#47 ROBOS (19-6)

avg: 1387.34  •  sd: 85.04  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
52 Instant Karma Win 14-12 1566.01 Aug 4th Coconino Classic 2018
170 Spoiler Alert** Win 15-3 1358.52 Ignored Aug 4th Coconino Classic 2018
48 Rubix Win 14-12 1599.79 Aug 4th Coconino Classic 2018
62 Long Beach Legacy Win 14-8 1827.83 Aug 5th Coconino Classic 2018
109 Superstition Win 10-9 1197.46 Aug 5th Coconino Classic 2018
199 Wasatch Sasquatch** Win 13-4 1178.35 Ignored Aug 18th Ski Town Classic 2018
74 Alchemy Loss 7-13 707.66 Aug 18th Ski Town Classic 2018
143 Bulleit Train Win 10-6 1387.76 Aug 18th Ski Town Classic 2018
110 California Burrito Win 12-9 1412.45 Aug 18th Ski Town Classic 2018
52 Instant Karma Win 11-9 1594.26 Aug 19th Ski Town Classic 2018
51 Cutthroat Win 11-9 1606.89 Aug 19th Ski Town Classic 2018
44 Bozos Loss 5-13 860.67 Aug 19th Ski Town Classic 2018
52 Instant Karma Loss 9-13 926.49 Sep 8th So Cal Mixed Sectional Championship 2018
170 Spoiler Alert Win 13-7 1316.06 Sep 8th So Cal Mixed Sectional Championship 2018
48 Rubix Win 10-9 1503.83 Sep 8th So Cal Mixed Sectional Championship 2018
65 Family Style Win 4-3 1412.22 Sep 9th So Cal Mixed Sectional Championship 2018
162 Fear and Loathing Win 13-4 1397.89 Sep 9th So Cal Mixed Sectional Championship 2018
109 Superstition Win 12-10 1310.58 Sep 9th So Cal Mixed Sectional Championship 2018
71 Robot Win 10-7 1664.57 Sep 9th So Cal Mixed Sectional Championship 2018
55 American Barbecue Win 13-10 1652.43 Sep 22nd Southwest Mixed Regional Championship 2018
61 Donuts Loss 6-13 692.12 Sep 22nd Southwest Mixed Regional Championship 2018
17 Polar Bears Loss 11-13 1510.84 Sep 22nd Southwest Mixed Regional Championship 2018
52 Instant Karma Win 13-5 1945.05 Sep 23rd Southwest Mixed Regional Championship 2018
48 Rubix Win 13-9 1797.4 Sep 23rd Southwest Mixed Regional Championship 2018
71 Robot Loss 11-13 1046.06 Sep 23rd Southwest Mixed Regional Championship 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)