finding power law winners

Extracted Attributes

• Contrast

  Contrasts with the illiquidity and long return cycles (J curve) of traditional VC

• Primary Goal

  Identify companies yielding massive, compounding returns

• Return Magnitude

  Aims for 100x, 1,000x, or even 10,000x returns on investment

• Application Field

  Venture Capital

• Impact on Portfolio

  One single investment often yields returns larger than all other investments combined

• Strategy Implication

  Investing in more companies increases the chances of finding these rare winners

• Underlying Principle

  Power Law Distribution

Freddie Highmore

Alfred Thomas Highmore (born 14 February 1992) is an English actor. He is known for his starring roles beginning as a child, in the films Finding Neverland (2004), Charlie and the Chocolate Factory (2005), Arthur and the Invisibles (2006), August Rush (2007), The Spiderwick Chronicles (2008), and the voice of the titular robot boy in Astro Boy (2009). He won two consecutive Critics' Choice Movie Awards for Best Young Performer and received two Screen Actors Guild Award nominations. Highmore starred as Norman Bates in the drama-thriller series Bates Motel (2013–2017), for which he was nominated three times for the Critics' Choice Television Award for Best Actor in a Drama Series and won a People's Choice Award, and Dr. Shaun Murphy in the ABC drama series The Good Doctor (2017–2024), for which he also served as a producer and was nominated for the Golden Globe Award for Best Actor.

Web Search Results

• Power law in startup investing - Hustle Fund

  This is why accelerators like Y Combinator keep increasing their batch sizes and investments. With great deal flow (and that part is key), investing in more companies actually increases their chances of finding those rare power law winners. Each massive success more than compensates for all the failures combined. ## What It Means to Be a Power Law Winner [...] When VCs talk about finding winners, they're not looking for 2x or 3x returns. They're hunting for companies that can deliver 100x, 1,000x, or even 10,000x returns. Let's put some numbers to this: If you invest at a $5 million valuation, a "100x winner" would exit at $500 million. But in reality, you'll face dilution through future funding rounds – about half your ownership might be diluted away. So you're really looking for billion-dollar outcomes to achieve that 100x net multiple. [...] Adding more investments doesn't dilute your returns – it actually increases your chances of finding that one extraordinary company that returns 100x or more. When most people see a portfolio with one massive winner and nine failures, they think, "What a shame – if only the other nine had succeeded too!" But experienced VCs think, "Let's find more companies like that winner, even if it means having more failures too."

• Power Law: A Pattern Behind Extreme Events

  There is also the benefit of understanding nonlinear growth and compounding effects. Power laws help explain why some people, companies, or nodes in a network can become the most powerful over time and why these "winner-takes-most" situations are not just unusual but normal in these kinds of systems. [...] The scaling exponent, which is usually written as 𝛼, is the first thing you need to model a power law distribution correctly. Maximum Likelihood Estimation (MLE) is one of the most common ways to get an estimate of 𝛼. It is a way to find the parameter values that make the observed data most likely in all of statistics. For continuous data with a known minimum value 𝑥min, this is how it works: [...] data, find the likelihoods, and then compare them. You have statistical support for the power law model if its likelihood is much higher.

• 3.1 - The Power Law in VC - VC Lab

  Venture capitalists and investors are always on the hunt for opportunities that embody the Power Law. To identify such opportunities, it’s essential to understand the typical characteristics of startups and markets that align with the Power Law: [...] Today, the Power Law is a fundamental principle in venture capital, shaping the way investments are made and portfolios are constructed. It has become an accepted truth that a small number of investments will yield returns far greater than the rest, often exceeding them combined. This understanding of the Power Law guides VCs in their search for the next breakout success, the one investment that will validate and overshadow all others. The Power Law, thus, continues to be a driving force in the [...] The Power Law in venture capital (VC) is a principle where one single investment yields returns larger than all other investments combined, often by orders of magnitude. The entire global venture capital industry’s success often hinges on a few companies that rise to prominence, overshadowing their peers and redefining markets. This article delves deep into the origins, implications, and strategies surrounding the Power Law in venture capital. The Power Law

• Power law

  {\displaystyle x\geq x_{\min }} {\displaystyle {\frac {\alpha -1}{x_{\min }}}} {\displaystyle x_{\min }} {\displaystyle {\mathcal {L}}(\alpha )=\log \prod _{i=1}^{n}{\frac {\alpha -1}{x_{\min }}}\left({\frac {x_{i}}{x_{\min }}}\right)^{-\alpha }} The maximum of this likelihood is found by differentiating with respect to parameter α {\displaystyle \alpha } {\displaystyle \alpha }, setting the result equal to zero. Upon rearrangement, this yields the estimator equation: [...] In general, power-law distributions are plotted on doubly logarithmic axes, which emphasizes the upper tail region. The most convenient way to do this is via the (complementary) cumulative distribution "Cumulative distribution function") (ccdf) that is, the survival function, P ( x ) = P r ( X > x ) {\displaystyle P(x)=\mathrm {Pr} (X>x)} {\displaystyle P(x)=\mathrm {Pr} (X>x)}, [...] On the other hand, in its version for identifying power-law probability distributions, the mean residual life plot consists of first log-transforming the data, and then plotting the average of those log-transformed data that are higher than the i-th order statistic versus the i-th order statistic, for i = 1, ..., n, where n is the size of the random sample. If the resultant scatterplot suggests that the plotted points tend to stabilize about a horizontal straight line, then a power-law

• Intro to Power Law - Risto Hinno - Medium

  `a` is a constant `x` is input a variable (for example income of person) `k` is also constant, exponent of a power law Example value is very simple to calculation: assume k=2, a=1 and x=3, then: 1\3^(-2), which is 1/9 (roughly 0.11111). Function outputs probability. In essence this function is very simple: we take input variable x into exponent -k and multiply with a. But simple things might have unexpected behavior. [...] One example where choosing wrong distribution for risk modelling blew up a company is Long-Term Capital Management (LTCM) (source). It was founded in 1994 and its board of directors included winners of Nobel Prize for Economics. Long story short: they used Gaussian distribution for predicting market fluctuations. Under Gaussian distribution chances of event happening that is 10 standard deviations from the mean is 1.31x10⁻²³, (very very rare event). Under power law with exponent of 2, this [...] Zipf’s law refers to the fact that in many numeric datasets the rank-frequency distribution has inverse relation (source). For example words in larger corpus: few words have very high frequency (low rank) but there are many words with lower frequency but higher rank. More frequent words tend to take most of the frequency probability mass. From the plot we could see that there are quite a lot of words with low frequency (upper left corner of the plot). On the other hand there tends to be less