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# Blog

## Cars, Gold, Houses, Toys & Stock: What gives value?

The title of this post is intentionally misleading. We frequently discuss the traits that lead to value here in the Lifeboat Blog. But today, I was asked a more nuanced question: “What things will hold their value?

And there is a ulterior motive in being a columnist for Lifeboat. Analyzing the dynamics of durable value leads to some surprising conclusions about the money supply and what a society chooses to use as money. We’ll get to this at end of this post.

We know that value comes from supply and demand. There are no exceptions. But, we have not addressed the properties that make an asset hold value over the long haul. Let’s consider some examples…

Cars

In an affluent, mobile society, most people desire personal, point-to-point transportation — and so there is clearly a demand for automobiles.

But style & technology change rapidly and automobiles deteriorate with use and weather. After 8 to 10 years, their cost and maintenance rise dramatically, and owners lust for a new model. So cars don’t get our award for assets that hold value.*

Popular Toys

In the 1970s, the Cabbage Patch doll from Calico Industries, and later, Tickle Me Elmo in the 1990s created a buyer frenzy that rivaled a lemonade stand in the desert. Shoppers fought each other to grab a limited supply. Clearly, demand was very high. The one shown below is listed at Ebay this week with a starting bid of $5,000. Other, less popular styles can be found for$4.99.

At first, this demand was driven by clever marketing and crying children in the week before Christmas. Demand was driven by a parent’s love. But at the peak of frenzy, demand shifted to buyers without children who felt certain that they could profit from selling the dolls that they snatched up first.

But the demand was not durable. Fads driven by frenzy don’t hold value for the long haul—especially when a manufacturer can simply turn the spigot back on.

Stocks & Bonds

A share of stock represents ownership in a corporation. A municipal bond represents a lien against a city—or the fees generated by an infrastructure project.

In both cases—especially bonds, which are a limited promise—no one expects value to last forever. It is a time-sensitive bet with the intention of expiration, redemption or exchange. So, these things also fail our criteria for durable value.

Houses & Real Estate

Like cars, homes require ongoing maintenance. But, most people weigh the maintenance cost against the benefit of having shelter, rather than comparing it to their gain or loss in value.

On the other hand, real estate value fluctuates in the long run due to things that are difficult to predict — population density, demographics, and quality-of-life issues related to infrastructure: weather, seismic events, politics, and access to health care and education.

Some real estate rises enormously in value over 50 or 100 years. Yet, we have seen boom-and-bust cycles that wipe out substantial wealth. So, real estate does not cut it in our contest for durable value.

Gold

The allure of gold and other precious metals is that their supply is capped — or limited by slow and predictable growth. The asset is difficult to find. It is acquired only from natural phenomena.

So, if we can also make it fungible, divisible, portable and difficult to counterfeit, then it meets most of Aristotle’s requirements for a functional currency. Theoretically, this can lead to widespread demand.

Gold certainly has exhibited its ability to hold value throughout thousands of years. But it is not so easily tested and divided in the field, and the impression that it has intrinsic value is an illusion. That’s because the fraction of gold acquired by investors dwarfs the amount actually needed for dentistry, electronics and even jewelry. In this modern era, even gold is becoming a house of cards, because its value is built upon speculation and emotion.

Oil (aka “black gold”)

With the rise of the automobile and power plants that burn fossil fuel, oil became a reserve currency of the 19th and 20th centuries. But there are two problems with it holding value over the long haul.

First, unlike gold, oil is a consumable in every market. Therefore it is difficult to think of it as an asset. Also, we now live in a century in which energy and transportation is rapidly switching away from oil, while at the same time, new technology is making it cheap to acquire new oil. This (along with a history of violent political theater) dramatically deteriorates its potential as a store of value in coming years.

Money

The supply-demand dynamics of money is widely misunderstood. More than 2,300 years ago, Aristotle defined the properties of a functional currency.

Earlier, we stated that all value comes from supply and demand. But, it is fair to ask “What creates the demand?” or “What backs the expectation of future demand?” Surprisingly, even if we limit our scope to just one country (USA), the value of government-issued currency has been tied to different things over time:

• Gold
• Promise of redemption
• Legal tender (public must accept it for all debts)
• Settlement of taxes
• The “good faith and credit” of workers

Ultimately, demand is influenced by oversupply and by public perception more than government promises or laws. The perception that the US dollar has no cap and that its supply can be inflated whenever a body of transient politicians decides to raise the debt ceiling may eventually cause its value to collapse. Although it has not happened yet, at some point consumers (or those holding our debt), will begin to question if Americans have the capacity and will to produce and export the goods & services necessary to balance their mass consumption of the past half-century.

And so, government-issued Fiat does not pass our smell test for durable value. Sooner or later, all national currencies collapse. On a personal level, the only question that matters is if you will be caught by surprise—with a fraction of wealth tied to your favored currency.

What has the potential to meet all requirements for holding value?

Wouldn’t it be fascinating if we could find an asset that is a product of pure mathematics? A perfect asset would be fair, fungible, immutable, and capped. It could never be inflated or manipulated by politicians. It would decouple governments from monetary policy. It would be politically agnostic.

If correctly designed, it would be capable of absorbing and incorporating improvements developed by any copycat or pretender nipping at its heels. Most important, it would be open source, peer-to-peer, massively distributed, redundant, and completely permissionless.

This perfect asset would derive trust from mathematics and crowd-sourced consensus. It would not require that anyone believe in a government, a bank, a land mass, or the uncertain supply of precious objects. Authenticity could tested easily and its value transmitted instantly. The history of each unit would be completely transparent. With free tools, anyone, anywhere could trace its history of moving from one owner to the next.

Ten years ago, such an asset was unleashed into the wild by a person or team of developers under the pseudonym, Satoshi Nakamoto. It not only meets all of these requirements, it has built-in immunity from competition. It even resolves a technical problem that troubled Aristotle more than two millennia ago.

I won’t name this radical yet natural evolutionary development in this answer—but, I can confidently state that it passes our test for an asset that will hold value over time. Despite a wildly fluctuating exchange rate with Fiat currency, its inherent value has never dropped. Ultimately, you will no longer asses value based on the exchange rate of an anachronistic currency that fails all of the other smell tests. Instead, you will assess value on how many heads of lettuce you can buy or how much that new sailboat costs.

* A classic car avoids the problems associated with use & maintenance—and it can hold value over a long period. But like a Picasso painting, the market for classic cars has a limited audience, especially for the florescent green ’63 Mustang that I found in in my great uncle’s garage. Additionally, it is subject to the whims of popular perception. Styles go in and out of vogue and so we cannot predict how long that car will hold value. (Please call me if you value my uncle’s Mustang at more than $150,000). Philip Raymond co-chairs CRYPSA, hosts the Bitcoin Event and is keynote speaker at Cryptocurrency Conferences. He is a top writer at Quora. ## Why does anyone attribute value to Bitcoin? Oh, Cheez…We’re back to this question, again! As a Bitcoin columnist, I get this question a lot. Today, an answer was requested at Quora.com, where I am the lead contributor on cryptocurrencies: “Clearly, some people value Bitcoin. But How can this be? There is nothing there to give it value!” Many individuals, like the one who asked this question, suspect that Bitcoin was pulled out of thin air—and that it is not backed by gold, a government, or an authoritative redemption guaranty. After all, it is just open source code. What stops me from creating an ElleryCoin using the same code?! Let’s start with the short answer: • Indeed, it was pulled out thin air • It isn’t backed by an asset, government or promise • You could easily clone Bitcoin (the entire mining ecosystem) and distribute it yourself. It would be exactly like Bitcoin. Yet, Bitcoin is clearly valued by everyone, and your new coin is unlikely to generate interest or adoption. A More Complete Answer: What is value? Bitcoin has more intrinsic value than a government printed paper bill. The value arises from a combiation of fundamental properties: • It has a capped supply • It is widely recognized, liquid, and resistant to legislation • It has attained the robust supply-demand of a growing, 2-sided network. • It is open and transparent. This elevates user trust • Unlike cash and credit, Bitcoin requires no back-end settlement. That’s because it is not a payment instrument. Rather it is money itself. • Finally, it’s value is likely to be durable, because it is not printed by a country that has racked up debt. In fact, it can never be inflated. Downside and Risks But wait! What about the long transaction delay and high cost? There are sharp disagreements anong miners, users and developers concerning block size, transaction malleability, and replay issues. Aren’t these a deal killers? And what about wild volatility in the exchange rate? Doesn’t this retard adoption as a functional currency? These are transient issues associated with a new technology. Although Bitcoin is weathering growth pains that arise from a new and distributed governance technology (democracy can be messy!), all of these issues have sound solutions. We have already witnessed and tested the solyutions with various forked coins. Think of them as beta tests. Even if current problems delay the day when you can spend bitcoin at every retail establishment—it is already sucking liquidity from national currencies and becoming the world’s de facto reserve currency. Many individuals find all of this hard to accept. That is because we have been conditioned to think that ‘value’ arises from assets with ‘intrinsic’ value, the promise of redemption, or by edict. This is not true. In all things, (including gold, a Picasso painting, or your labor) value arises from simple supply and demand. Some individuals claim that all other factors are secondary. But, even this statement is false. All other factors are irrelevant. They may be related, but they are not the source of value. I recognize that this answer may seem smug or definitive. So, allow me to suggest related questions with answers that are a bit more interesting, because they are subtle. Unlike the question of value, these two questions are open to analysis and opinion: (1) “Will people continue to value bitcoin in the future?” — And (2) “When will Bitcoin stop swinging wildly in value?” (measured by its exchange rate with other currencies). This is fun! Let’s explore… Philip Raymond co-chairs CRYPSA, publishes A Wild Duck and hosts the New York Bitcoin Event. Last month, he kicked off the Cryptocurrency Expo in Dubai. Click Here to inquire about a live presentation or consulting engagement. ## What gives Bitcoin Value? For most of us, figuring out the value of something that we want, comes from research. If you want a new set of wine glasses, you check the price online. Perhaps you consult a catalog. If the set of 8 stemware goblets that you like are a current model from a major company, there are probably many places to buy them. If there are multiple Ebay sellers and many recently completed sales, then you can establish the value with precision. I’ve written a lot of Bitcoin articles on this Lifeboat Blog and elsewhere, so, let’s dig a bit deeper this time. Let’s talk about from where value really comes. Supply and Demand In the end, an item’s value is a direct result of supply and demand. It’s no different with a currency. And let’s be clear: Despite a raging debate, Bitcoin is a currency and not just a payment instrument. How can I be certain? Try this mental exercise— Western Union money orders and Amazon Gift cards are each trusted monetary instruments. They facilitate cash transactions. But are they currencies with inherent value? If so, there would be no need to denominate them in units of fiat currency. A money order is only worth something before it is redeemed. The gift card is only worth$500 when it is purchased or received as a gift. As the $500 is depleted, it becomes worthless. Eventually, it is just a piece of plastic. But like a dollar bill, a bitcoin can be circulated over and over. You may believe that its value comes from the government, but more realistically, its value arises from brand recognition and from pure supply and demand—not from a trusted redemption authority. Bitcoin isn’t the first ethereal stash of bits with value. But it is more durable than others. The latest Pixar film on DVD or On Demand from your cable service provider has value. But piracy reduces the value dramatically. The supply is no longer scarce (no matter the demand) because of the ease and willingness to replicate digital files in any quantity. A Picasso painting is very rare, but it is so scarce, that we cannot gather enough data points to establish a stable value. Even worse, it’s not portable, divisible or fungible and it is nearly impossible to validate in the hands of the average person. But, commodities like iPhones, Doritos tortilla chips—or even non-branded things, like Idaho potatoes, have a large and fluid market. These things have very measurable value and we can track the change in value over time. People like to think that money is different than other commodities. In practice, it differs only by its handling characteristics: Compared to a Picaso painting or a new iPhone 6, currency has these properties. It is: • portable • fungible • divisible • widely recognized • resistant to forgery • backed by something tangible Bitcoin has all of these characteristics. In fact, it surpasses your national currency in every way. But many people are confused about that last niggling detail… Aristotle called it intrinsic value. They worry that there is no gold—or at least the promise of a stable government—to establish and stand behind the value of a bitcoin unit (BTC). The concern is understandable, but it is wrong. Recall that value arises through supply and demand and not simply because of authority or promises. The real question is Can we trust that the supply is limited and that the demand is durable?”. That is, will my coin be recognized, coveted and honored in the future? The Case Against Bitcoin as a Currency Here are some frightening facts (frightening for some cryptocurrency enthusiasts and early adopters): Bitcoin is manufactured out of thin air. It lacks the underpinnings of a traditional currency. Referring to that last item on Arisotle’s list above, Bitcoin seems to fail the test of intrinsic value, because it lacks at least one of these properties: • A fractional reserve requirement • An edict to remit taxes in Bitcoin • A promise of a trusted authority • Any claim of pegging it to the value to some essential commodity (intrinsic value) • Bitcoin doesn’t even offer a perception of uniqueness. The formula is open for anyone to copy. You could create a competing ‘Bob-coin’ tomorrow. In the absence of at least one of these things, detractors claim that Bitcoin lacks a foundation—and so it is effectively worthless. But value does not come only from authority. It comes from trust and is goverend by supply and demand. In fact, math may be a more trusted ‘authority’ than the directors of your national treasury and reserve board. Supply and demand leads to more tangible value than bankers, especially if the math leads you to believe that the demand will continue to outpace the supply. In fact, this is the primary reason that you are comfortable with a$20 bill in your pocket. You have a pretty good idea, that next week, it will still buy 2 movie tickets or 2 pizzas.

Bitcoin has achieved a “two-sided network effect” (Google the term and the economist “Marshall Van Alstyne”). It has captured the public imagination more than Picasso. It cannot be manufactured. With a reasonable understanding of wallets, it cannot be seized, stolen or lost.

The ability to mine new bitcoins is capped with a total supply of 21 million units, and so there is no opportunity for governments to inflate it through mismanagement of taxation or spending. They cannot even inflate it with good intent (for example, when they need to repair a bridge or provide for the poor). Instead, the ability to pay for these services (and all other government functions) forces them to live within a balanced budget. Spending cannot outpace the revenue generated by taxes and bonds. In a Bitcoin economy, the bonds will more likely be paid back by user fees rather than the future debt of unborn generations.* You get the point: Because governments no longer control the printing press, they cannot make hollow promises and then kick the problem into the next administration. With a limited money supply that everyone recognizes as money, governments are forced to live within their means.

The last item in the list above decries Bitcoin’s lack of uniqueness. You cannot mint your own bitcoins of course—but you can create an equivalent bitcoin ecosystem yourself. If your name is Bob, you can call it Bobcoin. Many countries and organizations are already doing this.

This is really no different than the US Dollar or your own national currency. The government note is difficult to counterfeit, but so is your own signature when placed on a fancy printed currency (Let’s call it a Bob-Buck). The problem is that the dollar is widely recognized, trusted and accepted, but few people other than your kids are collecting Bob Bucks.

You would face the challenge of spurring adoption. Whether it’s Bob Bucks (paper) or Bobcoin (cryptocurrency), how will you get the world to covet, mine and trade your new currency? That’s the point of a two-sided network. It becomes increasingly more difficult after one method rises to the top—especially if that method is open, transparent and extensible. Bitcoin is open. It is subject to worldwide scrutiny. But this works both ways. Bitcoin can also add incremental improvements that are part of any pretender to the throne.

Bitcoin is not just a transient coin-du-jour. It evolves and so it will not die.

How Can the Value be Measured?

I get this question a lot, and so I am adding the answer here. There is no need to measure the value of Bitcoin or define debt. Its value floats with supply and demand like a true world currency. Because the supply growth is capped and well understood, it is resistant to manipulation. As time goes by, it becomes far less likely to exhibit wild swings in value.

A few years from now, if Bitcoin spikes or tanks by 10% in a short time, you will be more likely to wonder “What is affecting the dollar?” (or Euro), rather than “What is affecting Bitcoin”. Consumers will budget for the cost of a new car or refrigerator in BTC rather than dollars or Euros. You will even see catalogs that print prices in BTC and honor them for the life of the catalog or online sale. After all, in an international market, it makes sense to quote a price in units with no geopolitical boundaries, just as we quote time in UTC (formerly called GMT).

Are these predictions crazy? They are not even bold. For us, here at Lifeboat Foundation, they are rather obvious. If we can be accused of dreaming, it is because we are ahead of the game. Look ahead, yourself. The signs are clear…

If Bitcoin has Value, What is the Value?

As Bitcoin adoption moves past enthusiasts and early adopters, the capped supply of coins (21 million, max) will be spread thinner and thinner. This doesn’t play out like a classic shortage, because unlike a supply squeeze on food or medicine, you can work with a smaller piece of the pie each year. The piece needed to pay for a car or an iPhone simply gets smaller as the unit price floats higher and higher.

Last year, I set up an equation to predict how high Bitcoin will float in 5 or 10 years. It involved a lot of WAGs (wild *ss guesses). Although I am a pundit, I am not a mathematician, and so the attempt was incomplete. No need to rehash that exercise.

As Hysteria Withers, Bold Becomes Mundane

Eventually consumers, banks, brokers, and governments will recognize that Bitcoin is a far greater opportunity than it is a threat. It pulls the world together by decoupling currency controls from national agenda, inflation, manipulation and loss (You can back up your Bitcoin. Try doing that with your paper money or a defunct bank).

Philip Raymond is CEO and Co-Chair of CRYPSA,
The Cryptocurrency Standards Association.

_____________
* This is just one reason why an eventual transition to Bitcoin (as currency, and not just as a payment instrument) is in the national interest. It demonstrates to citizens that monetary policy is backed by more than growing debt, inflation or the promises of transient officials. It returns any government or economic entity to a non-inflationary, limited-supply pie. The pieces of pie can grow in value, but the pie cannot be watered down by printing more ingredients, counterfeit or even by enemy action.

## What is water worth? — By Giorgis Kallis | Forbes India

The traditional framing of the issue is a choice between accepting the power of markets and ‘playing their game’ to win environmental concessions vs. the purist perspective of saying No to any hint of money or markets in environmental policy.
In this article we will describe the positions of two relatively new fields of study—Ecological Economics and Political Ecology—in an effort to redefine the terms of the choice and chart a path for a pragmatic approach.

## In a Jobless Economy we need Indemnification for Influence

originally posted @Ntegrationalism

This past week I was at the Canadian Consulate General in New York for their Celebration of Innovation in Financial Technology, which featured 10 start-ups or early stage companies from the most robust nation in financial terms, according the IMF & World Bank stress tests. I’ve been thinking about fintech and the use of technology to better distribute wealth via identifying the value that individuals possess for some time. The meeting hosted by @miriam_leia CanadaNY’s chief innovation officer compelled me to start piecing some of the writing that I’ve done on the topic of value. I’m unsure how to summarize things enough for a quick read, hopefully this isn’t too choppy.

Great Automation

We find ourselves at the beginning of a somewhat great automation. While formidable arguments exist from every political faction on the potential for job creation and depletion, forecasters can confirm that the growth trends of technology show no signs of slowing; continued automation of tasks in business services for enterprise are on the horizon.

·Demand side: Restore public-sector jobs and invest in infrastructure for immediate jobs and long-term growth.

·Supply side: Extend Bush-era tax cuts to spur economy. Cut spending to curb growth-crushing debt and deficit.

·Another way: Invest in community-based, member-owned cooperatives and reduce the workweek.

Even as the world economy reacts to the automation of the its most affluent and technologically advanced nations, we find ourselves unable to distribute vales well for physical laborers.

I wrote to the IEET a few years ago to provoke some discussion on the ethics of automation from an information technology standpoint, as the ethnography behind automation has been a large focus of mine over the past decade with corporations as a consultant. According to ContactHMRC.com, “Even as resistance was futile the protests continue at each firm I visited. It seems that the exponential growth of technology, not only from a hardware standpoint (via Moore’s Law), but also from a methodological and software standpoint, requires a new method of distributing values.” Jobs are difficult to generate in this great automation. Further, if our labor culture is changing for goods and services, I’m not aligned with the idea that we can restore historical methods. We must innovate out of joblessness.

Our inability to quantify the toiling of the individual and even the institution has hampered our ability to sustain gainful employment (jobs) during the accelerating changes. Note: I am not referring to sustainability, as it is illogical to seek, but to state that we are not agile. In the modern world individuals and institutions are participating more than ever in the process of development of goods/services through passive means. They are influencers. Specifically I mean: we are performing the act of development of goods and services without being compensated for it or even realizing our participation. Consider a long being in empty space, worthless. While human-kind can’t have inherent value, community can. We all generate values at the point we can interact with another.

In business CRM (customer relations management) and UX (user experience) are examples of how we leverage a consumer to be their own discoverer, developer, and deploy-er of solutions to problems. Via a conduit (profit seeking institution) as a platform, we interact. Sure there may be a single or group of experts monitoring the feedback, but the fact that feedback exists warrants some nominal indemnification other than the creation of new products for consumers, no? Figure 1. Represents a crude the CRM input process.

While the system is still new, culturally, functionally, and technologically – we’ve managed to build and identify better sensors for data points on participants in the crowd. Our ability to evaluate systemic risks and opportunities is actually an older statistical science that mathematicians and economists have been tinkering for decades. The fact that entrepreneurs who develop software solely based on customer reviews in the Salesforce.com AppExchange to then distribute as an added service is justification enough to consider how we indemnify a society for its knowledge and experience as a user. Is it valuable to seek more useful or even intuitive experiences? Companies have dedicated efforts to understanding sentiment on news by consumers and reactors to information regarding their enterprise. Firms like, Finmaven: Tool for publicly traded companies to monitor, publish and analyze social media…or, Market IQ: Analysis of unstructured data such as social media to provide real time insights to traders.. Automation is much broader than marketing and customer relations; however, the processes around relations are at the core of automation.

Too Connected To Fail

Our relations in our close and extended networks are what provide the tangible value that other individuals and institutions derive from us. As social beings we are somewhat obligated to interact well. While this sheds no light on egalitarian ideals, it does provide an opportunity for less-introverted individuals and institutions to be indemnified for participation. For example, at HBS participation often accounts for 50% of the total course grade. That method is good enough to start with, no? Lowering our degrees-of-separation from our peers and attracting new connections is what elects the people at the top of the classes at Harvard Business School. People learn how to communicate more effectively through the rigor of participation, and their value sky rockets relative to the rest of the business community.

In the past decade the global “great recession” exposed our systemic vulnerability and ties to each other at the micro and macro scale. Financial innovations have enabled risk transfers that were not fully recognized by financial regulators or by institutions themselves, complicating the assessment of a “too-connected–to-fail” problem. Companies in FinTech are closing the communication gap like Quantify Labs: CRM and Content Platform for Institutional Finance. In plain English they show all communication between bank’s customers and vendors. Scenarios like, who checked which communications and the supply-chain of communication. Anyone who has ever watched a movie about finance and trading of equities knows that traders and brokers mostly talk to each other to buy and sell stakes in equities for mutual benefit. Expanding communications or even knowledge of existing communications on creates more threads to an ever-expanding web of connectivity. As an evolving species, we need our web to continue to grow in order to support our initiative for growth, or creating technologies on a more grand scale.

The mentioned initiative can be found in every facet of our lives. Form the International Monetary Fund (IMF) Figure 2. A diagrammatic depiction of co-risk feedbacks, presents the conditional co-risk estimated between pairs of selected financial institutions. In plain English, the numbers on the diagram are communication linkages between people/assets at these banks.

While there is some debate on exactly what the framework should be to quantify co-dependence or co-risk, the best solution should be a growing group of qualified rating methods and agencies evaluating data differently as it changes and expands, as it does. Similar to this type of co-dependence our civilization’s most important institutions, our most important individuals are also co-dependent on a group of near and distant peers. We are all dependent on other to discover, develop, and deploy solutions to problems of a more personal kind.

Software services like Klout, Kred, Peer Index, and at least a dozen more are making efforts to mine through the data that we communicate to influence, in order to deliver a score with regards to our varied value (depending on their methodology and focus). Similar to the IMF individuals need to evaluate co-dependence and influence on their peers but also on institutions. Applying Klout-like methods to measure our value will enable us to make formidable legal and political arguments for out just due. And perhaps a more manageable transition through structural unemployment and globalization is possible via what’s due. Note: I am using influence and co-dependence to reference social value on entities. We all play a role in the negotiation that is life’s happenings but those of us who aren’t entrepreneurs rarely know that we’re a part of the conversations affecting decisions.

Connectivity is starting to span beyond social connections and socialization in general. It is starting to be quantified in new ways, as there are no shortages in places to install sensors and things to understand. Physical sensors that help us recognize, colors, odors, heat, distance, sounds, etc are all being created as devices that empower an application programming interface (API), which is another way to allow information technology to quantify how we engage sensors. The appification of sensors like Node or Canary or Leap Motion are the next generation of sensors that we’ve used on watches and cameras. Looking forward to days where nanorobotics can monitor mitochondrial stability to forecast and avoid apoptosis to help cells cheat death, of course, we still have a long way to go. Today, the case can be made to spread some of the abundant wealth by validating the abundant value each individual creates through info tech.

Technoprogressivism

From a political standpoint a new debate needs to be had around how we react to the types of joblessness that results from rapidly changing technology, as the rate of changing is seemingly increasing. In regards to technoprogressivism and structural unemployment via automation and other methods; it is ideal to embrace the end of work: meaning that there should be some equitable distribution of the leisure created by automation.

While ideas on the end of work have been around for centuries, the distribution of leisure within or succeeding a capitalistic system is quite new. The innovation in distribution of social values that needs to occur is separate from welfare. It is understandably just to enforce a pervasive welfare state for our inability to distribute value; however, it is not an adequate remedy to the wealth gap and technologies effort to quantify all things. The root of our problem is our inability to distribute values among those who actually participate in creating it, while managing risk (or insurances). A guaranteed basic income and/or a shorter work week are not elaborate enough to compel an agile system of developments in compensation, even while they are necessary.

Information technology and the manipulation of BigData, are enabling us to understand who is influencing which changes, even at a nominal rate. Incentivizing individuals and institutions to interact in a more transparent way is important in evaluating new ways to indemnify them for their participation in society. It helps them learn and others to learn about them. This may read invasive and unethical from a privacy stand-point. While that debate should be had, my stance in short: is that human-kind cannot achieve the interconnectedness it requires for distributing our relatively abundant resources through complete privacy or conservatism. Going forward, the next wave of value to exploit it not material but portable. Figure 3. Shows my Klout rating of 60/100 for influencing my social network. The question should be asked: who have I influenced? What decisions have they made based on my influence? Have they contributed to any gross domestic product (GDP)? Is that value portable?

Portable Value

In a short essay I wrote called “Portable Value”, influenced by Hernando de Soto I channeled his ideas that “adequate participation in an information framework that records ownership”, can spur economic growth. History shows that coinage in the exchange of good in local/international trade made portable wealth possible. The more transparent information-collection of purchase, stake holdings, and ownership of goods from legal-documentation to land to human-slaves made our modern economies grow. While I don’t agree with de Soto’s position on land titling and side with a more communal and democratic systems of collective land tenure because this offers protection to the poorest and prevents ‘downward raiding’ in which richer people displace squatters once their neighborhoods are formalized, I am aligned with the idea of an individual titling.

Neither de Soto nor his opponents wrote about individual titling specifically, and I’m not very fond of the phrase, but it’s fitting nomenclature assuming that they would have called the distributing of ownerships outside of land specifically some version of “individual titling” considering their democratic capitalism alignment. The significance here rest with ownership and the individual. The phenomenon of private ownership has pervaded and propelled the growth of humans and our technological extensions which make life more livable. Capitalism is the term of choice for the private ownership and trade of things. Per my views (@Ntegrationalism), Capitalism is merely an economic manifestation of human-kind’s technological evolution. While I’m aware of the rigid opposition to my last sentence, I’ve written it under the assumptions that

• All things in existence are physiologically connected.
• Humans cannot have nature.
• Technology is deterministic when applied to the human condition.
• Individualism spawns competition resulting in arbitrage.

Regarding information on ownership, economic growth requires a growth in participation of owners to in-turn expand the amount of wealth or wealth opportunities in the collective (system). Nostalgic and conservative ideas of constricting the potential growth of human-kind and the institutions that we’ve built is futile and unwise, as we live in a dynamic realm (world, galaxy, universe, multiverse) which renders life as we know it, unsustainable by definition. Even the home planet (Earth) warmed by the nearest star (Sun) will be an unsustainable body in due time. We need to be more agile in how we adapt to change.

As a species or parent of species, human-kind should be encouraged to compel as many participating owners of physical-properties (land) and nonphysical-properties (intellectual property, digital, and other) as possible to create a web of interconnected and integrated interest, per our vigilant growth. While land is scarce and in de Soto’s day, it was the end of the titling arguments, our ability to create valuable information and intellectual property has surpassed that of land property. The individual must be able to own the information that they distribute and monetize it. There is a firm called 4pay in Canada that aims to create the beginnings of a “data locker” by creating mobile applications that allow consumers to control transactions without giving personal or financial data to creditors or retailers. Knowing that this will be a heavy blow to the advertising industry and could hinder individuals from accessing valuable information on goods/services that they actually want and need, the delivery model may be flawed. While innovations away from the existing supply chain may prove formidable, I think that some legal and financial incentives to provide individuals with their own information footprint have to be implemented at the local and international scale not to protect the individual, but to empower them. Individuals have to know when they are being used as insights to make decisions about their peers lives.

## Why The #Occupy Movement Has No Chance, Yet

I’ve spent some time thinking about what the #Occupy movement is really representing. I’ve tried to attend the camps as I’ve traveled and interview the people in the camps; as well as, their formidable opponents in the ownership positions of the respective societies that Occupiers exist.

I think that I’m comfortable echoing the analysis in that Occupiers have done a good initial job in comparison to similar movements around the world and in the United States in particular. They’ve caught the attention of the masses, in that everyone knows what #Occupy means. Of course the problems of any fledgling movement are that its priorities aren’t hashed (#) out. While everyone knows what #Occupy is; no one has any idea of what it wants, or rather, needs.

Every movement-struggle-jihad, has is a battle of philosophy on how a society should exist versus how it does. Based on the consistent and more frequent collapse in the economic system, it is evident that we are due for some structural change in the modern world. When I listen to the rhetoric of this movement and the defense of its identified opponents, I think the following apply. There is a clash of ideals on whose altruism is not only virtuous but most beneficial. On the one hand we have that of the individuals, formally represented by the #Occupiers. On the other we have that of the institutions, formally represented by their owners/stakeholders. While individuals (humans in this case) can allocate a moral regard to their fellow man/woman based on their acknowledgment of his/her intrinsic or extrinsic value, institutions do not. Yet some individuals can advocate the virtues of an institution because for their holding that the institution’s incentives to take action better the society as a whole.

Institutions were created by individuals to protect the discovery, development, and deployment of technologies (methodologies, hardware, & software) that help individuals control what would otherwise be a chaotic environment. Who wants to live in 3000 B.C.E.? I’d doubt any of us could enjoy limiting our communication to a distances less than 20 feet. While institutions have served individuals well over the millennia their control mechanisms have the potential to run-a-muck. Their primary control mechanisms are related to their extrinsic value, or ability to generate revenues above the costs to exists. Controls validate the existence of each institution (for-profit & not-for-profit alike), but individuals don’t regard themselves as having extrinsic value alone (at least not all of them), per this on-going survey that I’ve been taking with some backlash about the use of language on “value”. Problem comes into play when those who are still benefiting from the existing operations of institutions clashes with those who are no longer benefiting. As institutions trying to sustain existence, they actually have incentives to suppress markets to indemnify stakeholders, per their understanding of who is most valuable.

Regarding the Occupy movement and its potential participants, the progress will occur when and if the most radical of the bunch agree that the contrast of values between individuals and institutions is infringing on their civil or even human rights and is in fact stifling their ability to live productive lives. Regardless of how they derive their understanding of the modern economic situation, they’ll have to hold it as dear and urgent as their more radical predecessors of the last past successful liberal movements. I’m not referring to MLK’s boycotts or the freedom riders, or the Jewish resistance in Europe, or the Mandela’s political activism. I’m referring to the immediate threat that militant groups like the Black Panthers, or the onslaught of the Allied Forces, or the provocative military growth of Umkhonto we Sizwe and the many like groups respectively per each struggle. The laws of arbitrage are clear and animalistic. Incumbent leadership, ideals, and conservatism can only respect some formidable opposition.

The incumbent power in 1950’s United States and 1980’s South Africa only yielded because they perceived an inevitable destructive threat; any rhetoric that suggests otherwise is misleading. It would take years to list all the martyrs from every movement who gave their lives to inspire the few, and were willing to take other’s lives for their cause. The pathology of pacifism is a failed effort when it does not inspire an aggressive colleague. Occupiers are going to have to figure out what in the world they can do to change the way institutions and individuals agree on human value. Although they were arguing slightly different causes, the incumbent powers decided to oblige Lyndon B Johnson immortalizing Martin Luther King in order to nullify the slogan “black power” and its author Kwame Ture (Stokely Carmichael). It seems as though it takes a guilty old man faced with the passions of an aggressive young man, to make any incremental change..

- Originally at Integrationalism

## 3 Question Human Value Survey

In chatting with people online about the enduring Jobs crisis in the USA and distribution of wealth (value), I regularly received responses that people (Americans in this case) care less about others who have (per the responders) intrinsic value. And so, I’m compelled to ask the question: If something has an intrinsic value, how can it’s care be avoided or go uncompensated? Please take a quick moment to give me your thoughts on THIS SURVEY.


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## Problem Solving

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# What is intermediate thrust and brief pause for checkout on launching Soyuz spacecraft?

In a book I'm reading, the author mentioned the launch of Soyuz spacecraft as follows:

What made it more spectacular was how close we were when it lifted off the launch pad. Yuri, Tim and I were sitting on the roof of the search-and-rescue tower, about 1.5 km away from the rocket. It was gone 3 a.m. on a beautifully clear night and, when I saw the main engines light up, followed by a deep roar a few seconds later, a huge grin spread across my face. But my expression soon changed to one of astonishment. What I had heard so far was merely the engines at intermediate thrust, when there’s a brief pause for a checkout. As the main engines opened up to full power, the noise engulfed me – a powerful rumble of deep bass notes that reverberated around my chest cavity. Just when I thought it couldn’t be any more impressive, the Soyuz lifted off the launch pad and, as it climbed, a deafening crackle filled the air.

My question is: what is intermediate thrust? And what is the brief pause before check out here for?

Does intermediate thrust means that the intermediate part try before starts and then they pause to check everything is okay before launch?

The Soyuz launch sequence goes:

T-00:00:20  ENGINE IGNITION (Core Stage and Boosters)
T-00:00:20  Core Stage Umbilical Mast Retraction
T-00:00:15  Intermediate Thrust Level
T-00:00:10  Engine Turbopumps at Flight Speed
T-00:00:05  Full Thrust Level
T-00:00:00  Booster Umbilical Tower Retraction
T-00:00:00  LIFTOFF

The engines are running for 20 seconds before liftoff, which is a really long time. Roughly half of that is spent at an intermediate thrust level (hence lowered fuel and oxidizer consumption, reduced heat load on pad) before the last five seconds are at full thrust.

There's a SpaceFlight 101 article about an abort that says a bit more:

Soyuz goes through a 20-second ignition sequence leading up to liftoff, starting with the spin-up of the turbopumps before the engines throttle up to an intermediate thrust level for a brief checkout. Data monitored during the engine check period includes pressure and temperature in the chambers, pump speeds and vibration.

ArianeSpace isn't all that more specific:

Ignition of the central core and boosters occurs at an intermediate level of thrust on the launch pad 20 seconds before lift-off in order to monitor engine health parameters before the engines are throttled up and the vehicle leaves the pad.


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# A scalable framework for NBA player and team comparisons using player tracking data

#### Abstract

Player tracking data provides a platform for the creation of new basketball statistics that can dramatically improve the ability to evaluate and compare player performance. However, the increasing size of this new data source presents challenges in how to efficiently analyze the data and interpret findings. A scalable analytical framework is needed that can effectively reduce the dimensionality of the data while retaining the ability to compare player performance.

In this paper, Principal Component Analysis (PCA) is used to identify four components accounting for 68% of the variation in player tracking data from the 2013-2014 regular season. The most influential statistics on these new dimensions are used to construct intuitive, practical interpretations. In this high variance, low dimensional space, comparisons across any or all of the principal components are possible to evaluate characteristics that make players and teams similar or unique. A simple measure of similarity between player or team statistical profiles based on the four principal components is also constructed. The Statistical Diversity Index (SDI) allows for quick and intuitive comparisons using the entirety of the player tracking data. As new statistics emerge, this framework is scalable as it can incorporate existing and new data sources by reconstructing principal component dimensions and SDI for improved comparisons.

Using principal component scores and SDI, several use cases are presented for improved personnel management. Team principal component scores are used to quickly profile and evaluate team performance, more specifically how New York’s lack of ball movement negatively impacted success despite high average scoring efficiency as a team. SDI is used to identify players across the NBA with the most similar statistical performances to specific players. All-Star Tony Parker and shooting specialist Anthony Morrow are used as two examples and presented with in-depth comparisons to similar players using principal component scores and player tracking statistics. This approach can be used in salary negotiations, free agency acquisitions and trades, role player replacement, and more.

## 1Introduction

The National Basketball Association (NBA) launched a public database in September 2013 containing new statistics captured by STATS LLC through their innovative sportVU player tracking camera systems (NBA partners with Stats LLC for tracking technology, 2013). The cameras capture and record the location of players on the court as well as the location of the ball, and the data are used to derive statistics that expand greatly upon traditional statistics available for analysis of basketball performance. We can now break down shot attempts and points by shot selection (e.g. driving shots, catch and shoot shots, pull up shots), assess rebounding ability for contested and uncontested boards, and look at new statistics like average speed and distance and opponent field goal percentage at the rim. The availability of such data enables fans and analysts to dig into the data and uncover insights previously not possible due to the limited nature of the data at hand.

For example, techniques to uncover different ‘positions’ based on grouping statistical profiles have become increasingly popular. It reflects the mindset of current NBA coaches and general managers who are very much aware of the different types of players beyond the five traditional roles. However, a recent proposal (Alagappan, 2012) has received criticism for its unintuitive groupings and inability to separate out the impact of player talent (Haglund, 2012). The NBA player tracking data has the ability to differentiate player performance across more dimensions than before (e.g. shot selection, possession time, physical activity, etc.) which can provide better ways to evaluate the uniqueness and similarities across NBA player abilities and playing styles. Another example is in the development of offensive and defensive ratings. Current methods require an estimation of possessions and other statistics to produce ratings (Oliver et al., 2007). With the ability to track players’ time of possession and proximity to players in possession of the ball through player tracking, we can develop more accurate representations of possessions and better player offensive and defensive efficiency metrics.

However, the high dimensionality of this new data source poses major challenges as it demands more computational resources and reduces the ability to easily analyze and interpret findings. We must find a way to reduce the dimensionality of the data set while retaining the ability to compare player performance. One method that is particularly well-suited for this application is Principal Component Analysis (PCA) which identifies the dimensions of the data containing the maximum variance in the data set. This article applies PCA to the NBA player tracking data to discover four principal components that account for 68% of the variability in the data for the 2013-2014 regular season. These components are explored in detail by examining the player tracking statistics that influence them the most and where players and teams fall along these new dimensions.

Additionally, a simple measure of similarity in statistical profiles between players and teams based on the principal components is proposed. The Statistical Diversity Index (SDI) can be calculated for any pairwise player combination and provides a fast and intuitive method for finding players with similar statistical performances along any or all of the principal component dimensions. This approach is also advantageous from the standpoint of scalability. The possibilities to derive new statistics from the player tracking data are endless, so as new statistics emerge, this approach can again be applied using the new and existing data to reconstruct the principal components and SDI for improved player evaluation and comparisons.

Numerous applications in personnel management exist for the use of SDI and principal component scores in evaluating and comparing player and team statistical performances. Two specific case studies are presented to show how these tools can be used to quickly identify players with similar statistical profiles to a certain player of interest for the purpose of identifying less expensive, similarly skilled players or finding suitable replacement options for a key role player within the organization.

This article is organized as follows. Section 2 describes the player tracking data and data processing. Section 3 provides the analysis and interpretations for the four principal components in detail, showing how players and teams can be compared across these new dimensions. Section 4 introduces the calculation for SDI and two case studies where principal component scores and SDI are used to find players with similar statistical profiles to All-Star Tony Parker and role player Anthony Morrow for personnel management purposes. 5 conclusion concludes the article with final remarks.

## 2NBA player tracking data

### 2.1Data description

Currently, there are over 90 new player tracking statistics, and data for all 482 NBA players from the 2013-2014 regular season are available. Separate records exist for players who played for different teams throughout the season, including a record for overall performance across all teams. Brief descriptions of the newly available statistics adapted from the NBA player tracking statistics website (Player Tracking, 2016) are provided for reference and will be helpful in better understanding the analysis going forward.

Shooting

Traditional shooting statistics are now available for different shot types:

• Pull up shots - shots taken 10 feet away from the basket where player takes 1 or more dribbles prior to shooting

• Driving shots - shots taken where player starts 20 or more feet away from the basket and dribbles less than 10 feet away from the basket prior to shooting

• Catch and shoot shots - shots taken at least 10 feet away from the basket where player possessed the ball for less than 2 seconds and took no dribbles prior to shooting

Assists

New assist categories are available that enhance understanding of offensive contribution:

• Assist opportunities - passes by a player to another player who attempts a shot and if made would be an assist

• Secondary assists - passes by a player to another player who receives an assist

• Free throw assists - passes by a player to another player who was fouled, missed the shot if shooting, and made at least one free throw

• Points created by assists - points created by a player through his assists

Touches

Location of possessions provides insight into style of play and scoring efficiency:

• Front court touches - touches on his team’s offensive half of the court

• Close touches - touches that originate within 12 feet of the basket excluding drives

• Elbow touches - touches that originate within 5 feet of the edge of the lane and the free throw line inside the 3-point line

• Points per touch - points scored by a player per touch

Rebounding

New rebounding statistics incorporate location and proximity of opponents:

• Contested rebound - rebounds where an opponent is within 3.5 feet of the rebound

• Rebounding opportunity - when a player is within 3.5 feet of a rebound

• Rebounding percentage - rebounds over rebounding opportunities

Rim Protection

When a player is within 5 feet of the basket and within 5 feet of the offensive shooter, opponents’ shooting statistics are available to measure how well a player can protect the basket.

Speed and Distance

Players’ average speeds and distances traveled per game are also captured and broken out by offensive and defensive plays.

### 2.2Data processing

Only players who played at least half the 2013-2014 regular season, 41 games, are included in this analysis. This restriction is made to reduce the influence of player statistics derived from only a few games played. Also fields containing season total statistics and per game statistics are dropped from the analysis since they could be influenced by number of games and minutes played throughout the season. Instead, per 48 minutes, per touch, and per shot statistics are used. The final data set contains 360 player records each containing 66 different player tracking statistics.

## 3Principal component analysis

With numerous player tracking statistics already available and the potential to develop infinitely many more, it is difficult to extract meaningful and intuitive insights on player comparisons. Now that the data are available for more granular and detailed comparisons, a methodology is needed that can analyze the entirety of the data set to extract a handful of dimensions for comparisons. These dimensions should be constructed in a way that ensures optimality in differentiating players (i.e. dimensions should retain the maximum amount of player separability possible from the original data) and can be understood in terms of the original statistics.

Principal Component Analysis (PCA), developed by Karl Pearson (Pearson, 1901) and later by Hotelling (Hotelling, 1933), is a particularly well-suited statistical tool that can accomplish this task through identifying uncorrelated linear combinations of player tracking statistics that contain maximum variance. Interested readers can find a brief technical introduction to PCA in Appendix A. Components of high variance help us better differentiate player performance in these directions in hopes that the majority of the variance will be contained in a small subset of components. This simple and intuitive approach to dimension reduction provides a platform for player comparisons across dimensions that best separate players by statistical performance and can be implemented withoutexpensive proprietary solutions, providing more visibility into how the method works at little to no additional cost.

### 3.1Dimension reduction

PCA is sensitive to different variable scalings in the original data set such that variables with larger variances may dominate the principal components if not adjusted. To set every statistic on equal footing, all statistics are standardized with mean 0 and variance 1 prior to conducting the analysis.

PCA is most useful when the majority of the total variance across variables are captured by only a few of the principal components, thus the dimension reduction. If this is the case for the NBA player tracking data set, it means that we are able to retain the ability to differentiate player performance without having to operate in such a high dimensional space. Figure 1 scree shows the variance captured by each principal component. Note the variance captured in the first principal component is very high and decreases drastically through the first four components. After that, the change in variance is relatively flat, forming an elbow shape in the plot. This means that the variances captured by the fifth component onward are very similar and much smaller than the first four components. Moreover, the first four components capture 68% of the variance across the original variables, so we utilize these four components going forward to analyze and compare player performance and playing styles.

### 3.2Principal components

Each principal component (PC) is a linear combination of the original variables in the dataset. There is a vector for each principal component containing the coefficients associated with each of the variables in the original data set and are called loading vectors. These describe the influence of each variable for each principal component and are used to interpret these new dimensions in terms of the original variables. Figure 2 pcload plots the categorized loading coefficients for the four principal components and is explored in detail in the following sections. Variables can have a positive or negative contribution to the principal component. While the sign is arbitrary, understanding which variables contribute positively or negatively can help with interpreting the principal components. The most important statistics for each component are presented in the following sections, but tables containing all loading coefficients for variables contributing significantly to the principal components are also available in Appendix B for more details.

Each player can then be given a set of PC scores by multiplying the standardized statistics by their corresponding loading coefficients and then taking a sum (see Appendix A for details). Figure 3 contains plots of the PC scores for all players with a select few noted for illustration. Using the loading and score plots here, we can begin to understand and interpret what these new dimensions are capturing and use them for player comparisons.

#### 3.2.1PC 1: Inside vs. Outside

The first principal component accounts for the most variation, 42% of the total variance. Table 1 lists statistics with highly positive and negative loadings for PC 1. Refer to Figure 2 for categorized PC 1 loadings for all statistics. These are used to better understand the meaning of the scores along this dimension. Players with positive scores for PC 1 are able to secure rebounds of all kinds and are responsible for defending the rim and close shots. Notable examples are Andre Drummond, DeAndre Jordan, and Omer Asik. Players with negative scores for PC 1 drive the basketball to the hoop and take pull up and catch and shoot shots often, which implies they tend to be outside players. These players also tend to possess the ball more often and generate additional offense through assists. Examples here are Stephen Curry, Tony Parker, and Chris Paul.

#### 3.2.2PC 2: Assist and Drive vs. Catch and Shoot

PC 2 accounts for another 12% of the total variance and Table 2 lists statistics with highly positive and negative loadings. Also refer to Figure 2 for categorized PC 2 loading coefficients. Players with positive PC 2 scores generate offense mainly through assists and driving shots. These players tend to possess the ball often and either kick the ball to teammates for shot attempts or drive the ball to the basket. Many point guards fall into this category with examples like Ricky Rubio, Tony Parker, and Chris Paul. Players with negative PC 2 scores provide offense primarily through catch and shoot shots and are very efficient scorers, especially from behind the 3-point arc. Primary examples are Klay Thompson, Kyle Korver, and Anthony Morrow.

#### 3.2.3PC 3: Scoring and Rebounding Efficiency vs. Speed

Table 3 lists statistics with highly positive and negative loadings for PC 3 which explains 9% of the total variance. Also refer to Figure 2 for categorized PC 3 loading coefficients. Players with positive PC 3 scores are extremely quick on both sides of the ball and cover a lot of ground while on the court. Some examples are Ish Smith, Shane Larkin, and Dennis Schroder. Players with negative PC 3 scores are largely responsible for scoring when on the court and provide a significant amount of offensive production per 48 minutes. Scoring and rebounding efficiency characterize many of the superstars in the NBA with players like Kevin Durant, Carmelo Anthony, and LeBron James touting highly negative PC 3 scores.

#### 3.2.4PC 4: Catch and Pass/Shoot vs. Slash

Table 4 lists statistics with highly positive and negative loadings for PC 4 which accounts for another 4% of the total variance. Also refer to Figure 2 for categorized loading coefficients for PC 4. This component is characterized by players’ tendencies when they receive possession of the ball. Players with positive PC 4 scores tend to pass or convert catch and shoot shots when the ball goes their way (e.g. Kevin Love, Spencer Hawes, and Patty Mills) while players with negative PC 4 scores tend to drive the ball and score efficiently when they get touches (e.g. Tyreke Evans, Rodney Stuckey, and Tony Wroten).

### 3.3Team PC Scores

Not only can we characterize players by principal components, but teams can also be profiled along these new dimensions as well. There are numerous ways to aggregate the player PC scores to form a team-level score, but here a simple weighted average is used. The kth team PC score, tk(team), can be found by taking an average of the kth PC scores across the n players weighted by the minutes played throughout the season, mi, i = 1, …, n.

##### (1)
tk(team)=i=1nmi*tk(i)i=1nmik=1,,4

Figure 4 shows the distribution of all NBA teams across these dimensions as well as their corresponding 2013-2014 regular season winning percentage. This view is useful in seeing the differences and similarities in team playing styles and how they impact success.

For example, the New York Knicks had an extremely negative PC 2 score. Further investigation shows it is partially the result of catch and shoot offense from J.R. Smith, Andrea Bargnani, and Tim Hardaway Jr. who were all in the top 50 in catch and shoot points per 48 minutes. However, another major factor is that 8 of the 12 New York players were below average in passes per 48 minutes (average was 58 passes per 48 minutes) which is indicative of poor ball movement. All-Star Carmelo Anthony is in this group and has long been labeled a “ball hog” (Naessens, 2014) which is supported by his below average passing and above average number of touches and scoring. In fact, Anthony’s top 10 performance in points per 48 minutes, points per touch, and rebounding efficiency helped earn his team the most negative PC 3 team average score. Note that team average PC 3 score is negatively correlated with winning percentage, yet the Knicks won only 37 games and failed to make the playoffs.

To better understand how these team average PC scores impact winning, Table 5 contains the results from a multiple linear regression analysis on winning percentage. Note that negative PC 3 scores are highly correlated with winning while positive PC 2 and PC 4 scores are also highly correlated with winning. Negative PC 3 scores are associated with high average scoring and rebounding efficiency. Referring back to Tables 2 and 4, passes, touches, and assists contribute positively to PC 2 and PC 4 scores. Regarding New York’s lackluster season, it seems that Anthony’s great offensive production was not enough to offset the negative impact of extremely poor passing and ball movement.

These results are better illustrated in Figure 5 where the regression-weighted sum of PC 2 and PC 4 scores (0.17*[PC 2 Score] + 0.09*[PC 4 Score]) determine the size of the points. Low average PC 3 scores (i.e. high average scoring and rebounding efficiency) have the strongest influence on winning percentages. However, controlling for PC 3 scores, teams with higher regression-weighted PC 2 and PC 4 scores (i.e. higher average passing, touches, and assisting) generally hold higher winning percentages. This helps account for the success of the Spurs, who distributed the scoring responsibilities more evenly across the team, resulting in lower average scoring efficiency and higher passing on average (9 of the 13 Spurs players were above the average 58 passes per 48 minutes).

## 4Statistical Diversity Index (SDI)

Another way the PC scores can be used to compare player statistical profiles is to combine them to produce one measure of how different one player’s statistical profile is from another. Here a simple calculation based on the sum of squared difference between the two players’ four PC scores is proposed.

### 4.1Calculation

For any two players, player i and player j, where tk(i) represents the kth principal component score for player i, the Statistical Diversity Index (SDI) can be calculated as

##### (2)
SDIij=k=14(tk(i)-tk(j))2

### 4.2Personnel management

A large SDI for two players indicates that their statistical profiles are very different across the four principal components defined above. Using this measure, we can develop lists of players that have statistical profiles most similar to certain players which has many applications in personnel management.

#### 4.2.1Case Study 1: Tony Parker

For coaches and general managers that have a certain player in mind they would like to add to their team, this measure can produce a list of players with the most similar statistical profiles who may also be good candidates to consider and might come at a lower price tag. For example, Table 6 lists the five players with the lowest SDI when compared with Tony Parker, meaning they have similar PC scores to Tony Parker, along with their 2013-2014 season salary.

To better understand the additional value of the player tracking data, PC scores and SDI can be recalculated using only traditional statistics. This approach identifies DeMar DeRozan as the most similar player to Tony Parker although DeRozan and Parker have an SDI of 75 using player tracking statistics (89 other players have a lower SDI compared to Tony Parker). To better explore the difference between the comparisons using traditional and player tracking statistics, DeRozan is included in the next set of comparisons along with J.J. Barea and Brandon Jennings from Table 6. See Fig. 6 for a comparison of the PC scores and Table 7 for selected statistics for these players.

##### Table 7

Selected statistics for comparison (per 48 minutes unless stated otherwise)

 Statistic Parker Barea Jennings DeRozan Points 27.1 21.5 21.7 28.5 Catch and Shoot Points 2.5 3.0 3.0 5.8 Driving Points 10.3 8 4.2 6.1 Assist Opportunities 19.4 20.5 21 10.4 Assist Points Created 21.9 22.5 23.7 12.6 Touches 123 121 113 76 Passes 90 89 84 43
##### Table 8

Players with lowest SDI compared with Anthony Morrow and 2013-2014 salary from http://www.basketball-reference.com/

 Player SDI Salary Klay Thompson (GSW) 2.3 $2,317,920 CJ Miles (CLE) 2.9$2,225,000 Tim Hardaway Jr. (NYK) 3.0 $1,196,760 Terrence Ross (TOR) 3.8$2,678,640 Martell Webster (WAS) 4.1 \$5,150,000
##### Table 9

Selected statistics for comparison (per 48 minutes unless stated otherwise)

 Statistic Morrow Thompson Miles Hardaway Jr. Ross Webster Catch and Shoot Points 9.7 12.3 11.4 9.3 10.4 9.7 Points per Touch 0.45 0.48 0.40 0.37 0.38 0.28 Passes 26.3 25.1 36.9 35.3 31.6 43.5
##### Table 10

 Statistic Loading Contested Rebounds 0.178 Rebounding Opportunities 0.176 Offensive Rebounds 0.173 Total Rebounds 0.172 Contested Offensive Rebounds 0.171 Opponent Field Goal Attempts at the Rim 0.17 Contested Defensive Rebounds 0.17 Uncontested Rebounding Percentage 0.169 Opponent Field Goal Makes at the Rim 0.169 Offensive Rebounding Opportunities 0.169 Defensive Rebounding Opportunities 0.166 Close Points 0.161 Close Attempts 0.159 Close Touches 0.158 Defensive Rebounds 0.156 Uncontested Rebounds 0.154 Uncontested Offensive Rebounds 0.153 Uncontested Defensive Rebounding Percentage 0.153 Uncontested Offensive Rebounding Percentage 0.145 Blocks 0.14 Uncontested Defensive Rebounds 0.14 Points per Half Court Touch 0.121 Elbow Touches 0.114 Catch and Shoot Effective Field Goal Percentage -0.104 Catch and Shoot Attempts -0.105 Catch and Shoot Makes -0.106 Pull Up 3-Point Percentage -0.116 Secondary Assists -0.117 Free Throw Assists -0.127 Pull Up 3-Point Makes -0.128 Driving Points -0.132 Driving Attempts -0.137 Assists -0.137 Pull Up 3-Point Attempts -0.137 Points Created From Assists -0.137 Catch and Shoot 3-Point Percentage -0.139 Assist Opportunities -0.14 Time of Possession -0.142 Pull Up Makes -0.143 Pull Up Points -0.145 Team Driving Points -0.146 Drives -0.146 Pull Up Attempts -0.15 Front Court Touches -0.15


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# Uniqueness of the Laplace Transformation

Today; when I was doing some Inverse Laplace transformation in the class, I encountered the following problem cited in Zill's book:

The inverse Laplace transformation may be not unique. In Problems 29 and 30 evaluate $\mathscr{L}\{f(t)\}$.

29. $f(t) = \left\{ \begin{array}{ll} 1, & \quad t\ge0,~ t\ne1, t\ne2 \\ 3, & \quad t=1 \\ 4, & \quad t=2 \\ \end{array} \right.$

30. $f(t) = \left\{ \begin{array}{ll} \text{e}^{3t}, & \quad t\ge0,~ t\ne 5, \\ 1, & \quad t=5 \end{array} \right.$

I did them, but I was wondered how to explain the students if they ask me about the criteria for the uniqueness. I think it is rooted in the functional analysis, however, I am weak in this area. Is there any easier way to explain the uniqueness for second year graduate students? Thanks for your time.

• Undergraduate, I mean. :-) – mrs May 5 '13 at 15:15
• Babak S: ✿Nice Question✿ – Software May 5 '13 at 15:22
• @Babk : for second year undergrads, I would just say that for the purposes of Laplace transforms, Fourier series, Fourier transforms, etc., we consider functions to be equivalent if they differ on only a finite number of points (or a finite number of points per period in the case of Fourier series). When you do the integrals to compute their Laplace transforms, etc., you get the same results. – Stefan Smith May 6 '13 at 0:01
• @StefanSmith: Thanks so much for the advices. I'll do that. :-) – mrs May 6 '13 at 11:02


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# Similar to electron diffraction, neutron diffraction microscope is also used for the determination of the structure of molecules.

Question:

Similar to electron diffraction, neutron diffraction microscope is also used for the determination of the structure of molecules. If the wavelength used here is 800 pm, calculate the characteristic velocity associated with the neutron.

Solution:

From de Broglie's equation,

$\lambda=\frac{\mathrm{h}}{m v}$

$v=\frac{\mathrm{h}}{m \lambda}$

Where,

$v=$ velocity of particle (neutron)

$h=$ Planck's constant

$m=$ mass of particle (neutron)

$\lambda=$ wavelength

Substituting the values in the expression of velocity (v),

$v=\frac{6.626 \times 10^{-34} \mathrm{JS}}{\left(1.67493 \times 10^{-27} \mathrm{~kg}\right)\left(800 \times 10^{-12} \mathrm{~m}\right)}$

$=4.94 \times 10^{2} \mathrm{~ms}^{-1}$

$v=494 \mathrm{~ms}^{-1}$

$\therefore$ Velocity associated with the neutron $=494 \mathrm{~ms}^{-1}$


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# Etude de la diffusion de particules lourdes sur des systèmes atomiques et nucléaires

Abstract : In this thesis quantum mechanical non-relativistic few-body problem is discussed. Basing on fundamentals ideas of Faddeev and Yakubovski three and four body equations are formulated and solved for fermionic atomic and nuclear systems. Former equations are modified to include long range interactions. Original results for nuclear and molecular physics were obtained : - Positively charged particle scattering on Hydrogen atoms was considered. Predictions for $\pi^(+)$-H, $\mu^(+)$-H and p$^(+)$-H scattering lengths were given. Existence of an unknown, very weakly bound H$^(+)_(2)$ bound state was predicted. - Motivated by the possible observation of bound four neutron structure at GANIL we have studied compatibility of such an existence within the current nuclear interaction models. - 4 nucleon scattering at low energies was investigated. Obtained results for n-3H, p-3H and p-3He systems were compared with the experimental data. Validity of realistic nucleon-nucleon interaction models is questioned.
Keywords :
Document type :
Theses
Complete list of metadatas

Cited literature [82 references]

https://tel.archives-ouvertes.fr/tel-00004178
Contributor : Admin Base Des Publications de l'In2p3 <>
Submitted on : Thursday, January 15, 2004 - 10:58:26 AM
Last modification on : Thursday, November 19, 2020 - 12:58:47 PM
Long-term archiving on: : Wednesday, September 12, 2012 - 12:25:09 PM

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• HAL Id : tel-00004178, version 1

### Citation

Rimantas Lazauskas. Etude de la diffusion de particules lourdes sur des systèmes atomiques et nucléaires. Physique mathématique [math-ph]. Université Joseph-Fourier - Grenoble I, 2003. Français. ⟨tel-00004178⟩

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# Proof by Induction (concerning $3^n\ge1+2n$)

I've been able to follow the idea and steps of induction so far but I've hit a road block in understanding one of the examples in a text book. This is what the book says p.97:

Prove: $3^n \geq1 + 2n$

Skipping past the base case and assuming it's true, the books inductive step is as follows:

Show: $3^{n+1} = 1 + 2(1+n)$

LHS $= 3\cdot 3^n$
LHS $\geq 3(1+2n)$ [by assumption]
LHS $\geq 1+2+2n+4n$ [algebra]
LHS $\geq 1+2(1+n)$ [since $n>0$]

How can the $4n$ be omitted by $n>0$? This really boggles me, appreciate any insights and help.

Thanks!

-

If n>0 then 4n>0 and omitting it from the RHS reduces the value. So >= still applies - or applies more strongly, and the = could be dropped.

-
Ah I see, thank you! Dropping the = makes sense, but does doing so change the proof? –  xlm Mar 1 '12 at 8:14
@xlm, it forces you to add the condition $n > 0$ and prove a new base case. Edit: hang on... is the base case not $n=0$? If not, why not? Equality holds then... I would have thought it should be $n \ge 0$, in which case you can't drop the $=$. –  Peter Taylor Mar 1 '12 at 9:34
Apologies for not adding it earlier, base is n=1 since the proof is for n>=1 –  xlm Mar 1 '12 at 13:18
Apologies for ambiguity there - I was not referring to the base case in dropping the equality, but to the inductive step. It is exploring such things which leads to extensions and generalisations. –  Mark Bennet Mar 3 '12 at 9:08

Since $n>0$, we have $4n>0$ so $1+2+2n+4n>1+2+2n=1+2(1+n)$, thus $\text{LHS}\geq 1+2(1+n)$.

-


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# Mahler polynomial

In mathematics, the Mahler polynomials gn(x) are polynomials introduced by Mahler (1930) in his work on the zeros of the incomplete gamma function.

Mahler polynomials are given by the generating function

$\displaystyle \sum g_n(x)t^n/n! = \exp(x(1+t-e^t))$

Mahler polynomials can be given as the Sheffer sequence for the functional inverse of 1+tet (Roman 1984, 4.9).

The first few examples are (sequence A008299 in OEIS)

$g_0=1;$
$g_1=0;$
$g_2=-x;$
$g_3=-x;$
$g_4=-x+3x^2;$
$g_5=-x+10x^2;$
$g_6=-x+25x^-15x^3;$
$g_7=-x+56x^2-105x^3;$
$g_8=-x+119x^2-490x^3+105x^4;$

## References

• Mahler, Kurt (1930), "Über die Nullstellen der unvollständigen Gammafunktionen.", Rendiconti Palermo (in German) 54: 1–41, JFM 56.0310.01
• Roman, Steven (1984), The umbral calculus, Pure and Applied Mathematics 111, London: Academic Press Inc. [Harcourt Brace Jovanovich Publishers], ISBN 978-0-12-594380-2, MR 741185 Reprinted by Dover, 2005


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Our minds use the notions of cause and consequences all the time. It is, without a doubt, one of the fundamentals of human reasoning. However, when observed closely and formally, it becomes apparent that our concept is based rather on intuition and lacks strictness and connection to the real word. This post is intended as a quick introduction to causation from the philosophical point of view.

General thoughts

1. Where to establish the causal relation? We are going to use propositions, which represent quantitative properties of various systems in a given moment of time, that is, measurable properties. Events represents the changes in these properties. These events will be selected as causes and consequences. This way we are building an expressive language to construct statements of a scientific value. Contrary, people often think about events that can either happen or not. In our system such events are easily modeled as Boolean properties (taking value of either 0 or 1).
2. There exist two seemingly equally justified points of view on the place of causal relations in the world.
• There are common causation laws that rule the world; then they are instantiated in different situations with different events.
• Causes and consequences are an indispensable part of the world, all generalizations are secondary.

Hume’s arguments

The arguments we are going to study can partly be traced to XVIII century! Meet David Hume, a philosopher.

Hume’s account on causes and consequences is highly empiric. What an observer (even ideal one) can observe about two events A and B is:

• A occured before B.
• A and B are close in space and time. Or they are connected by a chain of events, where each link is a relation between two events which satisfies these properties.
• When we observe something resembling A again, something resembling B appears as well.

It is suspicious that these three observations sum up everything an observer can notice — even an ideal observer. There is no way this information might be of a foundation for a strict causal relation as we usually imagine it.

Adding causality changes absolutely nothing here, because it does not give us anything observable.

In fact the pattern above can describe many events not necessarily connected in any way. Hume’s opinion is a great starting point, but now let us also address some problems about it.

Three properties of (A,B) pinpointed above occur not only in cases where we want to establish the relation, but also:

1. When A and B have a common cause
2. Just by coincidence
3. By preemption. It means that there is an event C that occurred before A and would have caused B anyway.

These three major problems are addressed differently depending on how one sees causation in general, which features of the cause-consequence pair are really key. Let’s now speak about those ways.

Sufficiency

To this day people tend to think about causation as a deterministic beast. Our knowledge about micro world, however, contradicts it (as many things about micro world contradict common sense). Contrary to some traditional views on causation based on necessity of the cause to bring about its effect, their modern counterparts fit mostly in two categories:

1. Those who base on regular occurrences of A followed by B.
2. Those who reconstruct the events of real world in the other, purely logical world.

Let’s take a closer look at the second point. In order to construct our logical structures we can use certain sets of rules (they can represent f.e. the laws of nature) of form:

Here for an event a the expression  F a will be substituted by an event instance; G a  is the consequence. As for the arrow, we can substitute it for either a material conditional (think simple implication) or something stronger like a subjunctive conditional (“If Oswald hadn’t killed Kennedy, someone else would have”).

Necessity

You know, sufficiency is not sufficient for causation. Even if we stick to determinism, the overdetermination (multiple causes) alone is a valid reason to question it. Let us say, A and B can both equally cause C and they occurred simultaneously. What caused C?

Basing on sufficiency of cause we can deduce:

• If A was the cause, then C would not have occurred without A. So A is not the cause.
• If B was the cause, then C would not have occurred without B. So B is not the cause.

Some people tend to think that it’s rather the cause’s necessity for the consequence that forms a casual relation between them. This way A caused B if and only if:

• A and B occurred.
• We can assert: “If A had not occurred, than B would not have occurred” (we will refer to it as sine qua non, because, well, its  what it is).

I guess it should look like: $A \land B \land ( \neg A \rightarrow \neg B)$.

It is but a foundation of a longer talk I intend to give in the next post based on arguments of Lewis and maybe Mackie (if I do have time for his book).

Probabilistic approach

Reasoning about sine qua non is not easy as long as you abandon determinism. Non-determinism, however, goes in pair with probabilities. The general idea of this approach is that causes increase probabilities of consequences in a large variety of contexts:

Here Z should take into account:

• Common causes of A and B.
• Preemptive causes of B.

This way preemption problem and common cause problem are addressed, and A and B become probabilistically independent.

The hard thing is to choose Z and a good definition of probability.

For example, take relative frequencies for probability. This way we should exclude from Z all causal consequences of B for which B is necessary. If we do not do it, we just get a wrong inequality 1 > 1 (check it!). However, look at it again: to calculate P we need exactly causal data for which we are building a theory! We have faced a vicious circle that is not easy to break.

Conclusion

As we see, there are loads of interesting subtleties when it comes to a closer study of causation. Numerous attempts were made to exile this relation completely from scientific thinking, but its roots are so strong it proved almost impossible to do. Next time I want to talk about Lewis’s very influential theory on causation, dated 1973, and then about his new causation theory of early 2000’s.