#104 Burly (8-4)

avg: 1025.93  •  sd: 84.75  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
241 defunCT** Win 15-4 438.58 Ignored Jul 6th AntlerLock 2019
225 Highlight Reel** Win 15-6 834.55 Ignored Jul 6th AntlerLock 2019
163 One Night Win 15-11 1092.3 Jul 6th AntlerLock 2019
94 Log Jam Win 14-10 1476.37 Jul 7th AntlerLock 2019
17 Sprout** Loss 6-15 1167.5 Ignored Jul 7th AntlerLock 2019
95 Red Tide Loss 11-15 695.91 Jul 7th AntlerLock 2019
165 Rising Tide U20B Win 13-8 1201.67 Jul 20th Vacationland 2019
53 Colt Loss 5-11 740.96 Jul 20th Vacationland 2019
66 Deathsquad Loss 8-13 746.16 Jul 20th Vacationland 2019
204 Spring Break '93 Win 11-6 978.98 Jul 20th Vacationland 2019
188 Thunder Boys Win 13-3 1134.21 Jul 21st Vacationland 2019
157 Ender's Outcasts Win 13-9 1161.22 Jul 21st Vacationland 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)