#41 Garden State Ultimate (1-9)

avg: 1656.97  •  sd: 78.83  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
33 Oak Grove Boys Win 13-8 2278.47 Jun 21st Philly Invite 2025
29 Shade Loss 9-12 1518.55 Jun 21st Philly Invite 2025
19 Mephisto Loss 10-12 1827.59 Jun 21st Philly Invite 2025
8 Truck Stop Loss 7-13 1773.73 Jun 21st Philly Invite 2025
17 Chain Lightning Loss 7-15 1485.17 Jun 28th 2025 Pro Elite Challenge East
8 Truck Stop Loss 7-13 1773.73 Jun 28th 2025 Pro Elite Challenge East
9 Sub Zero Loss 7-15 1606.76 Jun 28th 2025 Pro Elite Challenge East
21 Phoenix Loss 6-14 1447.99 Jun 29th 2025 Pro Elite Challenge East
25 Mallard Loss 10-15 1495.95 Jun 29th 2025 Pro Elite Challenge East
32 Sprout Loss 11-15 1401.78 Jun 29th 2025 Pro Elite Challenge East
**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)