#345 Cornell-B (8-7)

avg: 534.1  •  sd: 88.47  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
213 Ithaca Loss 7-10 685.97 Mar 23rd King of New York 2024
284 Marist Win 10-9 934.2 Mar 24th King of New York 2024
181 SUNY-Cortland Loss 7-8 1068.21 Mar 24th King of New York 2024
327 SUNY-Binghamton-B Win 7-4 1110.37 Mar 24th King of New York 2024
404 American-B Win 13-5 634.29 Mar 30th Atlantic Coast Open 2024
415 St Mary's (Maryland)** Win 13-1 92.91 Ignored Mar 30th Atlantic Coast Open 2024
392 Richmond-B Win 13-4 767.68 Mar 30th Atlantic Coast Open 2024
311 Maryland-B Loss 5-8 220.95 Mar 30th Atlantic Coast Open 2024
392 Richmond-B Win 12-10 405.8 Mar 31st Atlantic Coast Open 2024
311 Maryland-B Loss 4-7 178.39 Mar 31st Atlantic Coast Open 2024
378 SUNY-Buffalo-B Win 10-9 426.69 Apr 13th Metro East Dev Mens Conferences 2024
327 SUNY-Binghamton-B Loss 4-9 14.21 Apr 13th Metro East Dev Mens Conferences 2024
343 Connecticut-B Loss 8-10 286.66 Apr 14th Metro East Dev Mens Conferences 2024
397 SUNY-Albany-B Win 11-3 692.79 Apr 14th Metro East Dev Mens Conferences 2024
331 Rutgers-B Loss 8-11 230.67 Apr 14th Metro East Dev 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)