#258 Cornell-B (6-4)

avg: 569.22  •  sd: 95.2  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
157 Ithaca Loss 7-10 626.94 Mar 23rd King of New York 2024
- Marist Win 10-9 605.48 Mar 24th King of New York 2024
147 SUNY-Cortland Loss 7-8 923.48 Mar 24th King of New York 2024
321 SUNY-Binghamton-B Win 7-4 751.85 Mar 24th King of New York 2024
369 American-B** Win 13-5 417.5 Ignored Mar 30th Atlantic Coast Open 2024
382 St Mary's (Maryland)** Win 13-1 -45.62 Ignored Mar 30th Atlantic Coast Open 2024
328 Richmond-B Win 13-4 785.68 Mar 30th Atlantic Coast Open 2024
239 Maryland-B Loss 5-8 221.18 Mar 30th Atlantic Coast Open 2024
328 Richmond-B Win 12-10 423.8 Mar 31st Atlantic Coast Open 2024
239 Maryland-B Loss 4-7 178.62 Mar 31st Atlantic Coast Open 2024
**Blowout Eligible

FAQ

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
  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
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)