#226 Baywatch (2-12)

avg: 346.12  •  sd: 78.86  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
212 Mixed Results Loss 6-7 389.05 Jul 7th Huckfest 2018
177 OutKast Loss 3-13 97.14 Jul 7th Huckfest 2018
115 STAX Loss 7-13 479.9 Jul 7th Huckfest 2018
151 LoveShack Loss 4-13 256.43 Jul 7th Huckfest 2018
188 Hairy Otter Loss 7-13 83.73 Jul 8th Huckfest 2018
213 Heartbreakers Loss 6-8 198.49 Jul 8th Huckfest 2018
240 Memphis Hustle & Flow Win 13-3 744.49 Jul 8th Huckfest 2018
238 Strictly Bidness Win 13-9 605.98 Jul 8th Huckfest 2018
38 Columbus Cocktails** Loss 3-13 900.3 Ignored Jul 21st Club Terminus 2018
41 Storm** Loss 3-13 879.16 Ignored Jul 21st Club Terminus 2018
56 Murmur** Loss 4-13 721.17 Ignored Jul 22nd Club Terminus 2018
90 Mutiny** Loss 5-13 565.68 Ignored Jul 22nd Club Terminus 2018
54 JLP** Loss 4-13 725.88 Ignored Jul 22nd Club Terminus 2018
151 LoveShack Loss 2-13 256.43 Jul 22nd Club Terminus 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)