#286 Oberlin-B (1-5)

avg: -741.57  •  sd: 326.21  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
245 Ohio State-B** Loss 1-13 -414.17 Ignored Mar 2nd 2nd Annual 7th Annual Bens Bar Mitzvah
284 Miami (Ohio) Win 6-4 -227.14 Mar 2nd 2nd Annual 7th Annual Bens Bar Mitzvah
140 Cincinnati** Loss 0-11 312.84 Ignored Mar 2nd 2nd Annual 7th Annual Bens Bar Mitzvah
85 Dayton** Loss 0-13 643.31 Ignored Mar 2nd 2nd Annual 7th Annual Bens Bar Mitzvah
228 Xavier** Loss 3-13 -266.51 Ignored Mar 3rd 2nd Annual 7th Annual Bens Bar Mitzvah
284 Miami (Ohio) Loss 4-9 -1192.75 Mar 3rd 2nd Annual 7th Annual Bens Bar Mitzvah
**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)