#108 North Carolina-Charlotte (9-8)

avg: 1325.07  •  sd: 62.75  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
36 Alabama Loss 7-12 1202.62 Feb 9th Queen City Tune Up 2019 Men
57 Carnegie Mellon Loss 6-12 1008.07 Feb 9th Queen City Tune Up 2019 Men
52 Notre Dame Loss 10-13 1298.53 Feb 9th Queen City Tune Up 2019 Men
11 North Carolina State Loss 7-13 1470.04 Feb 9th Queen City Tune Up 2019 Men
131 Chicago Win 15-13 1480.67 Feb 10th Queen City Tune Up 2019 Men
61 Tennessee Loss 6-15 954.19 Feb 10th Queen City Tune Up 2019 Men
373 Edinboro** Win 13-3 960.23 Ignored Feb 23rd Chucktown Throwdown XVI
256 Georgia-B Win 11-3 1431.15 Feb 23rd Chucktown Throwdown XVI
330 Wingate** Win 10-4 1161.08 Ignored Feb 23rd Chucktown Throwdown XVI
94 Appalachian State Win 12-9 1717.8 Feb 24th Chucktown Throwdown XVI
256 Georgia-B Win 13-5 1431.15 Feb 24th Chucktown Throwdown XVI
33 Johns Hopkins Loss 8-13 1235.01 Mar 16th Oak Creek Invite 2019
66 Penn State Loss 8-11 1169.63 Mar 16th Oak Creek Invite 2019
54 Virginia Tech Loss 3-13 1019.44 Mar 16th Oak Creek Invite 2019
204 SUNY-Buffalo Win 11-10 1096.8 Mar 16th Oak Creek Invite 2019
120 James Madison Win 15-5 1882.8 Mar 17th Oak Creek Invite 2019
110 Williams Win 12-11 1440.82 Mar 17th Oak Creek Invite 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)