-Wilmington** Loss 5-13 1134.5 Ignored Feb 25th Easterns Qualifier 2024
126 Lehigh Loss 7-15 545.39 Feb 25th Easterns Qualifier 2024
111 SUNY-Binghamton Loss 5-8 738.12 Feb 25th Easterns Qualifier 2024
85 Carnegie Mellon Loss 8-11 952.72 Mar 2nd Oak Creek Challenge 2024
175 Maryland-Baltimore County Loss 9-10 803 Mar 2nd Oak Creek Challenge 2024
152 West Chester Win 8-7 1151.57 Mar 2nd Oak Creek Challenge 2024
84 Appalachian State Loss 10-11 1201.8 Mar 3rd Oak Creek Challenge 2024
85 Carnegie Mellon Loss 7-9 1038.99 Mar 3rd Oak Creek Challenge 2024
165 RIT Loss 6-13 365.29 Mar 3rd Oak Creek Challenge 2024
70 Case Western Reserve Loss 6-13 766.71 Mar 30th East Coast Invite 2024
98 Dartmouth Loss 8-9 1120.64 Mar 30th East Coast Invite 2024
184 George Mason Win 8-6 1176.54 Mar 30th East Coast Invite 2024
107 Princeton Loss 9-10 1083.6 Mar 30th East Coast Invite 2024
154 Harvard Win 11-9 1272.39 Mar 31st East Coast Invite 2024
123 Pennsylvania Loss 6-11 600.79 Mar 31st East Coast Invite 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)