#133 Holy City Heathens (5-18)

avg: 606.4  •  sd: 49.83  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
140 ScooberDivers Win 13-6 1123.04 Jul 7th Swan Boat 2018
44 El Niño Loss 7-8 1120 Jul 7th Swan Boat 2018
128 Vicious Cycle Loss 9-10 523.1 Jul 7th Swan Boat 2018
- Shore Break Win 13-7 694.1 Jul 7th Swan Boat 2018
137 Space Coast Ultimate Loss 11-13 336.13 Jul 8th Swan Boat 2018
108 Swamp Horse Loss 8-15 206.64 Jul 8th Swan Boat 2018
66 Bullet Loss 8-13 545.43 Jul 21st Club Terminus 2018
61 Tanasi Loss 2-13 495.92 Jul 21st Club Terminus 2018
97 Rush Hour Loss 5-10 277.28 Jul 22nd Club Terminus 2018
165 War Machine Win 11-4 703.5 Jul 22nd Club Terminus 2018
101 Memphis Belle Loss 7-13 272.34 Jul 22nd Club Terminus 2018
55 Ironmen Loss 8-13 656.06 Jul 22nd Club Terminus 2018
107 BaNC Loss 9-12 430.27 Aug 25th Rush Hour Round Robin 2018
98 Southern Hospitality Loss 9-12 503.68 Aug 25th Rush Hour Round Robin 2018
34 Lost Boys Loss 6-11 762.5 Aug 25th Rush Hour Round Robin 2018
97 Rush Hour Loss 5-9 322.12 Aug 26th Rush Hour Round Robin 2018
66 Bullet Loss 8-13 545.43 Aug 26th Rush Hour Round Robin 2018
102 H.O.G. Ultimate Loss 10-11 702.51 Aug 26th Rush Hour Round Robin 2018
23 Freaks** Loss 5-13 901.12 Ignored Sep 8th East Coast Mens Sectional Championship 2018
34 Lost Boys Loss 6-13 709.19 Sep 8th East Coast Mens Sectional Championship 2018
61 Tanasi Win 10-9 1220.92 Sep 8th East Coast Mens Sectional Championship 2018
102 H.O.G. Ultimate Loss 6-12 248.2 Sep 8th East Coast Mens Sectional Championship 2018
160 Duel Win 11-2 825.03 Sep 9th East Coast 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)