#8 NOISE (6-5)

avg: 1893.89  •  sd: 90.8  •  top 16/20: 87.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
1 shame. Loss 8-15 1602.4 Aug 4th 2023 US Open Club Championships ICC
4 BFG Win 14-13 2084.6 Aug 4th 2023 US Open Club Championships ICC
18 Polar Bears Win 15-12 2061.7 Aug 5th 2023 US Open Club Championships ICC
4 BFG Loss 5-15 1359.6 Aug 5th 2023 US Open Club Championships ICC
3 AMP Loss 9-15 1552.37 Sep 2nd TCT Pro Championships 2023
20 Toro Win 15-13 1951.66 Sep 2nd TCT Pro Championships 2023
16 Hybrid Win 15-6 2423.85 Sep 2nd TCT Pro Championships 2023
2 Drag'n Thrust Loss 9-15 1556.7 Sep 3rd TCT Pro Championships 2023
13 Slow Win 15-7 2449.58 Sep 3rd TCT Pro Championships 2023
18 Polar Bears Win 12-9 2106.58 Sep 3rd TCT Pro Championships 2023
3 AMP Loss 9-14 1593.98 Sep 4th TCT Pro Championships 2023
**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)