#153 Rawhide (2-18)

avg: 308.14  •  sd: 51.42  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
82 Riverside** Loss 4-13 344.4 Ignored Jun 30th Texas Two Finger 2018
141 DUPlex Loss 11-13 288.18 Jun 30th Texas Two Finger 2018
43 Clutch** Loss 5-13 654.73 Ignored Jun 30th Texas Two Finger 2018
166 Surrillic Audovice Win 13-10 410.9 Jul 1st Texas Two Finger 2018
- Texas Two Step Loss 10-13 129.21 Jul 1st Texas Two Finger 2018
150 The Bayou Boys Loss 6-13 -258.75 Jul 1st Texas Two Finger 2018
95 Scythe Loss 6-11 332.37 Jul 21st The Royal Experience 18
110 Dreadnought Loss 9-11 520.18 Jul 21st The Royal Experience 18
59 Mallard** Loss 3-11 498.06 Ignored Jul 21st The Royal Experience 18
60 DeMo Loss 6-11 549.88 Jul 21st The Royal Experience 18
130 Syndicate Loss 9-11 381.77 Jul 21st The Royal Experience 18
110 Dreadnought Loss 4-13 169.38 Jul 22nd The Royal Experience 18
- Confluence Win 15-13 406.38 Jul 22nd The Royal Experience 18
110 Dreadnought Loss 5-13 169.38 Aug 11th Hootie on the Hill 2018
101 Memphis Belle Loss 5-13 229.87 Aug 11th Hootie on the Hill 2018
83 Supercell** Loss 4-13 340.63 Ignored Aug 11th Hootie on the Hill 2018
101 Memphis Belle Loss 8-15 265.06 Aug 12th Hootie on the Hill 2018
83 Supercell Loss 10-15 487.03 Aug 12th Hootie on the Hill 2018
110 Dreadnought Loss 7-15 169.38 Sep 8th Ozarks Mens Sectional Championship 2018
83 Supercell Loss 11-15 559.47 Sep 8th Ozarks Mens Sectional Championship 2018
**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)