#29 RAMP (14-7)

avg: 1622.75  •  sd: 54.6  •  top 16/20: 0.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
19 Public Enemy Loss 11-13 1509.69 Jul 15th TCT Pro Elite Challenge East 2023
20 Toro Win 12-10 1975.61 Jul 15th TCT Pro Elite Challenge East 2023
24 Loco Win 11-9 1920.05 Jul 15th TCT Pro Elite Challenge East 2023
9 Space Force Win 13-12 2015.7 Jul 16th TCT Pro Elite Challenge East 2023
3 AMP Loss 9-11 1818.64 Jul 16th TCT Pro Elite Challenge East 2023
77 Bullet Train Win 10-3 1762.69 Jul 29th TCT Select Flight East 2023
47 Darkwing Win 13-9 1838.06 Jul 29th TCT Select Flight East 2023
31 Kansas City United Win 11-8 1960.76 Jul 29th TCT Select Flight East 2023
51 Classy Loss 10-11 1258.8 Jul 30th TCT Select Flight East 2023
30 Waterloo Loss 11-12 1490.31 Jul 30th TCT Select Flight East 2023
31 Kansas City United Loss 10-13 1267.01 Jul 30th TCT Select Flight East 2023
165 Prion** Win 15-5 1361.72 Ignored Sep 9th 2023 Mixed Central Plains Sectional Championship
131 Stackcats Win 15-7 1490.78 Sep 9th 2023 Mixed Central Plains Sectional Championship
136 Skyhawks** Win 15-1 1456.02 Ignored Sep 9th 2023 Mixed Central Plains Sectional Championship
27 Chicago Parlay Win 12-9 1982.88 Sep 10th 2023 Mixed Central Plains Sectional Championship
99 Nothing's Great Again Win 14-6 1647.91 Sep 10th 2023 Mixed Central Plains Sectional Championship
136 Skyhawks** Win 15-4 1456.02 Ignored Sep 23rd 2023 Great Lakes Mixed Regional Championship
105 Bandwagon Win 15-4 1628.24 Sep 23rd 2023 Great Lakes Mixed Regional Championship
16 Hybrid Loss 8-15 1259.04 Sep 23rd 2023 Great Lakes Mixed Regional Championship
88 Spectre Win 15-7 1708.62 Sep 24th 2023 Great Lakes Mixed Regional Championship
27 Chicago Parlay Loss 9-15 1122.03 Sep 24th 2023 Great Lakes Mixed 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)