#130 m'kay Ultimate (11-9)

avg: 1032.15  •  sd: 53.37  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
162 OutKast Win 10-4 1472.91 Jun 15th ATL Classic 2019
252 Big Bend Win 13-6 1012.1 Jun 15th ATL Classic 2019
105 Auburn HeyDay Win 11-10 1283.12 Jun 15th ATL Classic 2019
74 Trash Pandas Loss 11-12 1148.88 Jun 15th ATL Classic 2019
164 BATL Cows Win 7-3 1456.2 Jun 16th ATL Classic 2019
255 Mixchief Win 13-8 897 Jun 16th ATL Classic 2019
47 Huntsville Outlaws Loss 10-11 1348.42 Jun 16th ATL Classic 2019
230 The Umbrella Win 13-6 1113.47 Jul 20th 2019 Club Terminus
70 Memphis STAX Loss 9-13 882.96 Jul 20th 2019 Club Terminus
16 Weird** Loss 5-13 1196.8 Ignored Jul 20th 2019 Club Terminus
174 Magic City Mayhem Win 9-6 1204.73 Jul 20th 2019 Club Terminus
164 BATL Cows Win 13-9 1274.77 Jul 21st 2019 Club Terminus
16 Weird** Loss 5-13 1196.8 Ignored Jul 21st 2019 Club Terminus
70 Memphis STAX Loss 2-9 701.52 Jul 21st 2019 Club Terminus
165 APEX Loss 10-11 720.11 Sep 7th East Coast Mixed Club Sectional Championship 2019
225 Monster Win 13-8 1079.06 Sep 7th East Coast Mixed Club Sectional Championship 2019
62 JLP Loss 8-12 909.85 Sep 7th East Coast Mixed Club Sectional Championship 2019
230 The Umbrella Win 13-4 1113.47 Sep 7th East Coast Mixed Club Sectional Championship 2019
162 OutKast Loss 9-11 623.7 Sep 8th East Coast Mixed Club Sectional Championship 2019
167 Possum Win 13-11 1064.94 Sep 8th East Coast 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)