#88 PowderHogs (13-14)

avg: 908.48  •  sd: 56.47  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
- Flicky Stardust and the Cutters From Mars Win 12-7 817.53 Jun 22nd Eugene Summer Solstice 40
12 Rhino Slam** Loss 3-13 1179.67 Ignored Jun 23rd Eugene Summer Solstice 40
- Whitefish Win 13-6 941.91 Jun 23rd Eugene Summer Solstice 40
- Ham Loss 6-13 544.27 Jun 23rd Eugene Summer Solstice 40
78 Rip City Ultimate Loss 4-9 375.79 Jun 24th Eugene Summer Solstice 40
- Whitefish Win 13-7 899.44 Jun 24th Eugene Summer Solstice 40
99 Red Dawn Loss 11-12 718.71 Jun 24th Eugene Summer Solstice 40
78 Rip City Ultimate Win 11-10 1100.79 Jul 7th 2018 San Diego Slammer
- Carbon Win 11-10 913.33 Jul 7th 2018 San Diego Slammer
74 DOGGPOUND Loss 7-11 530.4 Jul 7th 2018 San Diego Slammer
91 Sprawl Win 11-8 1262.98 Jul 7th 2018 San Diego Slammer
40 Streetgang Loss 5-11 675.1 Jul 7th 2018 San Diego Slammer
86 Green River Swordfish Win 15-14 1047.11 Jul 8th 2018 San Diego Slammer
81 Sundowners Loss 9-13 529.15 Jul 8th 2018 San Diego Slammer
49 CaSTLe Loss 7-12 693.64 Jul 28th TCT Select Flight Invite 2018
38 Dark Star Loss 8-12 838.77 Jul 28th TCT Select Flight Invite 2018
19 Guerrilla Loss 6-13 947.89 Jul 28th TCT Select Flight Invite 2018
69 Gamble Loss 4-13 433.45 Jul 29th TCT Select Flight Invite 2018
78 Rip City Ultimate Win 12-9 1321.16 Aug 18th Ski Town Classic 2018
92 Choice City Hops Win 13-11 1116.83 Aug 18th Ski Town Classic 2018
144 Gridlock Win 13-6 1068.25 Aug 18th Ski Town Classic 2018
81 Sundowners Win 13-6 1547.72 Aug 18th Ski Town Classic 2018
43 Clutch Loss 10-13 926.58 Aug 19th Ski Town Classic 2018
81 Sundowners Win 11-10 1072.72 Aug 19th Ski Town Classic 2018
63 Sawtooth Win 13-10 1384.71 Sep 8th Big Sky Mens Sectional Championship 2018
- NCFO Loss 11-13 698.58 Sep 8th Big Sky Mens Sectional Championship 2018
89 The Killjoys Loss 9-12 557.72 Sep 8th Big Sky 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)