#81 Lady Forward (8-18)

avg: 333.07  •  sd: 68.8  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
82 Autonomous Loss 9-10 196.37 Jun 22nd SCINNY 2019
41 Indy Rogue Loss 7-14 546.15 Jun 22nd SCINNY 2019
99 Belle Win 11-8 211.01 Jun 22nd SCINNY 2019
68 Eliza Furnace Loss 8-12 220.63 Jun 23rd SCINNY 2019
90 Sureshot Loss 9-14 -257.23 Jun 23rd SCINNY 2019
82 Autonomous Loss 7-10 -68.3 Aug 3rd Heavyweights 2019
84 Cold Cuts Loss 10-12 62.64 Aug 3rd Heavyweights 2019
83 Inferno Loss 8-13 -187.99 Aug 3rd Heavyweights 2019
52 Stellar** Loss 4-13 359.5 Ignored Aug 3rd Heavyweights 2019
105 Frenzy Win 13-5 600 Ignored Aug 4th Heavyweights 2019
84 Cold Cuts Loss 9-13 -117.81 Aug 4th Heavyweights 2019
67 Helix Loss 9-10 537.47 Aug 24th Indy Invite Club 2019
60 Huntsville Laika Loss 7-12 290.29 Aug 24th Indy Invite Club 2019
82 Autonomous Win 10-8 584.03 Aug 24th Indy Invite Club 2019
90 Sureshot Win 13-10 544.78 Aug 24th Indy Invite Club 2019
60 Huntsville Laika Loss 7-10 421.13 Aug 25th Indy Invite Club 2019
82 Autonomous Win 13-10 649.51 Aug 25th Indy Invite Club 2019
33 Fusion** Loss 2-13 682 Ignored Sep 7th Northwest Plains Womens Club Sectional Championship 2019
84 Cold Cuts Win 12-9 646.12 Sep 7th Northwest Plains Womens Club Sectional Championship 2019
55 Crackle Loss 7-13 310.51 Sep 7th Northwest Plains Womens Club Sectional Championship 2019
91 MystiKuE Win 13-12 316 Sep 7th Northwest Plains Womens Club Sectional Championship 2019
31 Heist Loss 6-13 707.56 Sep 21st North Central Club Womens Regional Championship 2019
84 Cold Cuts Loss 8-10 38.09 Sep 21st North Central Club Womens Regional Championship 2019
55 Crackle Loss 3-10 268.04 Sep 21st North Central Club Womens Regional Championship 2019
16 Pop** Loss 2-13 1080.7 Ignored Sep 21st North Central Club Womens Regional Championship 2019
91 MystiKuE Win 13-8 687.16 Sep 22nd North Central Club Womens Regional 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)