#84 Autonomous (7-12)

avg: 295.43  •  sd: 68.03  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
90 Sureshot Win 15-6 781.56 Jun 22nd SCINNY 2019
85 Lady Forward Win 10-9 382.2 Jun 22nd SCINNY 2019
64 Notorious C.L.E. Loss 9-13 316.97 Jun 22nd SCINNY 2019
65 Eliza Furnace Loss 9-12 380.41 Jun 22nd SCINNY 2019
97 Belle Win 13-6 426.63 Jun 23rd SCINNY 2019
46 Indy Rogue Loss 7-15 500.16 Jun 23rd SCINNY 2019
75 Viva Loss 4-12 -38.94 Aug 3rd Heavyweights 2019
31 Fusion** Loss 1-12 783.04 Ignored Aug 3rd Heavyweights 2019
85 Lady Forward Win 10-7 646.86 Aug 3rd Heavyweights 2019
94 Inferno Win 10-6 585.7 Aug 4th Heavyweights 2019
106 Frenzy Win 12-4 600 Ignored Aug 4th Heavyweights 2019
90 Sureshot Loss 10-11 56.56 Aug 24th Indy Invite Club 2019
72 Helix Loss 9-10 508.8 Aug 24th Indy Invite Club 2019
63 Huntsville Laika Loss 1-13 165.09 Aug 24th Indy Invite Club 2019
85 Lady Forward Loss 8-10 -5.47 Aug 24th Indy Invite Club 2019
90 Sureshot Win 15-6 781.56 Aug 25th Indy Invite Club 2019
85 Lady Forward Loss 10-13 -70.94 Aug 25th Indy Invite Club 2019
64 Notorious C.L.E. Loss 5-15 135.54 Sep 7th East Plains Womens Club Sectional Championship 2019
90 Sureshot Loss 14-16 -26.73 Sep 7th East Plains Womens 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)