SlideShare a Scribd company logo
1 of 2
Download to read offline
VIEWPOINT
BCS Rankings and New Product
Development Scoring Systems
A computer does not adequately account for heart and character

BY BILL POSTON


So my Longhorns lost the Bowl Championship Series (BCS) National Championship
game to a team named after an algae bloom. Not that I am bitter, but I believe
that Sandra Bullock deserves to win Best Actress for pretending to be all hot and
bothered when meeting Nick Saban in The Blind Side. I don’t like that guy and
neither does she.

While I am obviously disappointed with the outcome, the BCS pairings were
mostly free from controversy this year. The convoluted system that has been
continuously tweaked since its inception in 1998 seemed to work. The complex
algorithm that underlies the ranking methodology uses a series of Borda counts
and combines the results of a sports writers poll, a coaches poll, and an average of      The problem occurs
six different computer scoring systems to arrive at its overall rankings (Borda was    when these algorithm-
an eighteenth century French mathematician who didn’t know anything about                      driven systems
college football). Football people don’t really trust computers – or the French – so     become the de facto
the human polls are now weighted more heavily. But the only reason we have              decision making tool.
computer scoring systems is because we were, once upon a time, not happy with
                                                                                       Even the BCS gives the
the consistency of the results of the human polls. We have the LSU version of Nick
Saban to thank for that.                                                                  computer only one-
                                                                                          third of the vote. If
The benefit of the computer ranking systems is that they use objective criteria to     Excel can tell us which
evaluate the performance of teams and that they are not subject to “reputation         new product concepts
bias” or any other bad ol’ form of subjective selection by mere human beings. I            we should invest in
think that having six different computer models account for a third of the input is     then why do we need
about right for college football and they appear to be doing a decent job of                 Vice Presidents?
predicting success. No one really thought that Cincinnati was going to beat
Florida. Did they?

Predicting success is also the objective of scoring systems used by companies in
the new product development process. I have worked with dozens of clients that
have extremely sophisticated systems for scoring and ranking new product
concepts. These systems identify characteristics of new product projects that are
historically correlated with winners and use these measures as predictors of
success.
VIEWPOINT
I like these systems and believe that they are, if properly constructed, a valuable
input into the project selection and portfolio prioritization process. The problem
occurs when these algorithm-driven systems become the de facto decision making
tool. Even the BCS gives the computer only one-third of the vote. If Excel can tell
us which new product concepts we should invest in then why do we need Vice
Presidents?

When we are talking about innovation we need to first consider that we are
dealing with tremendous amounts of uncertainty. No matter how hard we try to
quantify variables and assign probabilities to projections, we are still dealing with      Give the computer a
forecasts and estimates. Just because we put these numbers into a fancy                    voice but don’t let it
algorithm doesn’t make them true. We can get better over time by tweaking the
                                                                                             set your priorities.
algorithm based on demonstrated in-market success, but we will always have to
                                                                                           The best models out
deal with – and embrace – uncertainty.
                                                                                          there are not perfect
                                                                                          predictors of success
That is where management judgment comes into play. There is no good way to
automate project selection and prioritization. Scoring systems should be an input         and cannot replace a
into decision makers that aggressively question the assumptions behind the model         seasoned executive’s
and parse the variables that go into it. In my opinion, an informed executive with      knowledge of, and feel
a good gut beats a BASS (big-assed spreadsheet) every day. Give the computer a           for, the marketplace.
voice but don’t let it set your priorities. The best models out there are not perfect
predictors of success and cannot replace a seasoned executive’s knowledge of,
and feel for, the marketplace.

So let’s make sure that our scoring models are used properly and improved over
time. The BCS ranking system will continue to evolve and I’ll eventually get over
the Longhorns’ loss. The computer does not adequately account for heart and
character. If it did, Texas would be the clear #1. Hook ’em Horns!




                     KALYPSO CONTACT
                     Bill Poston, Managing Partner
                     bill.poston@kalypso.com

                     www.kalypso.com

More Related Content

Similar to BCS and NPD Scoring

Bundledarrows160 bit.ly/teamcaptainsguild
Bundledarrows160 bit.ly/teamcaptainsguildBundledarrows160 bit.ly/teamcaptainsguild
Bundledarrows160 bit.ly/teamcaptainsguildshadowboxingtv
 
Teaching Computers to Think Like Decision Makers: the next revolution in the ...
Teaching Computers to Think Like Decision Makers: the next revolution in the ...Teaching Computers to Think Like Decision Makers: the next revolution in the ...
Teaching Computers to Think Like Decision Makers: the next revolution in the ...Lorien Pratt
 
Design vs Data: Battle Royale (UX+Data Meetup)
Design vs Data: Battle Royale (UX+Data Meetup)Design vs Data: Battle Royale (UX+Data Meetup)
Design vs Data: Battle Royale (UX+Data Meetup)Jess Dale
 
How to select and Implement an ERP System
How to select and Implement an ERP SystemHow to select and Implement an ERP System
How to select and Implement an ERP SystemShalini Saha
 
How artificial intelligence will change the future of marketing_.pdf
How artificial intelligence will change the future of marketing_.pdfHow artificial intelligence will change the future of marketing_.pdf
How artificial intelligence will change the future of marketing_.pdfOnlinegoalandstrategy
 
Machine learning for Marketers
Machine learning for MarketersMachine learning for Marketers
Machine learning for MarketersFullstaak
 
Seven things CIOs and software buyers should know about artificial intelligence
Seven things CIOs and software buyers should know about artificial intelligenceSeven things CIOs and software buyers should know about artificial intelligence
Seven things CIOs and software buyers should know about artificial intelligenceAndy Mura
 
Data Analytics with Managerial Applications Internship
Data Analytics with Managerial Applications InternshipData Analytics with Managerial Applications Internship
Data Analytics with Managerial Applications InternshipJahanvi Khedwal
 
People Analytics: Creating The Ultimate Workforce
People Analytics: Creating The Ultimate WorkforcePeople Analytics: Creating The Ultimate Workforce
People Analytics: Creating The Ultimate WorkforceCenterfor HCI
 
Performance Appraisal in IT industry
Performance Appraisal in IT industryPerformance Appraisal in IT industry
Performance Appraisal in IT industrySudip Paudel
 
Analysis of "Big data hype and reality - Gregory Piatetsky-Shapiro"
Analysis of "Big data hype and reality - Gregory Piatetsky-Shapiro"Analysis of "Big data hype and reality - Gregory Piatetsky-Shapiro"
Analysis of "Big data hype and reality - Gregory Piatetsky-Shapiro"Dheepika Chokkalingam
 
The system, the goal, the goal tree and validating the measuring system in th...
The system, the goal, the goal tree and validating the measuring system in th...The system, the goal, the goal tree and validating the measuring system in th...
The system, the goal, the goal tree and validating the measuring system in th...Ricardo Anselmo de Castro
 
Einstein Analytics Prediction Builder
Einstein Analytics Prediction BuilderEinstein Analytics Prediction Builder
Einstein Analytics Prediction Builderrikkehovgaard
 
Guide to Predictive Lead Scoring
Guide to Predictive Lead ScoringGuide to Predictive Lead Scoring
Guide to Predictive Lead ScoringEvgeny Tsarkov
 
Guide to Predictive Lead Scoring
Guide to Predictive Lead ScoringGuide to Predictive Lead Scoring
Guide to Predictive Lead ScoringMohamed Mahdy
 
Talent Analytics: A Systems Perspective
Talent Analytics:  A Systems PerspectiveTalent Analytics:  A Systems Perspective
Talent Analytics: A Systems PerspectiveSharad Verma
 
2010 Wagner pk
2010 Wagner pk2010 Wagner pk
2010 Wagner pkWCET
 
WhoKnows People Data Analytics for Human Resources
WhoKnows People Data Analytics for Human ResourcesWhoKnows People Data Analytics for Human Resources
WhoKnows People Data Analytics for Human ResourcesJoanne Hernon
 

Similar to BCS and NPD Scoring (20)

Bundledarrows160 bit.ly/teamcaptainsguild
Bundledarrows160 bit.ly/teamcaptainsguildBundledarrows160 bit.ly/teamcaptainsguild
Bundledarrows160 bit.ly/teamcaptainsguild
 
Teaching Computers to Think Like Decision Makers: the next revolution in the ...
Teaching Computers to Think Like Decision Makers: the next revolution in the ...Teaching Computers to Think Like Decision Makers: the next revolution in the ...
Teaching Computers to Think Like Decision Makers: the next revolution in the ...
 
Systems development life cycle
Systems development life cycleSystems development life cycle
Systems development life cycle
 
Design vs Data: Battle Royale (UX+Data Meetup)
Design vs Data: Battle Royale (UX+Data Meetup)Design vs Data: Battle Royale (UX+Data Meetup)
Design vs Data: Battle Royale (UX+Data Meetup)
 
How to select and Implement an ERP System
How to select and Implement an ERP SystemHow to select and Implement an ERP System
How to select and Implement an ERP System
 
How artificial intelligence will change the future of marketing_.pdf
How artificial intelligence will change the future of marketing_.pdfHow artificial intelligence will change the future of marketing_.pdf
How artificial intelligence will change the future of marketing_.pdf
 
Machine learning for Marketers
Machine learning for MarketersMachine learning for Marketers
Machine learning for Marketers
 
Seven things CIOs and software buyers should know about artificial intelligence
Seven things CIOs and software buyers should know about artificial intelligenceSeven things CIOs and software buyers should know about artificial intelligence
Seven things CIOs and software buyers should know about artificial intelligence
 
Data Analytics with Managerial Applications Internship
Data Analytics with Managerial Applications InternshipData Analytics with Managerial Applications Internship
Data Analytics with Managerial Applications Internship
 
People Analytics: Creating The Ultimate Workforce
People Analytics: Creating The Ultimate WorkforcePeople Analytics: Creating The Ultimate Workforce
People Analytics: Creating The Ultimate Workforce
 
Performance Appraisal in IT industry
Performance Appraisal in IT industryPerformance Appraisal in IT industry
Performance Appraisal in IT industry
 
Analysis of "Big data hype and reality - Gregory Piatetsky-Shapiro"
Analysis of "Big data hype and reality - Gregory Piatetsky-Shapiro"Analysis of "Big data hype and reality - Gregory Piatetsky-Shapiro"
Analysis of "Big data hype and reality - Gregory Piatetsky-Shapiro"
 
The system, the goal, the goal tree and validating the measuring system in th...
The system, the goal, the goal tree and validating the measuring system in th...The system, the goal, the goal tree and validating the measuring system in th...
The system, the goal, the goal tree and validating the measuring system in th...
 
Einstein Analytics Prediction Builder
Einstein Analytics Prediction BuilderEinstein Analytics Prediction Builder
Einstein Analytics Prediction Builder
 
Final pm ppt.pptx
Final pm ppt.pptxFinal pm ppt.pptx
Final pm ppt.pptx
 
Guide to Predictive Lead Scoring
Guide to Predictive Lead ScoringGuide to Predictive Lead Scoring
Guide to Predictive Lead Scoring
 
Guide to Predictive Lead Scoring
Guide to Predictive Lead ScoringGuide to Predictive Lead Scoring
Guide to Predictive Lead Scoring
 
Talent Analytics: A Systems Perspective
Talent Analytics:  A Systems PerspectiveTalent Analytics:  A Systems Perspective
Talent Analytics: A Systems Perspective
 
2010 Wagner pk
2010 Wagner pk2010 Wagner pk
2010 Wagner pk
 
WhoKnows People Data Analytics for Human Resources
WhoKnows People Data Analytics for Human ResourcesWhoKnows People Data Analytics for Human Resources
WhoKnows People Data Analytics for Human Resources
 

BCS and NPD Scoring

  • 1. VIEWPOINT BCS Rankings and New Product Development Scoring Systems A computer does not adequately account for heart and character BY BILL POSTON So my Longhorns lost the Bowl Championship Series (BCS) National Championship game to a team named after an algae bloom. Not that I am bitter, but I believe that Sandra Bullock deserves to win Best Actress for pretending to be all hot and bothered when meeting Nick Saban in The Blind Side. I don’t like that guy and neither does she. While I am obviously disappointed with the outcome, the BCS pairings were mostly free from controversy this year. The convoluted system that has been continuously tweaked since its inception in 1998 seemed to work. The complex algorithm that underlies the ranking methodology uses a series of Borda counts and combines the results of a sports writers poll, a coaches poll, and an average of The problem occurs six different computer scoring systems to arrive at its overall rankings (Borda was when these algorithm- an eighteenth century French mathematician who didn’t know anything about driven systems college football). Football people don’t really trust computers – or the French – so become the de facto the human polls are now weighted more heavily. But the only reason we have decision making tool. computer scoring systems is because we were, once upon a time, not happy with Even the BCS gives the the consistency of the results of the human polls. We have the LSU version of Nick Saban to thank for that. computer only one- third of the vote. If The benefit of the computer ranking systems is that they use objective criteria to Excel can tell us which evaluate the performance of teams and that they are not subject to “reputation new product concepts bias” or any other bad ol’ form of subjective selection by mere human beings. I we should invest in think that having six different computer models account for a third of the input is then why do we need about right for college football and they appear to be doing a decent job of Vice Presidents? predicting success. No one really thought that Cincinnati was going to beat Florida. Did they? Predicting success is also the objective of scoring systems used by companies in the new product development process. I have worked with dozens of clients that have extremely sophisticated systems for scoring and ranking new product concepts. These systems identify characteristics of new product projects that are historically correlated with winners and use these measures as predictors of success.
  • 2. VIEWPOINT I like these systems and believe that they are, if properly constructed, a valuable input into the project selection and portfolio prioritization process. The problem occurs when these algorithm-driven systems become the de facto decision making tool. Even the BCS gives the computer only one-third of the vote. If Excel can tell us which new product concepts we should invest in then why do we need Vice Presidents? When we are talking about innovation we need to first consider that we are dealing with tremendous amounts of uncertainty. No matter how hard we try to quantify variables and assign probabilities to projections, we are still dealing with Give the computer a forecasts and estimates. Just because we put these numbers into a fancy voice but don’t let it algorithm doesn’t make them true. We can get better over time by tweaking the set your priorities. algorithm based on demonstrated in-market success, but we will always have to The best models out deal with – and embrace – uncertainty. there are not perfect predictors of success That is where management judgment comes into play. There is no good way to automate project selection and prioritization. Scoring systems should be an input and cannot replace a into decision makers that aggressively question the assumptions behind the model seasoned executive’s and parse the variables that go into it. In my opinion, an informed executive with knowledge of, and feel a good gut beats a BASS (big-assed spreadsheet) every day. Give the computer a for, the marketplace. voice but don’t let it set your priorities. The best models out there are not perfect predictors of success and cannot replace a seasoned executive’s knowledge of, and feel for, the marketplace. So let’s make sure that our scoring models are used properly and improved over time. The BCS ranking system will continue to evolve and I’ll eventually get over the Longhorns’ loss. The computer does not adequately account for heart and character. If it did, Texas would be the clear #1. Hook ’em Horns! KALYPSO CONTACT Bill Poston, Managing Partner bill.poston@kalypso.com www.kalypso.com