#49 The Killjoys (18-7)

avg: 1384.64  •  sd: 55.77  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
70 Sundowners Win 13-5 1828.1 Jun 15th San Diego Slammer 2019
55 OAT Loss 10-13 993.59 Jun 15th San Diego Slammer 2019
69 Streetgang Win 13-11 1463.32 Jun 15th San Diego Slammer 2019
70 Sundowners Loss 9-13 809.54 Jun 16th San Diego Slammer 2019
167 OC Crows Win 13-7 1232.24 Jun 16th San Diego Slammer 2019
69 Streetgang Win 13-12 1359.48 Jun 16th San Diego Slammer 2019
70 Sundowners Win 13-11 1456.94 Aug 24th Ski Town Classic 2019
167 OC Crows** Win 13-3 1274.71 Ignored Aug 24th Ski Town Classic 2019
158 Cojones** Win 13-5 1342.54 Ignored Aug 24th Ski Town Classic 2019
68 Sawtooth Win 13-8 1732.57 Aug 24th Ski Town Classic 2019
113 Choice City Hops Win 13-10 1320.04 Aug 25th Ski Town Classic 2019
80 ISO Atmo Loss 13-14 1021.91 Aug 25th Ski Town Classic 2019
61 Battery Win 11-10 1404.3 Aug 25th Ski Town Classic 2019
105 Low Point Win 13-8 1517.25 Sep 7th Big Sky Mens Club Sectional Championship 2019
68 Sawtooth Win 13-9 1654.98 Sep 7th Big Sky Mens Club Sectional Championship 2019
168 Sandbaggers Win 13-7 1231.75 Sep 7th Big Sky Mens Club Sectional Championship 2019
- Old Ephraim Win 13-7 1256.43 Sep 7th Big Sky Mens Club Sectional Championship 2019
90 PowderHogs Win 11-8 1458.41 Sep 8th Big Sky Mens Club Sectional Championship 2019
90 PowderHogs Win 15-14 1217.8 Sep 8th Big Sky Mens Club Sectional Championship 2019
13 Furious George Loss 11-12 1711.56 Sep 21st Northwest Club Mens Regional Championship 2019
77 SOUF Loss 10-11 1030.91 Sep 21st Northwest Club Mens Regional Championship 2019
15 Rhino Slam! Loss 11-13 1588.75 Sep 21st Northwest Club Mens Regional Championship 2019
100 Seattle Blacklist Win 13-5 1636.65 Sep 21st Northwest Club Mens Regional Championship 2019
68 Sawtooth Loss 14-15 1111.41 Sep 22nd Northwest Club Mens Regional Championship 2019
77 SOUF Win 1-0 1755.91 Sep 22nd Northwest Club Mens Regional 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)