Finding A-Players

What is an A-Player?

I have been humbled by how difficult it can be to accurately predict if someone will end up being an A-Player before hiring them. I have hired people who I thought were going to be rockstars, who weren’t. And I have hired people who I didn’t think were rockstars, who ended up being being some of the best people I have ever worked with. Below is the framework I have been using to more accurately bet on people more likely to be A-Players. First: what do we even mean when we say “A-Player”?

An A-player is not someone who always succeeds. An A-Player is not someone who achieves successful outcomes by simply taking lots of risk. An A-player is not someone who loves failure. An A-Player is not someone who loves risk.

My definition of an A-Player is someone who will achieve extraordinary Risk-Adjusted Outcomes. They achieve the maximal outcome at the minimal level of risk:

For example, A-Players are the people that have the talent to turn a low risk (5% chance of failure) 1.2x opportunity into an equally low risk 2x opportunity. They are the people that can go after a big risky bet that could be a 1,000x return, and find a way to do it only a 80% chance of failure instead of a 95% chance of failure. Give an A-player a budget (i.e. hold the level of risk constant) and you will get a much better outcome than if you gave it to a B-Player. Give an A-player a goal (i.e. hold the outcome constant), and it will get done with a much lower probability of failure than if you asked a B-Player.

Misconceptions of A-Players

This definition of an A-Player has given me a helpful framework to evaluate candidates because it clarifies a few misconceptions that can trip hiring managers up (as they did me):

1. The defining feature of an A-player is not the actual outcomes they achieve but the quality of their decisions.
2. A-players do not love risk, in fact they hate risk. They are just unafraid to tolerate risk if it is necessary to go after something with an incredible potential outcome.
3. A-Players are associated with “high risk takers” not because they like risk, but because they succeed much more often than B-Players in high-risk situations.
4. A-players usually succeed, but still will often will fail when placed in higher risk situations. As long as the right decisions were made along the way, prior failure actual makes the candidate more appealing not less appealing, since it will make their future decisions even better.
5. A-Players are defined by their future performance, not their past performance. Hence, a candidate’s past experience is only relevant as a data point in assessing the likelihood of achieving the maximal risk-adjusted outcome in the future.

Evaluating Past Experiences

Given point (1) above, the quality of past decisions is a better predictor of future performance than the actual outcomes. As such, when evaluating a candidate, each of her experiences should be broken out based on both the outcome (success or failure) and the decision quality (great or average). Note that I do this in my head, I don’t literally place their resume into this grid.

• Hard Success: Hard Success is a successful outcome achieved through a series of great decisions. Some defining characteristics: (a) Hard Success is achieved at a minimal level of risk relative to the magnitude of the outcome. (b) Hard Success is achieved by thoughtfully navigating risk. (c) Hard Success is hard because the rigorous and continual management of the risk throughout an initiative is always extremely hard.
• Easy Success: Easy Success is a successful outcome achieved through a series of average decisions. Some defining characteristics: (a) Easy Success is achieved at an average level of risk relative to the magnitude of the outcome. (b) Easy Success is achieved by flippantly navigating risk. (c) Easy Success is easy because the risk will not have been rigorously managed throughout.
• Smart Failure: Smart Failure is a failed outcome that occurs despite a series of great decisions. Some defining characteristics: (a) Before failing the project will have an extraordinary risk-adjusted outcome. (b) Smart Failure occurs due to known and calculated risks. (c) Smart Failure leads to substantial learnings about the world, oneself, or others. And (d) Smart Failure is usually well contained to limit the blast radius of the damage.
• Dumb Failure: Dumb Failure is a failed outcome that occurs after a series of average or bad decisions. Some defining characteristics: (a) Dumb Failure often occurs due to risks that were unknown or uncalculated. (b) Dumb Failure often leads to few if any learnings about the world, oneself, or others. And (c) Dumb Failure is often poorly contained and can be substantially damaging to a company, product, or objective.

A-Players will have experiences with Hard Success and Smart Failure. B-Players have experiences with Easy Success and Dumb Failure.

How can I tell A-players from the rest?

When evaluating a candidate using this framework there will be four typical profiles that will emerge: A-Players, Safe-Players, B-Players, and Non-Players. Each of them can look very similar without this framework:

A-Players Will Have Had Smart Failures (Unlike Safe-Players)

Safe-Players are highly competent people who have succeeded by making exclusively low-risk decisions. Safe-Players have never experienced any real form of failure — as such, despite their competence are still average decision makers. When asked to make higher-risk decisions a Safe-Player’s likelihood of success is noticeably lower than that of an A-player, whose decision making has been refined by experiences with Smart Failure.

Smart Failure is a necessary part of becoming an A-player. While failure is never good, those who have failed intelligently in the past are the much more likely to be effective in the future.

A-Players Will Have Made Great Decisions (Unlike B-Players)

B-Players are those who make average decisions, and hence their work has average risk-adjusted outcomes. Since A-Players and B-Players will both have diverse experiences of failure and success, differentiating them is about dissecting the quality of their decisions.

The surest way to differentiate an A-Player from a B-Player is by dissecting whether their failures were Smart Failures or Dumb Failures. A-players will only have Smart Failures: they will take responsibility for their failures, will describe the assumptions they got wrong, and discuss how they managed the blast radius of the failure themselves. B-players will only have Dumb Failures: they will write off failures as unavoidable, will offer no clear learnings from the failure, and typically someone else will have swooped in to manage the blast radius from the failure.

A-Players Will Have Been In The Arena (Unlike Non-Players)

Non-Players are people people who have never been given the authority to succeed or fail themselves. They are the people who just followed orders, or have been in companies that provided them with little autonomy. Their experiences, both successes and failures, will be a consequence of other people’s decisions. Only asking candidates about outcomes with questions such as “how much was sold?” or “how much revenue did the product bring in?” can leave Non-Players looking dangerously similar to A-Players. Asking questions around decision quality will quickly reveal a Non-Player:

• “why did you make that decision?”
• “how did you know it was worth it?”
• “were you worried about the outcome?”

Note that experiences where a candidate did not have authority should not be held for or against him. The best approach to evaluating an apparent Non-Player is to search for a different domain where the candidate did have authority. Everyone, at some point in life, in some domain, has had the authority to succeed or fail. Whether it be in a college sport, an extra-curricular, or some side project. Even if authority was not given at work, you can catch a potential A-player by diving into more peripheral experiences where they did have the authority to succeed or fail.

In Conclusion

This framework has helped me catch several potential bad hires, since B-Players, Non-Players, and Safe-Players can all look very similar to A-Players. This framework has also helped me find several hidden A-Players, whose outcomes were nothing particularly impressive yet, but whose decision quality was beyond great.

The key part missing in this article is the harder question of how to create A-Players from Non-Players, B-Players, and Safe-Players. I haven’t fully thought through how growth and grooming fits into this framework yet. Will do some thinking and hopefully add to this in the coming months.

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