#170 Boomtown Pandas (10-9)

avg: 727.04  •  sd: 68.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
231 POW! Win 13-7 817.19 Jul 8th Heavyweights 2023
105 Bandwagon Loss 7-13 470.71 Jul 8th Heavyweights 2023
86 Mad Udderburn Loss 6-13 521.14 Jul 8th Heavyweights 2023
210 ELevate Win 9-7 731.72 Jul 9th Heavyweights 2023
200 Pixel Loss 7-13 -32.75 Jul 9th Heavyweights 2023
179 Frostbite Loss 10-11 521.15 Jul 9th Heavyweights 2023
159 Pandamonium Win 13-9 1204.42 Aug 19th Cooler Classic 34
241 PanIC Win 10-7 531.46 Aug 19th Cooler Classic 34
176 The Force Win 10-9 776.87 Aug 19th Cooler Classic 34
211 Lake Superior Disc Win 13-5 1046.79 Aug 19th Cooler Classic 34
135 Point of No Return Loss 8-13 365.35 Aug 20th Cooler Classic 34
116 Jabba Loss 6-15 392.19 Aug 20th Cooler Classic 34
176 The Force Loss 10-11 526.87 Aug 20th Cooler Classic 34
159 Pandamonium Loss 10-11 660.85 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
60 Minnesota Star Power Loss 3-15 695.76 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
244 Underdogs Win 15-7 684.14 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
135 Point of No Return Win 14-10 1260.21 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
211 Lake Superior Disc Win 15-5 1046.79 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
128 Mousetrap Win 10-9 1039.95 Sep 10th 2023 Mixed Northwest 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)