#5 Chicago Machine (16-7)

avg: 2189.71  •  sd: 50.11  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
83 Red Wolves** Win 15-2 1947.98 Ignored Jul 15th TCT Pro Elite Challenge East 2023
28 Tanasi Win 15-3 2332.45 Jul 15th TCT Pro Elite Challenge East 2023
39 Pittsburgh Temper Win 15-8 2189.52 Jul 15th TCT Pro Elite Challenge East 2023
6 Ring of Fire Loss 13-14 2055.66 Jul 16th TCT Pro Elite Challenge East 2023
2 PoNY Loss 12-15 2033.44 Aug 4th 2023 US Open Club Championships ICC
14 Sockeye Win 15-12 2300.37 Aug 4th 2023 US Open Club Championships ICC
27 Omen Win 15-11 2120.95 Aug 4th 2023 US Open Club Championships ICC
7 DiG Win 15-14 2292.51 Aug 5th 2023 US Open Club Championships ICC
2 PoNY Loss 13-15 2119.76 Aug 5th 2023 US Open Club Championships ICC
8 Johnny Bravo Win 15-13 2326.95 Aug 6th 2023 US Open Club Championships ICC
10 Rhino Slam! Win 13-12 2210.8 Sep 2nd TCT Pro Championships 2023
9 Doublewide Win 15-10 2560.41 Sep 2nd TCT Pro Championships 2023
1 Truck Stop Loss 11-14 2185.89 Sep 2nd TCT Pro Championships 2023
6 Ring of Fire Win 15-10 2634.26 Sep 3rd TCT Pro Championships 2023
6 Ring of Fire Loss 11-12 2055.66 Sep 3rd TCT Pro Championships 2023
4 Chain Lightning Loss 12-14 1990.93 Sep 3rd TCT Pro Championships 2023
9 Doublewide Loss 13-15 1892.63 Sep 4th TCT Pro Championships 2023
88 Black Lung** Win 13-4 1902.74 Ignored Sep 23rd 2023 Great Lakes Mens Regional Championship
76 Haymaker Win 13-7 1934.69 Sep 23rd 2023 Great Lakes Mens Regional Championship
155 Chicago Dark Frogs** Win 13-3 1493.2 Ignored Sep 23rd 2023 Great Lakes Mens Regional Championship
47 Beacon Win 15-9 2078.72 Sep 24th 2023 Great Lakes Mens Regional Championship
27 Omen Win 15-7 2339.79 Sep 24th 2023 Great Lakes Mens Regional Championship
55 Colonels** Win 15-6 2103.69 Ignored Sep 24th 2023 Great Lakes 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)