#112 Dizzy Kitty (6-5)

avg: 1003.82  •  sd: 78.62  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
61 Malice in Wonderland Loss 11-12 1169.25 Jul 8th Summer Glazed Daze 2023
107 Columbus Chaos Win 13-10 1355.67 Jul 8th Summer Glazed Daze 2023
199 MoonPi Win 12-7 1052.42 Jul 8th Summer Glazed Daze 2023
124 Magnanimouse Win 11-9 1210.79 Aug 12th HoDown Showdown 2023
153 Memphis STAX Win 11-9 1044.74 Aug 12th HoDown Showdown 2023
248 Pickles** Win 15-3 626.54 Ignored Aug 12th HoDown Showdown 2023
43 Dirty Bird Loss 7-15 878.32 Aug 13th HoDown Showdown 2023
69 Too Much Fun Loss 8-15 652.47 Aug 13th HoDown Showdown 2023
108 Bear Jordan Loss 10-15 573.49 Aug 13th HoDown Showdown 2023
98 FlyTrap Win 13-11 1282.24 Aug 26th Soda City Round Robin
98 FlyTrap Loss 10-11 928.4 Aug 26th Soda City Round Robin
**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)