#71 Cornell (9-10)

avg: 1503.6  •  sd: 80.9  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
50 Case Western Reserve Loss 11-13 1411.17 Feb 25th Easterns Qualifier 2023
51 Virginia Loss 7-12 1114.95 Feb 25th Easterns Qualifier 2023
26 Georgia Tech Win 12-9 2213.7 Feb 25th Easterns Qualifier 2023
150 George Washington Win 12-10 1386.37 Feb 25th Easterns Qualifier 2023
27 South Carolina Loss 9-13 1429.61 Feb 26th Easterns Qualifier 2023
33 Duke Win 15-11 2171.83 Feb 26th Easterns Qualifier 2023
25 North Carolina-Wilmington Loss 5-11 1284.26 Feb 26th Easterns Qualifier 2023
134 Carnegie Mellon Win 13-6 1836.31 Mar 25th Carousel City Classic
50 Case Western Reserve Win 14-11 1953.35 Mar 25th Carousel City Classic
153 Columbia Win 15-7 1740.62 Mar 25th Carousel City Classic
82 Binghamton Win 11-10 1586.54 Mar 26th Carousel City Classic
31 Ottawa Loss 10-12 1591.57 Mar 26th Carousel City Classic
63 Rutgers Loss 10-13 1240.64 Mar 26th Carousel City Classic
106 Liberty Loss 10-11 1217.91 Apr 1st Atlantic Coast Open 2023
172 East Carolina Win 12-5 1663.43 Apr 1st Atlantic Coast Open 2023
45 Georgetown Loss 7-14 1113.8 Apr 1st Atlantic Coast Open 2023
77 Temple Loss 7-15 880.32 Apr 2nd Atlantic Coast Open 2023
101 Navy Loss 9-10 1251.49 Apr 2nd Atlantic Coast Open 2023
167 Virginia Commonwealth Win 13-7 1647.64 Apr 2nd Atlantic Coast Open 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)