#380 Case Western Reserve-B (2-8)

avg: 330.75  •  sd: 82.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
345 American Loss 6-12 -77.51 Mar 2nd Huckin in the Hills VI
356 West Virginia Loss 6-11 -97.92 Mar 2nd Huckin in the Hills VI
145 Dayton** Loss 3-13 589.68 Ignored Mar 2nd Huckin in the Hills VI
432 SUNY-Buffalo-B Win 13-10 231.43 Mar 2nd Huckin in the Hills VI
148 Michigan-B Loss 6-13 581.95 Mar 23rd CWRUL Memorial 2019
348 Western Michigan Win 12-10 725.69 Mar 23rd CWRUL Memorial 2019
174 Cedarville** Loss 1-13 467.46 Ignored Mar 23rd CWRUL Memorial 2019
320 Ohio State-B Loss 11-12 467.54 Mar 23rd CWRUL Memorial 2019
347 Wright State Loss 11-12 366.16 Mar 24th CWRUL Memorial 2019
303 Ohio Northern Loss 11-15 271.04 Mar 24th CWRUL Memorial 2019
**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)