#83 Rutgers (10-12)

avg: 1432.97  •  sd: 59.79  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
18 Michigan Loss 10-13 1580.62 Feb 8th Florida Warm Up 2019
65 Florida Win 11-9 1784.95 Feb 8th Florida Warm Up 2019
22 Georgia Loss 6-13 1234.49 Feb 8th Florida Warm Up 2019
25 South Carolina Loss 8-10 1524.02 Feb 9th Florida Warm Up 2019
17 Minnesota Loss 9-10 1826.05 Feb 9th Florida Warm Up 2019
12 Texas Loss 6-12 1430.59 Feb 9th Florida Warm Up 2019
72 Alabama-Huntsville Loss 11-15 1102.82 Feb 9th Florida Warm Up 2019
106 Illinois State Win 13-9 1745.9 Feb 10th Florida Warm Up 2019
49 Northwestern Loss 13-14 1512.69 Feb 10th Florida Warm Up 2019
338 Wake Forest** Win 13-0 1133.6 Ignored Feb 23rd Oak Creek Challenge 2019
114 Liberty Loss 9-10 1175.11 Feb 23rd Oak Creek Challenge 2019
345 American** Win 13-2 1101.81 Ignored Feb 23rd Oak Creek Challenge 2019
137 North Carolina-B Win 12-8 1674.31 Feb 23rd Oak Creek Challenge 2019
142 Princeton Loss 9-10 1084.71 Feb 24th Oak Creek Challenge 2019
206 West Chester Win 13-6 1566.25 Feb 24th Oak Creek Challenge 2019
174 Cedarville Win 15-11 1448.62 Feb 24th Oak Creek Challenge 2019
120 James Madison Loss 9-11 1033.6 Mar 30th Atlantic Coast Open 2019
33 Johns Hopkins Loss 8-13 1235.01 Mar 30th Atlantic Coast Open 2019
101 Connecticut Win 12-6 1935.55 Mar 30th Atlantic Coast Open 2019
91 Mary Washington Loss 8-11 1016.9 Mar 30th Atlantic Coast Open 2019
171 RIT Win 14-12 1302.61 Mar 31st Atlantic Coast Open 2019
158 Lehigh Win 13-7 1686.61 Mar 31st Atlantic Coast Open 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)