#156 Cojones (4-19)

avg: 742.9  •  sd: 56.51  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
15 Rhino Slam!** Loss 3-13 1253.54 Ignored Jun 22nd Eugene Summer Solstice 2019
166 Rip City Win 11-7 1151.03 Jun 22nd Eugene Summer Solstice 2019
40 Blackfish** Loss 4-13 812.76 Ignored Jun 22nd Eugene Summer Solstice 2019
53 Ghost Train Loss 4-13 710.85 Jun 22nd Eugene Summer Solstice 2019
121 Green River Swordfish Loss 7-15 340.19 Jun 23rd Eugene Summer Solstice 2019
110 Oregon Eruption! Loss 12-13 864 Jun 23rd Eugene Summer Solstice 2019
121 Green River Swordfish Loss 13-15 726.01 Aug 3rd CBR 2019
53 Ghost Train Loss 10-15 857.25 Aug 3rd CBR 2019
83 Seattle Blacklist Loss 9-15 610.54 Aug 3rd CBR 2019
40 Blackfish Loss 12-15 1112.27 Aug 4th CBR 2019
83 Seattle Blacklist Loss 8-14 589.99 Aug 4th CBR 2019
90 Choice City Hops Loss 9-13 685.03 Aug 24th Ski Town Classic 2019
165 OC Crows Win 12-10 933.53 Aug 24th Ski Town Classic 2019
46 The Killjoys** Loss 5-13 767.52 Ignored Aug 24th Ski Town Classic 2019
61 Sundowners Loss 5-13 668.07 Aug 24th Ski Town Classic 2019
72 Sawtooth Loss 6-13 588.67 Aug 25th Ski Town Classic 2019
176 Daybreak Loss 6-8 336.1 Aug 25th Ski Town Classic 2019
170 Sandbaggers Win 11-5 1261.73 Aug 25th Ski Town Classic 2019
127 Journeymen Loss 4-11 300.7 Sep 7th Nor Cal Mens Club Sectional Championship 2019
54 Battery Loss 4-11 707.49 Sep 7th Nor Cal Mens Club Sectional Championship 2019
62 OAT Loss 2-11 657.83 Sep 7th Nor Cal Mens Club Sectional Championship 2019
184 Arithmetic Ultimate Win 11-5 1159.87 Sep 7th Nor Cal Mens Club Sectional Championship 2019
145 Little Teapots Loss 11-12 663.5 Sep 8th Nor Cal Mens Club Sectional Championship 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)