#224 Hofstra (3-8)

avg: 696.95  •  sd: 115.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
167 Temple Loss 2-13 465.75 Feb 24th Bring the Huckus 9
244 College of New Jersey Win 13-11 733.08 Feb 24th Bring the Huckus 9
175 Ithaca Loss 3-13 411.89 Feb 24th Bring the Huckus 9
192 Ohio Wesleyan Win 7-5 1260.06 Mar 3rd Atlantic City 7 2018
199 Salisbury Loss 5-7 558.67 Mar 3rd Atlantic City 7 2018
129 Colby Win 6-4 1658.22 Mar 3rd Atlantic City 7 2018
202 Shippensburg Loss 6-9 461.88 Mar 4th Atlantic City 7 2018
201 SUNY-Fredonia Loss 4-7 384.79 Mar 4th Atlantic City 7 2018
99 Princeton** Loss 3-12 920.12 Ignored Mar 31st Garden State 8
152 SUNY-Geneseo Loss 3-13 568.7 Mar 31st Garden State 8
- Syracuse Loss 8-9 718.95 Mar 31st Garden State 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)