#393 Susquehanna (4-6)

avg: 267.6  •  sd: 106.69  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
422 Saint Joseph's Win 11-5 667.09 Mar 23rd Spring Awakening 8
389 Cornell-B Win 9-4 874.9 Mar 23rd Spring Awakening 8
375 Vermont-B Loss 2-9 -245.25 Mar 23rd Spring Awakening 8
223 Rensselaer Polytech** Loss 4-13 316.61 Ignored Mar 24th Spring Awakening 8
281 Skidmore Loss 6-12 170.29 Mar 24th Spring Awakening 8
239 Slippery Rock** Loss 5-13 296.36 Mar 30th Garden State 9
435 Lehigh-B Win 13-6 389.69 Mar 30th Garden State 9
416 Temple-B Loss 9-11 -124.3 Mar 30th Garden State 9
266 Penn State-B Loss 10-13 470.19 Mar 31st Garden State 9
435 Lehigh-B Win 7-6 -85.31 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)