Tag Archive for: hypernomics

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Solve Profit First

Suppliers make products and see what markets will bear for them.  That’s precisely backward.

Instead, we can solve for profit potential first and discover product specifications second.

Suppose a market has products for which there are particular quantities, and prices demanded, as shown by the red dots.  We want to avoid competition, so we choose a Target Price, 1, that exploits a price gap.  Given a Demand Frontier, this sets a quantity limit, 2.

With some work (not shown), we find the market supports Features A & B with a green Value Surface (supportable prices based on those features), and that there’s an area of interest with no competition.  Linked to that region are the costs for 1 and 200 units of our new product.  If we constrain the problem (orange planes), we form an enclosure.

We then run Financial Catscans through this region.  Much like brain scans, they are virtual market section cuts.  At the optimum, we solve for the specs of Features A (3) and B (4), and the per-unit profit (5).  Per unit profit (5) times the demand limit quantity (2) yields max potential profit.

In the process, we’ve solved a 4D problem (Feature A, Feature B, Price, Quantity) from a 1D goal (profit).

#innovation #price #value #markets #profit #sales #manangement

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COVID-19 Over Time

Weeks ago, we considered the world’s countries populations on the horizontal axis and the number of their COVID-19 cases on the vertical as the points in blue, below. Several countries formed Upper COVID Infection Limit (those with white triangles inside blue circles). The regressed blue line through these points described 98.5% of their variation.

By May 22, things changed, indicated by the green dots. While the US still led the world in infections, and all states on the Limit in April remained there, some other countries reached this unenviable level (i.e., the line through the green dots with white squares, correlated to 98.4%). The case count in Bahrain more than doubled; Kuwait’s infection rate went up nearly 5.5x. Along with Qatar, with over triple the cases of a few weeks ago, these Gulf States were hard hit. This phenomenon is baffling as most countries in Africa to the west and Asia to the east are doing better.

Peru and Chile did especially poorly, dispelling the idea COVID might be hemispherically-based. Vitamin D levels are of particular interest. Low levels of it correlate to high European mortality rates (https://lnkd.in/gh2tbej)

#covid #inthistogether #innovation #health #covid19analytics

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NFL Wideout Valuation: Go Faster

In 1968, Rocky Bleier joined the Pittsburg Steelers.  After the season, once drafted, he volunteered for duty in Vietnam.

When he came #price to the team camp in 1972, he posted a 4.6-second 40-yard dash.

With part of his right foot blown off.

His previous best was 4.8.

What’s the value of added speed for veterans?  If we remove the rookie contracts and draft halo effects by looking at pros in the league for six or more years (thanks, Jem Anderson!), we can find out.  A shows us the total compensation for NFL wideouts goes up with receptions per game.  At the same time, their value falls dramatically with age.  Speed plays a role too. A 28-year old receiver with 4 catches a game running a 4.65 40-yard dash is worth about $6 million per year (note the equation forming surfaces A and B has P-values of 6.43E-07, 0.41%, and 3.6% for catches/game, age, and 40-yard times, in that order, and 2.32E-06 for the entire equation).

In B, we take another 28-year old with 4 receptions/game, but this one runs the 40-yard dash a quarter second quicker.  The extra speed adds another 2/3 to his compensation, bringing it to $10 million.

It’s hard to improve speed.  But if you can do it in the NFL, it pays off.

#value #valueproposition #generalmanager #worth

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NFL Wideout Valuation

If you’re into the game, you probably have a rough idea about how the National Football League assigns values to its players. A little analysis provides unexpected insights.

Each dot in A and B denotes one of 106 NFL wide receivers in 2019. A’s plane shows the value the league assigns to them. It’s a function of their league years, receptions per game, and draft round (P-values of 7.70E-17, 5.84E-08, 0.02%, respectively, and 6.61E-25 for the entire equation, with an adjusted R^2 of 66.7%). Here, we’ve set the round to 1, years to 4, and receptions per game to 3.04. For those valued features, the NFL awards a wideout with $5 million/year.

B shows us how others can get the same. If we keep league years at 4, we find that if we increase the receptions to 6.09, a 3rd-round wideout (note lower plane) can get as much as a 1st-rounder.

That’s twice the receptions for the same salary.

Knowing this informs decisions. If too-high valuations for 1st-rounders come from long contracts too often, perhaps GMs should seek shorter terms. If a 3rd-rounder receives an extended period offer at a low rate but knows he can perform, maybe he should negotiate for bonuses for his excellent work.

#nfl #nfldraft2020 #players #playervaluation #valuation #value #generalmanager

Laffer Curve Quantified: Pot Taxes Get Too High

The Laffer Curve is the relationship between tax rates and revenues.  For income, taxes of 0% or 100% produce no tax revenue.  Maximum tax receipts lie in-between.

The study of this phenomenon has mainly been theoretical.

The recent rush of states legalizing recreational marijuana gives us a real-world example.

In 2014, Colorado and Washington legalized recreational pot.  Other states followed suit, all with different tax rates.  If we exclude the results for Oregon and California in 2019 (in red), the remaining six blue points form most of the Laffer curve for cannabis.  This blue power curve is highly negative (exponent -1.55) and significant (P-value 1.96E-03).  It explains why Nevada, in 2019, made over 30 times as much per cannabis user as did Washington State in 2014.

In 2019, California, with nearly 13 times the population of its neighbor Nevada, made barely half of the receipts of The Silver State.  California struggles mightily with the cannabis black market because of its tax policy.  There’s a lesson here: Never turn a market analysis problem into a legal one.  If someone blows smoke your way arguing for high marijuana taxes, don’t inhale.

#Laffercurve #markets #marketanalysis #cannabisnews #cannabistax #taxpolicy

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COVID-19 Analysis in 4D

Many variables are at work in the COVID-19 pandemic.  Analyses in 4 dimensions help visualize them.  In markets, such structures use prices as objective functions.  As the virus seeks to replicate, its goal is to infect hosts.  We see each infection as a case.

At right, we plot countries’ populations against their COVID-19 cases on April 28, 2020.  Each dot signifies one of the 163 nations in the study.  Unchecked, only the size of the global community caps the number of cases.  However, we observe a yellow line marking the disease’s Infection Limit on that date.  That line is well-correlated (98.6% R^2); there is little chance it came about accidentally (P-value of 8.21E-10).  Countries on or close to that frontier are worse off than those far away from it.

The green side plane represents an equation derived from the population (set to 720,000,000), density, and GDP per capita (P-values in turn of 3.13E-35, 0.68%, and 5.90E-35).  While we would expect infection rates to go up with density and population, its strong relationship to GDP is unexpected.  Wealthier nations have more resources to fight such outbreaks, but it appears their travel patterns more than offset that.

#covid19 #covid19research #covid19analytics

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Walk This Way – It Could Be More Lucrative

How do businesses’ pay to give workers a short stroll to amenities?  Using open-source data, we find companies buy easy access to nearby banks, stores, and cafes as they pay for office space and zip codes.

In A, we see LA commercial real estate prices rise with square footage and nearby household income (P-values 3.82E-16 and 0.01%, respectively), as shown by the surface.  Included in the calculation of that log-linear plane is “Walk Score,” which “measures the walkability of any address (www.walkscore.com, no affiliation with me).”

B shows the Walk Scores of 60 properties versus their prices.  Walk Score is a statistically significant (P-value 0.69%) contributor to Value (as sustainable prices).  The overall equation uses square footage, household income, and Walk Score.  It has an adjusted R^2 of 71.8%, implying there’s more work to do.

Figures C and D reveal that in Feb 2020 LA, doubling the Walk Score more than proportionally lifted the sustainable price.  Firms wishing to put up a new facility need to know this.  If the added Value of a new building exceeds its added cost, it may be worthwhile to set it up in high walkability areas.

Is NYC like LA? Look at the next post.

#price #marketanalysis #marketintelligence #realestate #target

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What Holds Up Prices?

Price formation often seems steeped in mystery.  “Seeing what the market will bear” is a mantra for many, but why would we want to leave prices to chance if we could avoid it?

What supported the prices for 2013 electric cars?  As shown below, we could make a statistically significant (9.3E-09) estimate of prices using a surface running through the 18 electric car models (as green spheres) that made up the market that year.  That surface reflects that after buyers paid about $6,500 to enter the market, the Price went up $102 for every horsepower and $172 for every added mile of range (P-Values, 0.00038, 4.19E-07, respectively).  Models priced above the surface may be overpriced, those below may be under-priced, or some other significant Features may be at work.

The diagram & the market math behind it demonstrates the first 2 adages of the Law of Value and Demand, which are:

  1. Product Features (as horsepower, range) determine Value
  2. Value determines Price

The green region is Value Space.  How does it relate to Demand?  Read the next post for an answer.

#prices#value

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A Change Of Perspective

Modern economics gets inspiration from thermodynamics, constantly looking for the equilibriums such systems demonstrate.

Multidimensional Economics has a different point of origin.

Consider the maps below and the three questions that follow.

Which two countries are these?

Where do they touch?

Why does it matter?

Look to the next post for the answers.

#demandforecasting #prices

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Why Find Demand Frontiers?

In the last few posts, we’ve been examining Demand Frontiers. You might ask, “What is the point of doing that?”

Well, let’s look.

Recall in the last post, we found the Demand Frontier moved little in 20 years.  In 2016, the Demand Frontier had an equation describing the line running from the upper left to the lower right in the diagram below.  Because the programs forming this line clustered closely about it, the standard deviation of the line is relatively low: $25.5 million.

The United States Air Force proposes to build 100 B-21 bombers at a “projected average procurement unit cost of $550 million per plane in FY2010 (https://lnkd.in/g4Rkx2R or $610M per plane in 2016 dollars.  What are their chances of making that number of planes at that price, given the standard deviation of the Demand Frontier?

As shown below, the B-21 Target is nine standard deviations over the predicted limiting price ($380M) for the B-21.  Examined by another metric, the historical maximum percentage deviation over the Demand Frontier was 17.8%.  The B-21 program proposes to exceed it by 60.5%.

What was the procurement history of other programs that tried to exceed the Demand Frontier? We will find out next time.

#demand #demandforecasting