#113 Syracuse (13-10)

avg: 1188.84  •  sd: 43.46  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
126 Lehigh Win 13-9 1563.96 Feb 5th New Jersey Warmup
123 Pennsylvania Win 13-9 1566.05 Feb 10th New Jersey Warmup
107 Princeton Loss 9-10 1083.6 Feb 10th New Jersey Warmup
169 Rutgers Win 13-10 1279.78 Feb 10th New Jersey Warmup
196 NYU Win 12-7 1361.7 Feb 11th New Jersey Warmup
107 Princeton Win 11-10 1333.6 Feb 11th New Jersey Warmup
60 Temple Loss 10-14 1036.32 Feb 11th New Jersey Warmup
142 Boston University Win 10-8 1331.38 Mar 2nd No Sleep till Brooklyn 2024
198 Delaware Win 11-6 1381.37 Mar 2nd No Sleep till Brooklyn 2024
31 Middlebury Loss 1-13 1057.12 Mar 2nd No Sleep till Brooklyn 2024
142 Boston University Win 13-8 1564.87 Mar 3rd No Sleep till Brooklyn 2024
167 Columbia Win 11-8 1323.86 Mar 3rd No Sleep till Brooklyn 2024
46 Williams Loss 8-12 1083.81 Mar 3rd No Sleep till Brooklyn 2024
236 MIT Win 12-11 805.57 Mar 23rd Carousel City Classic 2024
32 Ottawa Loss 4-13 1053.66 Mar 23rd Carousel City Classic 2024
148 Rochester Win 13-12 1161.81 Mar 23rd Carousel City Classic 2024
111 SUNY-Binghamton Loss 10-11 1066.72 Mar 24th Carousel City Classic 2024
96 Connecticut Loss 10-11 1124.4 Mar 30th East Coast Invite 2024
25 McGill Loss 4-13 1174.26 Mar 30th East Coast Invite 2024
150 Navy Loss 10-11 909.29 Mar 30th East Coast Invite 2024
278 SUNY-Stony Brook** Win 13-5 1097.13 Ignored Mar 30th East Coast Invite 2024
70 Case Western Reserve Loss 8-12 925.56 Mar 31st East Coast Invite 2024
150 Navy Win 12-9 1379.66 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)