#2 PoNY (21-3)

avg: 2333.94  •  sd: 58.4  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
39 Pittsburgh Temper Win 13-7 2182.24 Jun 24th Phantom Invite 2023
12 Raleigh-Durham United Loss 9-12 1661.26 Jun 24th Phantom Invite 2023
38 Phantom Win 13-9 2043.55 Jun 24th Phantom Invite 2023
31 Garden State Ultimate Win 13-6 2276.12 Jun 25th Phantom Invite 2023
110 CITYWIDE Special** Win 13-3 1767.75 Ignored Jun 25th Phantom Invite 2023
8 Johnny Bravo Win 15-9 2628.26 Aug 4th 2023 US Open Club Championships ICC
14 Sockeye Win 15-14 2124.88 Aug 4th 2023 US Open Club Championships ICC
5 Chicago Machine Win 15-12 2490.2 Aug 4th 2023 US Open Club Championships ICC
3 Revolver Win 15-12 2545.79 Aug 5th 2023 US Open Club Championships ICC
5 Chicago Machine Win 15-13 2403.89 Aug 5th 2023 US Open Club Championships ICC
1 Truck Stop Win 15-13 2713.41 Aug 6th 2023 US Open Club Championships ICC
3 Revolver Win 15-13 2459.48 Sep 2nd TCT Pro Championships 2023
4 Chain Lightning Loss 12-13 2086.89 Sep 2nd TCT Pro Championships 2023
8 Johnny Bravo Win 14-11 2426.11 Sep 2nd TCT Pro Championships 2023
9 Doublewide Win 15-10 2560.41 Sep 3rd TCT Pro Championships 2023
13 Vault Win 13-11 2233.2 Sep 3rd TCT Pro Championships 2023
1 Truck Stop Loss 7-15 1899.23 Sep 3rd TCT Pro Championships 2023
3 Revolver Win 15-11 2626.46 Sep 4th TCT Pro Championships 2023
32 Scoop Win 15-8 2240.38 Sep 23rd 2023 Northeast Mens Regional Championship
51 TireBizFriz Win 13-6 2152.5 Sep 23rd 2023 Northeast Mens Regional Championship
15 GOAT Win 15-4 2572.27 Sep 23rd 2023 Northeast Mens Regional Championship
94 Magma Bears** Win 13-2 1891.23 Ignored Sep 23rd 2023 Northeast Mens Regional Championship
7 DiG Win 15-11 2548.68 Sep 24th 2023 Northeast Mens Regional Championship
21 Phoenix Win 15-12 2159.75 Sep 24th 2023 Northeast Mens 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)