#66 Banshee (8-13)

avg: 625.96  •  sd: 54.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
81 Stormborn Win 11-6 837.74 Jun 24th Spirit of the Plains
97 Minnesota Superior A** Win 13-2 576.66 Ignored Jun 24th Spirit of the Plains
90 Twisted Womxn Win 13-0 703.82 Jun 24th Spirit of the Plains
60 Wicked Loss 8-9 597.92 Jun 25th Spirit of the Plains
54 Stellar Win 8-7 1001.94 Jun 25th Spirit of the Plains
29 Pop Loss 3-7 802.13 Jun 25th Spirit of the Plains
39 Brooklyn Book Club Loss 7-9 803.86 Jul 29th TCT Select Flight East 2023
55 Shiver Win 11-8 1236.26 Jul 29th TCT Select Flight East 2023
54 Stellar Loss 5-12 276.94 Jul 29th TCT Select Flight East 2023
80 Notorious C.L.E. Win 15-9 818.46 Jul 30th TCT Select Flight East 2023
62 Dish Loss 8-12 261.63 Jul 30th TCT Select Flight East 2023
50 Drift Loss 8-12 473.79 Jul 30th TCT Select Flight East 2023
60 Wicked Loss 8-9 597.92 Aug 26th Ragna Rock 2023
40 Hayride Loss 6-9 659.18 Aug 26th Ragna Rock 2023
64 TWISTED Loss 4-5 564.75 Aug 26th Ragna Rock 2023
60 Wicked Loss 5-11 122.92 Aug 27th Ragna Rock 2023
35 Huntsville Laika Loss 5-10 598.7 Aug 27th Ragna Rock 2023
35 Huntsville Laika Loss 2-11 572.59 Aug 27th Ragna Rock 2023
60 Wicked Win 10-9 847.92 Sep 9th 2023 Womens West Plains Sectional Championship
54 Stellar Loss 9-15 361.46 Sep 9th 2023 Womens West Plains Sectional Championship
90 Twisted Womxn Win 15-5 703.82 Sep 9th 2023 Womens West Plains Sectional Championship
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)