#193 Heavy Flow (7-20)

avg: 661.14  •  sd: 44.71  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
165 Possum Loss 9-11 556.31 Jun 22nd Summer Glazed Daze 2019
259 ThunderCats Win 13-5 904.02 Jun 22nd Summer Glazed Daze 2019
110 Rat City Loss 9-11 825.09 Jun 22nd Summer Glazed Daze 2019
116 Seoulmates Loss 8-11 691.31 Jun 23rd Summer Glazed Daze 2019
273 Rampage Win 13-6 832.02 Jun 23rd Summer Glazed Daze 2019
136 Crucible Loss 8-13 435.35 Jun 23rd Summer Glazed Daze 2019
236 RnB Win 13-5 1033.03 Jun 23rd Summer Glazed Daze 2019
87 Fleet Loss 7-15 563.71 Jul 13th Philly Invite 2019
103 Soft Boiled Loss 8-12 669.59 Jul 13th Philly Invite 2019
15 Loco** Loss 4-15 1156.06 Ignored Jul 13th Philly Invite 2019
49 League of Shadows** Loss 4-15 837.71 Ignored Jul 13th Philly Invite 2019
179 Unlimited Swipes Win 14-12 944.95 Jul 14th Philly Invite 2019
92 The Bandits Loss 10-14 735.65 Jul 14th Philly Invite 2019
77 Ant Madness Loss 4-13 621.61 Aug 10th Chesapeake Open 2019
139 Tequila Mockingbird Loss 3-10 308.67 Aug 10th Chesapeake Open 2019
89 HVAC Loss 6-13 552.46 Aug 10th Chesapeake Open 2019
110 Rat City Loss 8-12 633.14 Aug 10th Chesapeake Open 2019
87 Fleet Loss 8-12 722.56 Aug 11th Chesapeake Open 2019
136 Crucible Loss 6-15 331.51 Aug 11th Chesapeake Open 2019
89 HVAC Loss 8-13 656.3 Sep 7th Capital Mixed Club Sectional Championship 2019
102 Tyrannis Win 12-11 1237.17 Sep 7th Capital Mixed Club Sectional Championship 2019
221 District Cocktails Win 11-10 668.74 Sep 7th Capital Mixed Club Sectional Championship 2019
280 Nottoway Flatball Club Win 13-4 743.38 Sep 7th Capital Mixed Club Sectional Championship 2019
87 Fleet Loss 7-15 563.71 Sep 8th Capital Mixed Club Sectional Championship 2019
106 Fireball Loss 6-15 495.65 Sep 8th Capital Mixed Club Sectional Championship 2019
128 Legion Loss 6-15 391.65 Sep 8th Capital Mixed Club Sectional Championship 2019
102 Tyrannis Loss 8-13 616.01 Sep 8th Capital Mixed 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)