#210 Rochester (6-8)

avg: 953.29  •  sd: 55.6  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
391 John Carroll** Win 13-2 872.41 Ignored Mar 23rd CWRUL Memorial 2019
286 Toledo Win 9-6 1141.5 Mar 23rd CWRUL Memorial 2019
355 Northwestern-B Win 10-5 1032.8 Mar 23rd CWRUL Memorial 2019
158 Lehigh Loss 8-9 1004.08 Mar 23rd CWRUL Memorial 2019
171 RIT Loss 11-12 956.65 Mar 24th CWRUL Memorial 2019
64 Ohio Loss 8-15 974.59 Mar 24th CWRUL Memorial 2019
145 Dayton Loss 8-14 653.64 Mar 24th CWRUL Memorial 2019
247 Xavier Win 8-5 1328.35 Mar 24th CWRUL Memorial 2019
163 SUNY-Geneseo Loss 8-13 610.42 Mar 27th Fracas on the Genesee 2019
223 Rensselaer Polytech Loss 8-9 791.61 Mar 30th Uprising 8
193 Colgate Win 11-9 1261.04 Mar 30th Uprising 8
110 Williams Loss 7-9 1036.49 Mar 30th Uprising 8
268 Ithaca Win 13-11 1016.25 Mar 31st Uprising 8
190 Maine Loss 9-12 679.69 Mar 31st Uprising 8
**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)