#157 Yale (7-3)

avg: 1125.16  •  sd: 142.71  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
150 George Washington Win 10-8 1410.91 Mar 4th Oak Creek Challenge 2023
248 Drexel Loss 4-6 378.94 Mar 4th Oak Creek Challenge 2023
67 Virginia Tech Win 8-7 1678.29 Mar 4th Oak Creek Challenge 2023
70 Lehigh Loss 4-13 926.73 Mar 5th Oak Creek Challenge 2023
124 Towson Win 7-6 1394.13 Mar 5th Oak Creek Challenge 2023
67 Virginia Tech Loss 2-13 953.29 Mar 5th Oak Creek Challenge 2023
- Central Connecticut State Win 7-3 1284.52 Mar 26th Late Blooming Bids
316 Rensselaer Polytech** Win 12-1 940.76 Ignored Mar 26th Late Blooming Bids
- Southern Connecticut State** Win 14-0 207.26 Ignored Mar 26th Late Blooming Bids
322 Vassar Win 9-4 903.68 Mar 26th Late Blooming Bids
**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)