Visualizing Value

In Multidimensional Economics, Value is a sustainable product price based on its features.  Producers set Prices.  Customers determine Value.  When they don’t match, problems arise.  Buyers pay no mind to cost when considering Value.  If you paid $1000 for a laptop, you don’t care if its cost was $1900, $900, or $90.  You just know it satisfied your Value proposition.  How do markets establish Value?

Value is whatever the market says it is.  For business jets, Fig. A shows us there is a positive correlation between speed and price.  The faster the planes go, the more buyers who can are willing to pay.  Note, though; there is high variation in A near 560 MPH, reflected in the Mean Absolute Percentage Error in D.  Fliers like to be able to take people along with them; thus, it makes sense in B that buyers pay for added capacity.  No one wants to be cramped, either, so observe in C that taller cabins fetch more money than shorter ones.  As we add features B & C, we lower errors in D.

Aircraft speed, capacity, and comfort value terms are analogous to those for computers. Laptop buyers want processor speed, short- and long-term memory, and easy to read screens.

Analysts should consider all features markets find useful.

#business #value #marketanalysis #price #innovation

Fantasy Football: More Real Than Imagined

You’d think that NFL salaries would be performance-based. But, those predictions for running backs seldom exceed R2s of 60%.

Happily, fantasy football gets it right.

In A and B, we predict the value for two NFL running backs. If we take a database of 84 of them and filter out those with no catches or touchdowns in 2019, we get 55 players represented by the A-B points. The equation for their total points has an adjusted R2 of 97.7%, based on their 2019 rushing yards, receiving yards, and touchdowns (P-values 1.66e-27, 8.65e-16, and 9.94e-15, in that order).

Leonard Fournette and Todd Gurley had nearly equal scores but took different paths to success. Fournette, in A, had many rushing and receiving yards as he scored 3 TDs. His actual score (183.4 points) exceeds his prediction (164.2). Gurley (B) had fewer yards but scored more often, and his exact number (188.4 points) mimics the prediction (186.4).

Christian McCaffrey and James White contributed much more than this equation suggests. Both had nearly 3 standard deviations worth more points than their predictions. Multiple different variable combinations would offer more insight into player contributions.


#nfl #fantasyfootball #players #player #value #football #nflnews

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

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

Cannabis Laffer Curve Expanded:
The Netherlands Sparked North American Interest

In an earlier post, we examined the recreational pot tax structure.  Using US-only data, we discovered that at its frontier, a Laffer Curve formed that described the maximum amount of tax revenues possible given specific tax rates.

Here we entertain other authorities taxing legal recreational pot.  Added to the blue points forming a limit is another describing the tax rate and revenue per user for The Netherlands (NL) in 2008 (adjusted for inflation).  Through these blue points, the Laffer Curve explains 90% of their variation and is highly negative (power exponent -1.61).

Also considered now but not part of the Laffer Curve is the recent experience of British Columbia (BL 2019).  Observe it registered minuscule tax revenues.

At least 3 factors influence cannabis tax receipts: 1) Ease of legal access: BC, OR, and CA lag far behind their better-organized counterparts in making legal recreational marijuana sufficiently available.  2) Tax rate: From 15.3% (NV 2019) to 108% (WA 2014), revenues go up as tax percentages go down.  3) The proximity of lower-cost options: some would-be CA or CA tourist receipts or go to NV or black markets.

#laffercurve #market #marketanalysis #price #taxpolicy #demand #tax

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

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

COVID-19: Wealth, Density, Population, and Latitude

In the last post, we discovered an Upper Infection Limit for COVID-19.  Its cases positively correlate to density (as expected) and per capita GDP (not expected).  After Mike McRae wrote that “COVID-19 Deaths Are Being Linked to Vitamin D Deficiency (Health, May 1, 2020),”  I decided to dig deeper.

I found COVID-19 has a Lower Infection Limit at work, too.  Shown in white below, the line describing this threshold has an adjusted R^2 of 93.5% and a P-value of 3.05E-06, reflecting that it did not come about by chance.  The difference in the countries that form each line is just as significant.

Excepting Qatar and the United States, all of the countries along the upper curve are European, and their southernmost extent is 37°55′ N (the southern tip of Italy).  The nations forming the Lower Infection Limit are Asian or African, and, if we remove India and China, their northernmost point is 28°32’N (Myanmar), with most of their landmasses in the tropics.

Given the relative success of the tropical countries, should we increase sun exposure in northern climes?  How does sun angle to the ground regulate Vitamin D uptake and Coronavirus inflection rates?

#COVID #COVID19 #population #infection #VITAMIND

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