#150 Igneous Ultimate (7-11)

avg: 925.05  •  sd: 47.04  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
138 Choco Ghost House Loss 10-15 541.15 Jun 29th Rose City Rumble 2019
127 Hive Loss 11-12 922.23 Jun 29th Rose City Rumble 2019
64 The Administrators Loss 9-11 1095.75 Jun 29th Rose City Rumble 2019
232 Natural Twenties Win 15-7 1091.54 Jun 29th Rose City Rumble 2019
152 Fable Win 13-10 1250.16 Aug 3rd Kleinman Eruption 2019
36 Garage Sale** Loss 6-15 963.38 Ignored Aug 3rd Kleinman Eruption 2019
127 Hive Loss 10-14 648.53 Aug 3rd Kleinman Eruption 2019
234 Midnight Whiskey Win 15-8 1054.38 Aug 3rd Kleinman Eruption 2019
161 Breakers Mark Win 10-8 1155.38 Aug 4th Kleinman Eruption 2019
87 Garbage Loss 8-13 709.21 Aug 4th Kleinman Eruption 2019
95 Platypi Loss 9-10 1053.4 Aug 4th Kleinman Eruption 2019
161 Breakers Mark Loss 8-10 630.05 Sep 7th Oregon Mixed Club Sectional Championship 2019
222 Eugene Skyfall Win 13-3 1198.53 Sep 7th Oregon Mixed Club Sectional Championship 2019
36 Garage Sale Loss 5-10 989.48 Sep 7th Oregon Mixed Club Sectional Championship 2019
77 Happy Hour Loss 8-11 903.75 Sep 7th Oregon Mixed Club Sectional Championship 2019
222 Eugene Skyfall Win 16-14 806.82 Sep 8th Oregon Mixed Club Sectional Championship 2019
127 Hive Loss 10-12 809.11 Sep 8th Oregon Mixed Club Sectional Championship 2019
272 SkyLab** Win 15-2 875.16 Ignored Sep 8th Oregon 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)