4 Insights you can gather from Michael Lewis’ Moneyball as a Data Analyst

Moneyball book cover

If you follow me on LinkedIn you already know that I’m, or actually was, reading Moneyball. Just finished the book today, and so I thought it would be fun to share what I got from this book using my perspective, in other words, a data analyst’s perspective.

It’s no surprise that this book is, or was, a best seller. It’s easy, and compelling, to read, the story is engaging for those who likes baseball, or for those who likes “think out of the box” approaches, or for those who likes to see underdogs beating favorites, or if you like a story of someone that became relevant for something. With that being said, Moneyball do not provides any hard data or numbers, it’s not a math or statistics book, it’s just a story book, but you one can gather reliable insights by reading it.

So here are four insights gathered from Moneyball:

  1. It’s hard to challenge common wisdom

Billy Beane had to went from a becoming next baseball super start to a baseball failure to understand that the way things were going were wrong.

That being said, it’s easy to find people saying all over that we should think out of the box, but in truth they expect that you think inside their sandbox, and if you challenge their common sense they call you wrong or even worse. Besides, just thinking is something, acting is something else. If you are thinking differently and you start acting differently it may that a while until you get people to see that your way of doing it is better. Actually, you can find even today people that still do not take numbers and stats into account, because “they know better”.

  1. It takes time to build your data stats properly

The guy who really put things together were Billy James, who first article was published in 1977, and back then it wasn’t easy to gather all data, and not all data were properly gathered, in fact, gather, prepare and figure out which data you actually need is a on-going process that never ends. In fact, 1977 studies from James just started to being used by Billy Beane much later in 1997. So it was a 20 years apart thing, and furthermore, after all those years with baseball stats being used since 97 by Billy Beane’s team, they still found new and improved way to use and gather new data in 2001 by using Voros McCracken new way to measure defense data.

So, knowing that, it is important, specially if you are a leader, a manager or a director, to understand that data literacy takes time, a lot of time, maybe not 30 years, but it’s not a 6 month project that will get all the sweet sweet insights you need – it might give you some, but understand that sometimes what you really need was not even thought about.

  1. It’s hard to trust data when they tell you something you disagree

Moneyball introduces us some players that are only playing baseball major league because Billy Beane and Paul DePodesta stats sheet. All other teams thinks that Billy’s team is going to loose, but they end up winning many games, because stats. It doesn’t matter if the player looks good, or if they run fast, or if they thrown a ball 95mph, all that matters is if, by using baseball metrics they can deliver wins, and that is what happens. Many times players are doing something that everyone else think they should do differently, but common wisdom is usually wrong.

Have you ever seen something like this happen? When a manager check it’s report that says that he should be selling more in this area and not that, or that some product should be selling more than other, but he insists in doing something different from what the data is telling him to? It’s common and very similar to our point 1, because people often think they know better.

  1. The luck factor

In a baseball you can have many games throughout the season, but only a few in the playoffs. What happens is, the way your data applies changes, because your sample changes. That is why during a season Billy’s team would end up high in the ranking, but usually lost in the playoffs.

That shows us that not every kpi, metric, should be applied to the same sample, or considered in the same context. Be aware of what you are measuring before taking a metric in account, you may end up with a correct number for a wrong situation.

Conclusion

There are many more scenarios one can gather from reading this book, those listed here are just some that come from the top of my head as I’m writing this blog post. Something else worth noticing is how Billy dealt with this team members, it’s a long story, but follows pretty much the idea you can gather by reading another book, First, break all the rules – which I also highly recommend, specially if you are in a lead position.

You can buy Moneyball here, I don’t make a single dollar by recommending it, but I’m doing it anyway 🙂

Publicado por Pedro Carvalho

Apaixonado por análise de dados e Power BI

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