#224 Mud Turtles (9-12)

avg: 527.86  •  sd: 85.76  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
143 Impact Win 11-6 1441.65 Jun 15th Texas Two Finger 2019
113 blOKC party Loss 2-11 466.68 Jun 15th Texas Two Finger 2019
214 Chili Poppers Win 11-3 1180.52 Jun 15th Texas Two Finger 2019
14 Public Enemy** Loss 2-11 1159.91 Ignored Jun 15th Texas Two Finger 2019
262 Balloon Win 15-8 842.95 Jul 13th Riverside Classic 2019
292 Mixfits** Win 15-1 432.33 Ignored Jul 13th Riverside Classic 2019
129 Moontower Loss 2-15 388.19 Jul 13th Riverside Classic 2019
183 Wildstyle Loss 8-11 346.73 Jul 14th Riverside Classic 2019
292 Mixfits** Win 15-3 432.33 Ignored Jul 14th Riverside Classic 2019
262 Balloon Win 15-5 878.14 Jul 14th Riverside Classic 2019
45 Waterloo** Loss 3-15 847.57 Ignored Jul 27th PBJ 2019
126 Risky Business Loss 4-15 410.25 Jul 27th PBJ 2019
230 Discney Loss 11-13 231.93 Jul 27th PBJ 2019
129 Moontower Loss 4-15 388.19 Jul 28th PBJ 2019
214 Chili Poppers Loss 10-15 126.92 Jul 28th PBJ 2019
262 Balloon Win 12-11 403.14 Jul 28th PBJ 2019
45 Waterloo** Loss 2-13 847.57 Ignored Sep 7th Texas Mixed Club Sectional Championship 2019
183 Wildstyle Loss 3-12 112.34 Sep 7th Texas Mixed Club Sectional Championship 2019
270 Boomshakalaka Win 13-3 842.71 Sep 7th Texas Mixed Club Sectional Championship 2019
229 Tlacuaches Loss 5-11 -133.35 Sep 8th Texas Mixed Club Sectional Championship 2019
230 Discney Win 13-5 1060.77 Sep 8th Texas 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)