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
    composable analytics
    How Composable Analytics Unlocks Modular Agility for Data Teams
    9 Min Read
    data mining to find the right poly bag makers
    Using Data Analytics to Choose the Best Poly Mailer Bags
    12 Min Read
    data analytics for pharmacy trends
    How Data Analytics Is Tracking Trends in the Pharmacy Industry
    5 Min Read
    car expense data analytics
    Data Analytics for Smarter Vehicle Expense Management
    10 Min Read
    image fx (60)
    Data Analytics Driving the Modern E-commerce Warehouse
    13 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: The Human Factor Continually Confounds Probability Models
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 > Modeling > The Human Factor Continually Confounds Probability Models
ExclusiveModelingPredictive AnalyticsStatistics

The Human Factor Continually Confounds Probability Models

paulbarsch
paulbarsch
3 Min Read
SHARE

With four weeks to go in the 2011 Major League Baseball season, the probability of the Boston Red Sox of making the playoffs was 99.6%. And most of us know the story; in one of the biggest collapses in baseball history, the Red Sox tanked a nine game lead and served the wild card slot to the Tampa Bay Rays. In creating “one for the record books”, the 2011 Red Sox show us that the human factor continually confounds probability models.

With four weeks to go in the 2011 Major League Baseball season, the probability of the Boston Red Sox of making the playoffs was 99.6%. And most of us know the story; in one of the biggest collapses in baseball history, the Red Sox tanked a nine game lead and served the wild card slot to the Tampa Bay Rays. In creating “one for the record books”, the 2011 Red Sox show us that the human factor continually confounds probability models.

Some things aren’t supposed to happen. The 2011 Boston Red Sox certainly should not have missed the playoffs with a nine game lead, and the 1995 Anaheim Angels should not have finished their year 12-26 (losing a nine game lead and missing the playoffs). Moreover, probability models said the stock market (DJIA) should not have lost 54% of its value in the 2008 “Great Recession”.

More Read

By collecting previous crime statistics and external factors —…
eCommerce Brands Use Big Data for Logistics and Fulfillment Warehouses Protection
An Overview of Predictive Analytics World
Redefining Business Intelligence: Don’t play the game, change the game!
Interview: Françoise Soulie Fogelman, KXEN

There’s definitely a danger in too much reliance on normal distribution probability models, especially when humans are concerned says Financial Times writer John Authers. 

Studying the 2011 Boston Red Sox, Authers suggests the team may have been overconfident in statistics since few teams in baseball history had collapsed with such a lead.  

Authers also believes bell curve probabilistic models would not have been a reliable indicator of possible failure because such models assume event independence where one event should not affect another. But those who follow sports understand the concept of “momentum in a game”, or even from game-to-game where a team can feed off past success to gain confidence.

In reference to the 2008 market crash, Steven Solmonson, head of Park Place Capital Ltd said; “Not in a million years would we have expected this gyration to be as vicious and enduring as it has been.”  And I’m sure that Boston Red Sox fans didn’t believe their team could lose a significant lead over the Tampa Bay Rays with just a few games left in the season.

Whenever humans are involved, the lesson is clear: don’t get over confident in normal distribution probability models. Next thing you know, you might get slapped (or worse) by the fat tail.

 

 

TAGGED:bayesianbell curveprobabilitystatistics
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

student learning AI
Advanced Degrees Still Matter in an AI-Driven Job Market
Artificial Intelligence Exclusive
mobile device farm
How Mobile Device Farms Strengthen Big Data Workflows
Big Data Exclusive
composable analytics
How Composable Analytics Unlocks Modular Agility for Data Teams
Analytics Big Data Exclusive
fintech startups
Why Fintech Start-Ups Struggle To Secure The Funding They Need
Infographic News

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Why Can’t We Just Use Prediction Markets?

6 Min Read

Is Your eCommerce Website Suffering From Usability Issues?

8 Min Read

“Average” Statistics that Bruise Our Ears

4 Min Read

The difference between Statistics and Machine Learning

3 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
AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive

Quick Link

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

Sign in to your account

Username or Email Address
Password

Lost your password?