Being in the Right Place at the Right Time in Career- A Statistical Perspective

In many coaching sessions, one of the frequent questions I get; is about “being in the right place at the right time” or about “luck” factor in professional career. This question always reminds me the photo in the cover image- a photo that has been captured by a blogger and posted in social media in my home country Turkey. Who was the lucky one in the photo: the cat or the photographer? Was it luck or was it being in the right place at the right time? Who benefitted from this “lucky event” at most, the cat, the photographer, or ING Bank? What is luck really? And the most important question back to the topic of this article: What is the real impact of being lucky in our careers or being in the right place at the right time? Can statistics help us to answer this question?  I will try to answer three questions in this article: 1- What is luck, chance, and probability? 2- How can we define luck with a statistical perspective? 3- What is the role of being “in the right place at the right time” in broader career success formula?

Let us start with the definition of luck: Oxford Dictionary defines it as the force that causes good or bad things to happen to people. There is a supernatural, external force implication here in this definition. What about chance? Chance is associated with probability. But probability is not luck. Probability is a calculated chance. And contrary to many people's belief, there is a negative relation between luck and probability. When you put in a lot of effort to grow and have the right skills, you are more likely (you are probably) to be advancing in your career. And if you do not, then you call yourself unlucky. On the other hand, if you do not put a lot of effort or you do not have the required skills/experiences, but you still advance in your career; then people call you lucky. Luck goes against probability. When something improbable happens, it is called luck. This is the first contradiction here: Luck cheapens and diminishes the value of challenging work, skills, experiences, and effort. 

Now look how statisticians see “luck.” The lottery is an effective way to illustrate this. Based on the probabilities involved, the expected value of the return on your money is less than the money you spend on lottery tickets. The expected value of a random variable can be thought of as the “average” value attained by the random variable. But we also know that that luck entails relying on results that are unexpected and rare. The closest statistical equivalent to luck that we can think of is error. Error is the difference between the expected value and an actual observed value. Most winnings in lottery will be close to the center but a few will be far in either direction. The distance between the expected value and the value that you personally achieve is your luck, or error. If you are lucky, you will be far to the right of the mean with a positive error. If you are unlucky, you will be to the left with a negative error. 

The next logical question is how we formulate “being in the right place at the right time” within the broader career success formula. Let's think what some critical inputs to a successful career growth are:  

  • Ability to spot a good problem/opportunity to solve or new value to provide (Ideas) 
  • Ability to execute on the problem/opportunity identified (Domain knowledge)  
  • Ability to make right decisions (Experience) 
  • Ability to communicate well and build partnerships (Teamwork) 
  • Ability to profit constructively from failures (Grit) 

You can further build on this list. All these points above are critical for success, this model simply says career growth is not about one thing, but it is about many things. What this means statistically is that the rate of success (career growth) for this individual is proportional to the “joint probability” of each item above. Here is the crucial point: Joint probability does not mean “addition”; joint probability means “multiplication” of random variables which results in the lognormal distribution of success. The conclusion is that if you are bad at just one factor, it sinks your overall success. This is innate in the multiplicative model.  (You cannot be successful if you are good at identifying opportunities but lacking skills to execute) Being moderately good at everything is better than great at some and terrible at others. One needs to be good at many things.  

And since success is multiplicative, the logs are additive and so the log of success follows the normal distribution, the log normal distribution. And the log normal distribution tells why the super successful ones are rare. Super success in career is not because of the error variable (luck) but because of lognormal distribution of multiplicative model of success.  

Some of you might still ask the factor of being in the right place at the right time. Right timing is discussed in detail in Malcolm Gladwell's book "Outliers". One of Gladwell's main points though is that while yes, timing matters a great deal, it is how one utilizes the opportunities which determine the difference between success and failure. This is consistent with our success definition before and the first item in the list: Ability to spot a good problem/opportunity to solve or new value to provide   

Whether in professional or entrepreneur world; opportunities and good problems are out there to be grabbed. Winners find them. 

To finish, luck seems to me to be like evolution, that is random mutation and non-random selection. Events around you may seem random, but the choices you make are not. If the choices, you make reduce your options, then the possibility of "luck" is reduced. If those choices increase your options, then the likelihood of "luck" increases. Even then luck is a matter of being able to recognize opportunity and being prepared to take advantage of changing circumstances. If we accept that each action of us becomes a cause that meets its effect eventually, then we can start understanding why we start to call many things “luck”. Our human brains have limited ability to handle a model with multiple variables. But with an almost infinite number of entities creating another near-infinite number of causes practically renders it impossible for us to co-relate any resultant event/ effect with its original cause. 

We are not the cat waiting to be photographed in front of the ING’s lion logo. We cannot be the ones who bet our careers in the error factor. We create our own luck. Let me finish with the quote of Roman philosopher Seneca who is the main idea of this article: “Luck is what happens when preparation meets opportunity.”