#96 Y'all (8-15)

avg: -4.36  •  sd: 92.46  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
34 Indy Rogue** Loss 2-15 586.34 Ignored Jun 17th SCINNY1
102 Solstice Win 14-3 281.02 Jun 17th SCINNY1
92 Sureshot Win 15-4 647.83 Jun 17th SCINNY1
72 KnoxFusion Loss 6-15 -106.8 Jun 17th SCINNY1
103 Bullseye Win 15-3 100.11 Jun 18th SCINNY1
72 KnoxFusion Loss 8-14 -42.83 Jun 18th SCINNY1
92 Sureshot Win 14-9 521.69 Jun 18th SCINNY1
102 Solstice Loss 7-9 -598.32 Aug 19th Motown Throwdown 2023
83 Autonomous Loss 1-12 -323.56 Aug 19th Motown Throwdown 2023
92 Sureshot Loss 6-9 -370.74 Aug 19th Motown Throwdown 2023
83 Autonomous Loss 6-14 -323.56 Aug 19th Motown Throwdown 2023
80 Notorious C.L.E. Loss 6-8 2.48 Sep 9th 2023 Womens East Plains Sectional Championship
102 Solstice Win 12-5 281.02 Sep 9th 2023 Womens East Plains Sectional Championship
83 Autonomous Loss 8-13 -219.72 Sep 9th 2023 Womens East Plains Sectional Championship
92 Sureshot Loss 6-13 -552.17 Sep 9th 2023 Womens East Plains Sectional Championship
31 Rival** Loss 0-13 766.44 Ignored Sep 10th 2023 Womens East Plains Sectional Championship
102 Solstice Win 11-8 46.63 Sep 10th 2023 Womens East Plains Sectional Championship
62 Dish Loss 8-15 137.97 Sep 23rd 2023 Great Lakes Womens Regional Championship
31 Rival** Loss 3-15 766.44 Ignored Sep 23rd 2023 Great Lakes Womens Regional Championship
83 Autonomous Win 12-8 717.6 Sep 23rd 2023 Great Lakes Womens Regional Championship
34 Indy Rogue** Loss 5-15 586.34 Ignored Sep 24th 2023 Great Lakes Womens Regional Championship
102 Solstice Win 15-4 281.02 Sep 24th 2023 Great Lakes Womens Regional Championship
80 Notorious C.L.E. Loss 6-15 -297.02 Sep 24th 2023 Great Lakes Womens Regional 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)