Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    unusual trading activity
    Signal Or Noise? A Decision Tree For Evaluating Unusual Trading Activity
    3 Min Read
    software developer using ai
    How Data Analytics Helps Developers Deliver Better Tech Services
    8 Min Read
    ai for stock trading
    Can Data Analytics Help Investors Outperform Warren Buffett
    9 Min Read
    media monitoring
    Signals In The Noise: Using Media Monitoring To Manage Negative Publicity
    5 Min Read
    data analytics
    How Data Analytics Can Help You Construct A Financial Weather Map
    4 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: On Moneyball and the Importance of Data
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Predictive Analytics > On Moneyball and the Importance of Data
AnalyticsPredictive Analytics

On Moneyball and the Importance of Data

MIKE20
MIKE20
3 Min Read
SHARE

Contents
  • Limited Means
  • Simon Says
  • Feedback

At the recent IBM Information On Demand Conference, keynote speaker Michael Lewis discussed some of the principles behind his best-selling book, Moneyball. In his superb and compelling text, Lewis describes how Billy Beane (general manager of the Oakland Athletics, an American baseball team), successfully used data-oriented strategies to compete against teams with payrolls two or three times as high.

More Read

Google+ and the Numerati
Crucial Benefits of Collecting and Analyzing Data for Modern Businesses
Are You an Analytics Champion? Prove It!
New Report on Decision Management Technologies – The Four Capabilities
Solving Supply Chain Risks [INFOGRAPHIC]

To be sure, Lewis and Beane (also in attendance) were not addressing the baseball intelligentsia at the conference. (OK, maybe a few wannabes and baseball geeks.) They were talking to information management (IM) professionals from a wide array of industries. Yet, the principles in the book could not have been more apropos to the audience:

  • Information matters now more than ever.
  • Information has never been easier to obtain and manipulate.
  • Any lead or advantage gleaned from the effective use of information is fleeting. It isn’t that hard to employ a copycat strategy.
  • People often refuse to adopt information-based strategies later in life because they know what’s best.

Lewis’ last point about change-averse baseball old-timers–and people in general–is particularly salient. For all of the pontificating I do on this site about data quality, intelligent information management, and the like, it all comes down to people. Human beings make decisions about what–and what not–to do.

Limited Means

Necessity is the mother of invention, as they say. It’s interesting to note that Beane had to rely upon unconventional means to field a competitive baseball team. In other words, he did not have the luxury of a big budget that would have allowed him to spend the big money on traditionally valued players. Instead, he had to develop and use new statistics, in many cases relying upon neglected but potentially valuable Sabermetrics. As Lewis explained at the conference and in the book, Beane had to look at the market for undervalued players and make intelligent bets. Paying $120 million to sign his best player at the time (Jason Giambi) was not an option. (Ten years ago, Giambi signed a 7-year $120-million deal with the New York Yankees.)

While the A’s have yet to win a championship on Beane’s watch, the team has been very competitive with limited means–especially in comparison to other small market clubs like the Pittsburgh Pirates. In fact, teams with far bigger payrolls have performed much worse.

Simon Says

The parallels between the A’s and most organizations could not be more stark. Few have unlimited means. Nearly all have to make tradeoffs between what is necessary and what is desirable. Learn from Lewis’ book and Beane’s approach to information management. Embrace data and new ways at looking at things. You may well be surprised with the results.

Feedback

What say you?

TAGGED:baseball statistics
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

0622cae5 f7d7 4f74 84b5 eabd1a823dca
How Data-Driven Grocery Recommendations Help Shoppers Eat Better With Less Effort
Big Data Exclusive
business recovering from data loss
How Data-Driven Businesses Protect MySQL Databases from Shutdown
Big Data Exclusive
ai driven task management
Reducing “Work About Work” with AI Task Managers
Artificial Intelligence Exclusive
data center uptime
Why Rodent-Resistant Conduits Are Critical for Data Center Uptime
Big Data Data Management Exclusive Risk Management

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

World Series Analytics

9 Min Read

Big Data Analytics Versus Your Own Lying Eyes

0 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai chatbot
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?