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Batter Up! Moneyball is SMART as Hell

Posted by on April 1st, 2013 with 0 Comments

I’m happy to say that baseball season is back.

I know that football is America’s most popular sport and I really enjoy it.
For guys my age, however, baseball is the game we learned and played with our dads.
It’s the game we grew up with. It’s the game we’ll grow old with.

Baseball is also heaven for geeks, because there is so much data.
162 games, three to four at-bats a game, hundreds of pitches; it’s a game made for spreadsheets.

If you saw the movie “Moneyball” – or read the book – you know what I’m talking about.
The book shows how the Oakland A’s overcame a cash disadvantage by being smarter than other teams; they took action on what businesses talk about every day, they ‘did more with less’.

They did this by betting that data would tell them more than their eyes did.
They did this by believing that ‘all-stars’ with high batting averages weren’t necessary great.
They did this by believing that players who ‘walked’ to first base a lot – historically the wimpiest offensive outcome in baseball, by the way – were inherently valuable.

“It’s looking at process rather than outcomes,” says Paul, “Too many people make decisions based on outcomes rather than process. “
– Michael Lewis, Moneyball (page 146)

Research has shown that process is a better predictor of success than success is.

In other words, a basketball player who makes 10 free throws today won’t necessarily outperform, in the future, another player who only made 3 free throws today.
Similarly, a baseball player who got 3 hits today isn’t necessarily going to consistently outperform another player who got no hits today.

Single game outcomes are subject to randomness and small sample size.
Process is not.
So, by watching the process used by a baseball (or basketball) player, you can better predict their future results.

Additionally, a player who uses success as the sole criteria of good performance will fall victim to superstitious behaviors – believing that they were successful because of (rather than in spite of) certain things they did.

  • For example, a player who gets three hits in a row by swinging at the first pitch might believe he or she should always swing at the first pitch. A player who cares about process would know better.

This idea is important because it also happens in the business world.
We fall in love with output, while forgetting about process.

An employee has a big success and we promote them.
An employee has a big failure and we demote them.

Companies spend millions of dollars developing processes and then forgive – even reward – the ‘winners’ who don’t follow those processes.
That’s not a recipe for success.

An example: a few years ago, I managed an engineering team.

  • Like most teams, we had a ‘star’.
    He was a hero who dramatically found problems no one had seen, boldly fixed those problems with risky and expensive solutions, and then loudly identified the departments that caused the problems.
    He was – essentially – Dirty Harry. He carried a big gun, blew up a lot of stuff, and always caught the bad guy. Customers and teammates loved him. He won multiple awards.
  • Also like most teams, we a ‘quiet contributor’.
    Some considered him the ‘worst’ on the team. He quietly did his work. When he ran into a problem, he asked for help, using our escalation processes. People thought he was ‘slow’, ‘over-reliant’, and ‘needed too much babysitting’.

Just one problem with that assessment:
The ‘star’ was taking three times as long and spending 15 times as much money as the ‘quiet guy’ to finish a project.

No one was looking at the data. They wanted to judge performance with their eyes. They wanted to applaud the ‘sexy’ outcomes rather than the ‘boring’ process that they spent so much to create.

This story does have a great ending.
I published the data and challenged the ‘star’ to deliver the same bottom-line results as the ‘quiet’ guy. Within six months, he did. He really was a great performer. He was just being rewarded for the wrong performance.

What Michael Lewis wrote about, and what I applied with my engineers is – in SMART as Hell language – a combination of Measurement and Relevance that starts with two questions:

  1. Can we find data that is more reliable than our eyes?
  2. Can we find data that relevant to predicting future performance?

If we can, we should, because that’s SMART as Hell.

SMART as Hell Exercise:

  1. Reflect: Which do you use for evaluation in your organization – outcomes or process?
  2. Consider: Do outcomes predict future success? (for example, does a big sale mean that a salesperson will continue to make big sales?)
  3. Consider: Does ‘good process’ predict future success in your organization? If not, why not? Are your processes broken?
  4. Ask: Does your organization reward success that is achieved with bad process? Why or why not?
  5. Identify: Areas where you are focusing on outcomes when you should be focusing on process?
  6. Shift: Your focus to the process. If you do that properly, the outcomes will follow.

Share your comments, questions, and reflections below.

 

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