#106 Lehigh (5-7)

avg: 1079.64  •  sd: 86.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
163 SUNY-Binghamton Loss 4-5 657.43 Mar 23rd Jersey Devil 8
134 Syracuse Win 8-7 1094.92 Mar 23rd Jersey Devil 8
92 Skidmore Loss 7-8 1063.27 Mar 23rd Jersey Devil 8
60 Ottawa Loss 0-4 840.6 Mar 23rd Jersey Devil 8
165 Temple Loss 6-7 652.8 Mar 24th Jersey Devil 8
241 Cornell-B Win 10-5 802.34 Mar 24th Jersey Devil 8
231 Pennsylvania-B** Win 11-2 917.18 Ignored Mar 24th Jersey Devil 8
134 Syracuse Win 12-6 1549.24 Mar 30th Garden State 9
53 Mount Holyoke Loss 7-8 1377.72 Mar 30th Garden State 9
66 Wesleyan Loss 4-13 793.48 Mar 30th Garden State 9
159 SUNY-Albany Win 11-5 1424.44 Mar 31st Garden State 9
63 New Hampshire Loss 9-10 1289.87 Mar 31st Garden State 9
**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)