#174 Daybreak (1-17)

avg: 635.48  •  sd: 50.13  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
56 Ghost Train Loss 9-15 805 Jul 13th TCT Select Flight Invite West 2019
46 Haymaker Loss 10-15 943.03 Jul 13th TCT Select Flight Invite West 2019
69 Streetgang Loss 10-15 780.87 Jul 13th TCT Select Flight Invite West 2019
80 ISO Atmo Loss 0-13 546.91 Jul 14th TCT Select Flight Invite West 2019
50 UpRoar Loss 10-13 1032.33 Jul 14th TCT Select Flight Invite West 2019
68 Sawtooth Loss 11-12 1111.41 Jul 14th TCT Select Flight Invite West 2019
61 Battery** Loss 1-13 679.3 Ignored Aug 24th Ski Town Classic 2019
168 Sandbaggers Loss 10-13 346.07 Aug 24th Ski Town Classic 2019
80 ISO Atmo Loss 4-13 546.91 Aug 24th Ski Town Classic 2019
90 PowderHogs Loss 2-13 492.8 Aug 24th Ski Town Classic 2019
158 Cojones Win 8-6 1043.03 Aug 25th Ski Town Classic 2019
55 OAT Loss 7-13 764.2 Aug 25th Ski Town Classic 2019
167 OC Crows Loss 6-9 256.14 Aug 25th Ski Town Classic 2019
161 Boulder United Flatiron Hammers Loss 10-13 387.16 Sep 7th Rocky Mountain Mens Club Sectional Championship 2019
148 Syndicate Loss 11-13 563.8 Sep 7th Rocky Mountain Mens Club Sectional Championship 2019
41 Inception** Loss 4-13 820.45 Ignored Sep 7th Rocky Mountain Mens Club Sectional Championship 2019
113 Choice City Hops Loss 8-13 495.74 Sep 8th Rocky Mountain Mens Club Sectional Championship 2019
80 ISO Atmo Loss 4-13 546.91 Sep 8th Rocky Mountain 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)