Using Data as a Predictor of Sports Success

There’s a huge celebration going on this week – a celebration of decision making. You see the NFL Draft starts Thursday (4/28/11) and runs through Saturday (4/30/11) and fans tune in to see who their team selects. No games are played, just people’s names being called.

Why do we care? The simple answer is hope. We’ve entrusted the future of our favorite teams to a room full of guys with spreadsheets. We want to believe they have the magic formula for selecting the players who succeed in the NFL. They’ve studied film, measured height, weight, speed, interviewed the candidates, and surveyed other experts. They’ve quantified all these inputs and ranked the candidates. Most of the time they tier them for purposes of trading up or down. Teams win Super Bowls because of these three days.

It’s a lot of data and yet every year mistakes are made. As a General Manager, the person ultimately making the decision, you need the hits to be proportionally more successful than your misses. And you need to learn from your data year over year to see which inputs pan out and which ones do not. From there you can use heuristics to simplify the ranking order and reduce the risk of missing on a selection.

Below are two videos. One is from the Sloan Sports Conference and it features Peter Tingling. I’m a fan of Mr. Tingling and his company, Octothorpe Software (this is not a paid endorsement). Peter provides a presentation about how how successful NHL drafts are.

The second video is from the most famous sixth round pick ever – Tom Brady. He is your classic case of not using the data correctly.

http://www.kaltura.com/index.php/kwidget/wid/_203822/uiconf_id/1898102/entry_id/1_bukfpvkn/

Simple Heuristics That Make Us Smart – A Book Review

Quick Take: Simple Heuristics That Make Us Smart is a collection of academia based essays proving the comparative value of decision making based on good enough information. The examples and anecdotes are good, but there is complex math to wade through. It isn’t a leisure read. However, each section can be consumed on it’s own. If you’re a student of decision making, whether it’s group dynamics or individual situations, then this book is a good heuristics reference.

Detail Review: Many of us have a comfortable chair which serves as our place to relax.
Its great for 40 winks. But why do we relish peacefully falling asleep
in a chair? Most of the time it’s because we are mentally exhausted. Everyday we are faced with an ever changing list of choices to make and each has a list of known variables and all kinds of factors which are unknown. We try to streamline choices that have worked so we don’t need to concentrate on it. I take the same route to work everyday even though there are probably another ten ways to get there, for instance.

I wish I had a computer in my head to compute all the different inputs into making a decision. I could continually collect data and analyze it practically to a 100% decision certainty. But I don’t have a computer or unlimited time, instead I rely on heuristics. Heuristics are simple methods for using particular cues and constraints to make a choice. Gerd Gigerenzer, Peter M. Todd, and The ABC Research Group authored this tome as a study of how accurate specific heuristics are.

Here are a few heuristics covered in the book:

Recognition
Definition – If one of two objects is recognized and the other is not, then infer that the recognized object has the higher value with respect to the criterion.
Example – If I ask 100 Americans which city in Germany is more populated Berlin or Saarburg? The results will be close to 100% correct – Berlin is more populated. Of the 100 people few, if any, will recognize Saarburg as a city, but practically all of them will have heard of Berlin. Because of that recognition they will answer Berlin even though they know little about the actual number of people who live in either city.

Take the Best
Definition – When making a judgment based on multiple cues, the criterion are tried one at a time according to their cue validity, and a decision is made based on the first criterion which discriminates between the alternatives.
Example – Suppose we ask the question about population again, but instead of Saarburg we use Frankfurt. Berlin and Frankfurt are both recognizable so we must use other reasons to discriminate population. We pose a list of usual indicators of large populations – historical relevance, it’s a capital, tourism, sports teams, and so on. From that list we rank the list based on which ones usually are more of an indication of population and try to separate the two. We compare Frankfurt and Berlin for tourism and realize that Berlin is much more of a destination than Frankfurt is. We stop there and don’t review the other reasons. We take the best separator – tourism – and decide to invest no more time in evaluating. Berlin is the answer.

Take the Last
Definition – When making a judgment based on multiple cues, the criterion are sorted according to what worked last time. It uses memory of prior problem solving instances and works from what was successful before.
Example – I’m now comparing Frankfurt and Munich in population. I’ve heard of both so I can’t use Recognition. I use Tourism as the candidate since it worked with Berlin and Frankfurt. This time I go with Munich because they’ve hosted an Olympics and is more of a destination than Frankfurt. This answer is correct and time and energy was saved because I didn’t need to sort through all the other criteria.

In addition to those there are:

  • Franklin’s Rule – calculates for each alternative the sum of the cue values multiplied by the corresponding cue weights (validaties) and selects the alternative with the highest score.
  • Dawes’s Rule – calculates for each alternative the sum of the cue values (multiplied by a unit weight of 1) and selects the alternative with the highest score.
  • Good Features (Alba & Marmorstein, 1987) selects the alternative with the highest number of good features. A good feature is a cue value that exceeds a specified cutoff.
  • Weighted Pros (Huber, 1979) selects the alternative with the highest sum of weighted “pros.” A cue that has a higher value for one alternative than for the others is considered a pro for this alternative. The weight of each pro is defined by the validity of the particular cue.
  • LEX or lexicographic (Fishburn, 1974) selects the alternative with the highest cue value on the cue with the highest validity. If more than one alternative has the same highest cue value, then for these alternatives the cue with the second highest validity is considered, and so on. Lex is a generalization of Take the Best
  • EBA or Elimination by Aspects (Tsersky, 1972) eliminates all alternatives that do not exceed a specified value on the first cue examined. If more than one alternative remains, another cue is selected. This procedure is repeated until only one alternative is left. Each cue is selected with a probability proportional to its weight. In contrast to this probabilistic selection, in the present chapter the order in which EBA examines cues to determine by their validity, so that in every case the cue with the highest validity is used first.
  • Multiple Regression is a statistically analysis of how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. This is beyond the capacity of a normal human and usually requires a resources like a computer.

The book uses the city example to run a test against a few heuristics and Regression testing (computing intensive). The results are startling when you consider the number of cues needed to reach the decision (a low number for Take the Best and Take the Last and a high number for the other three).

Here’s a chart showing relative performance for this particular case study:

As you can see, Take the Best and Regression Analysis are very similar in performance. This means if you pick the right Heuristic to use for the situation you can save time and resources and still get the performance that is comparable for the trade off (time and energy).

So what does this mean? Sometimes it’s the difference between life and death.

A
man is rushed to a hospital in the throes of a heart attack. The doctor
needs to decide quickly whether the victim should be treated as a
low-risk or a high-risk patient. He is at high risk if his life is
truly threatened, and should receive the most expensive and detailed
care. Although this decision can save or a cost a life, the doctor does
not have the luxury of extensive deliberation: She or he must decide
under time pressure  using only the available cues, each of which is,
at best, merely an uncertain predictor of the patient’s risk level. For
instance, at the University of California, San Diego Medical Center, as
many as 19 such cues, including blood pressure and age, are measured as
soon as a heart attack patient is admitted. Common sense dictates that
the best way to make the decision is to look at the results of each of
those measurements, rank them according to their importance, and
combine them somehow in to a final conclusion, preferable using some
fancy statistical software package.

Consider in contrast the simple decision tree below, which was designed
by Breiman and colleagues to classify heart attack patients according
to risk using only a maximum of three variables. A patient who has  a
systolic blood pressure of less than 91 is immediately classified as
high risk – no further information is needed. Otherwise, the decision
is left to the second cue, age. A patient under 62.5 years old is
classified as low risk; if he or she is older, the one more cue (sinus
tachycardia) is needed to classify the patient as high or low risk.
Thus, the tree requires the doctor to answer a maximum of three yes/no
questions to reach a decision rather than to measure and consider 19
predicators, letting life-saving treatment proceed sooner.

To wrap up, the book has many interesting essays as chapters, ranging from bicycle races, hindsight bias, ants, mate selection, and bargaining. It’s a solid 365 pages with small font. The math and the science can be dense, but the applicability of the results are real. It doesn’t sugar coat what goes into making heuristics worthwhile – a lot of up front analysis. It does however show how powerful those paths or decision trees can be once they are implemented.

Gerd Gigerenzer has other books that are probably more digestible for the heuristically curious (Gut Feelings: The Intelligence of the Unconscious and Calculated Risks: How to Know When Numbers Deceive You) but if you’re into behavior and why particular decision paths are more economical than others, then this book is a good educational read.


Other Reviews:
The IBM Data Governance Unified Process: Driving Business Value with IBM Software and Best Practices – A Book Review / How Pleasure Works – A Book Review / Why We Make Mistakes – A Book Review / Drive: The Surprising Truth About What Motivates US – A Book Review / Rules of Thumb – A Review / I Hate People – A Review / The Job Coach for Young Professionals – A Review / A Review of The Fearless Fish Out of Water: How to Succeed When You’re the Only One Like You / A Quick Review of Johnny Bunko (a manga story)

The March 2011 Silicon Valley Positive Outlook Update

The focus of my last post was on whether or not I believe Silicon Valley has inflated like a 1999 bubble. I don’t think it has, but the stock valuations are still pretty high. If I were to guess, I’d say the apparent investment upswing is a byproduct of cash sitting on the sidelines. Investors have it and big companies have it. Investors plant the seeds and companies like Google buy the fruit at the first sign of flowering.

Silicon Valley is an interesting place though. Many of the companies there don’t want to be viewed as uncool. Once that happens it means a particular culture has set in. Which of these companies seem more exciting: Facebook or Yahoo? Google or Microsoft? You get the picture.

Earlier this week there was an article on cnn.com in the Tech section about the hiring on Silicon Valley. Here are some quotes and stats from the article “Silicon Valley experiencing new hiring boom” by Dan Simon:

  • Silicon Valley: 10.6% unemployment rate
  • Last month’s (March 2011) national average was 8.8%
  • Silicon Valley produced 1,200 jobs last month and expected to add thousands more in 2011.
  • According to SimplyHired.com, a search engine for job listings:
    • nearly 40% of 130,000 open positions in Silicon Valley are for software engineers
    • Since July of 2009 there’s been a 245% increase in openings that have “Facebook” as a keyword
    • Over the same time period, a 421% increase in “Twitter” job postings
  • Innovations in social media, mobile and cloud computing are driving the growth, said Dion Lim, SimplyHired’s president.
  • LinkedIn, the social-networking site for professionals, hired nearly 500 workers last year — almost doubling its workforce.
  • “As we grow the company, we’re always on the lookout for top talent,” said Jeff Weiner, LinkedIn’s CEO.

A Dan Pink Speaking Experience

A couple of weeks ago I was staring at my computer screen and in comes an Instant Message asking if I knew Dan Pink was speaking in Charlotte? The IM was from Jill, a work friend for over 10 years. I had no idea about the event, but I was excited. She sent me the link to the UNCC NEXT Speaker Series and I promptly bought a $40 ticket.

The day of the event arrived, but I wasn’t sure where to go. The Blumenthal has several stages and the one I was looking for was the Booth Playhouse. Luckily, there was an event before hand for networking, so I figured I could follow the crowd. It was easy. There were several people standing in the hall welcoming Dan Pink fans and pointing to will call for picking up tickets. I was in extrovert mode and introduced myself to several other attendees, but the response I got was uncomfortable friendliness, forced smiles and all. After a few of these interactions, I realized the people I was trying to chat up were college professors. Maybe they aren’t used to networking in a real business world? Undaunted, I bought a beer and spotted someone who wasn’t part of the school clique. I introduced myself to Darren and we discussed Pink’s books.

Although we are standing in the lobby of a small theatre, it sort of feels like a post modern fashion store. There are doors at the ends, but the entire area is visible through clear windows. I wasn’t at the mall, but I could have sworn I saw some t-shirts on sale for $250. Thankfully, Jill arrived and we discussed our day of work.

We decided to head in early to get a good seat. I heard it was interactive so I wanted to be near the front. However, when we walked in I was very stunned to see the first eight rows or so were reserved for VIPs. It isn’t a big venue so this preferential seating situation was a bit much. For $40 I should be able to sit close.

I met another friend as we were deciding where to sit. My inner voice was screaming “yea!” that this friend showed up. There’s always a rewarding feeling when someone else tries out music, a book, or a restaurant you suggested and this was the same appreciation.

The lights dimmed and the last few seats were taken. I noticed Peter Gorman, the Superintendent of the Charlotte-Mechklenberg schools, sitting across from us – not a VIP either. I’m not sure who kicked it off. It was either the Chanceller or the President of UNCC. He was kind of funny. The Dean of the Business School then introduced Dan to the audience.

I’ve viewed most of the videos for Drive and was nervous that Dan would stick to the script. He mostly followed the themes but he certainly was able to ad lib. He did his homework and talked about the local area some. He quizzed the audience about motivation and interacted with a few different guests. Throughout the session some slides were used to highlight the research that reinforced his points. Time flew by and it felt like it was short, but he spoke for about 70 min.

Overall, I enjoyed my first Dan Pink speaker series. I went with friends and made some connections. Next time I’m going to penetrate the inner circle though 🙂

Working Thoughts 2/10/09
Sustaining Large Economic Growth is Key for the US

An Interview with Dan Pink and the NEXT Speaker Series

Perhaps I’m just now noticing it but over the last 5 years there’s been what I consider an upswing in speaking series, notably around new ways to think and perceive our world. A local college in Charlotte – University of North Carolina in Charlotte – has a program going called NEXT in the Belk School of Business. Dan Pink is speaking tomorrow night (February 1st, 2011) and I’m excited about attending it.

I’ve featured Dan Pink throughout this blog and figured I’d email him some questions. Below is our exchange plus links to his books and a video with Oprah.


1) You’re currently doing a speaking tour – sharing ideas and promoting your books. Does the repetition of this ever sap your enthusiasm for it?



Airport security lines sap my enthusiasm. Big time. As does bad food and lack of exercise on the road. But the conversations with people never get old. Folks seem extremely engaged in this set of ideas — and they’re always showing me new practices or new ways to look at the topic. That’s what keeps me going.




2) Clay Shirky and others have recently highlighted a change in how people spend their time. He calls it the Cognitive Surplus. It’s the observation of people spending less time watching TV and more time creating something, whether its an update to Wikipedia or a dance video on youtube. These themes tend to run throughout your books as well. What are your thoughts on this situation? Is it good or bad that math and science scores are down, but evil squirrel videos are up?




Cognitive surplus is a fascinating idea. And if even a fraction of it goes for noble, interesting pursuits, that can be a game-changer. Wikipedia is a good case in point.  That said, some people will always squander their time.  Today, though, there are many more options for people to use time in (slightly) more creative and ennobled ways.  As for math and science scores, I give evil squirrels a pass on this one. The real problem is that we’ve got an 19th century education system that’s designed mostly for the convenience of adults rather than the education of children.




3) Since publishing Drive, I bet many people have told you about how they instituted ROWE or 20% time. For instance Michael Lebowitz of Big Spaceship in the NY Times mentioned Intellectual Property Fridays. A few hours where they brainstorm very simple ideas and see which ones to run with. Can you share one or two that stuck out to you?




There are lots of examples – and they’re all pretty cool. For instance, I heard about Intuit doing 10 percent time — with terrific results. The head of innovation there, Roy Rosin, told me: “After our CEO declared ‘mobile’ was key to our strategy, none of our business units were able to change direction on a dime, but our employees using 10 percent time create seven mobile apps before any other mobile projects even got started.” What’s also cool is that several schools have begun holding “FedEx Days” — both for teachers and for students.




4) How are your books received in other countries? I imagine many of the concepts in Drive are scoffed at in China or the ideas of A Whole New Mind are “duhs” in Europe.




Both of those books have done surprisingly well overseas — especially in Japan, South Korea, and Brazil.  In Europe, A WHOLE NEW MIND did pretty well — but DRIVE has done much better.  In general, though, the curious reaction to both baskets of ideas — anywhere in the world — is similar. People say, “I’m a right-brainer. I’m motivated internally. That’s how I want the rest of the workplace to be.”


     

The Train with No Known Destination

Last week news broke of Eric Schmidt leaving the CEO post at Google. He’s replaced by Larry Page. Speculation is that Schmidt no longer felt he was in control of the company. The triumvirate of Sergey Brin, Larry Page, and Eric Schmidt had become a duopoly of Brin and Page, the founders. The genesis of their relationship is rooted in the need for someone who knew how to run a big company – Schmidt. Around 2000 when Google was  preparing to go public it was growing at an immense rate. The size of the company had surpassed the experience level of 20 somethings. The founders would concentrate on a start up atmosphere of constant disruption. Disruption is where money is made.

At some point, every successful company grows out of it’s novelty state. The disruption becomes the norm. Competitors look for weakness and stagnant ideas. Being a perpetual start up is the dream of people like Brin and Page. But how do you do it?

Intelligent continual employee turnover.

The enterprise must become a train with no known destination, just stops letting people get on and get off. When the enterprise becomes “the destination” then protection ensues. People can be very good at their jobs, but if they are doing the same thing for more than three years then you have to wonder why? Why isn’t the job evolving? Why isn’t it automated? Why is it needed?

Many large companies, including Google, want to be smaller. Being nimble is key. But wanting a start up mentality and structurally building it in to the culture is not the same. There are a lot of tough conversations to be had. For instance, Netflix has a running practice of “adequate performance gets a generous severance package” and they apply a keeper test which is pretty simple: which people would you fight to keep, at any cost, if they told you they were leaving in two months? This is supplemented by honest conversations about the employee’s commitment and ability to deliver. No surprises.

The NY Times in their weekly section called The Corner Office interviewed Jeremy Allaire, chairman and chief executive of Brightcove. He talked about his conversations with his work force. He said he asks them “What are you trying to do? Where are you trying to head?” This survey reinforces the need to be ever improving.

When the culture of the company is to evolve the job, to morph it, to leave it, or to destroy it (automate) then, as an employee, you know when it’s time for a change. Just ask Google.

Tracking Employment News

14 million people are without jobs. Imagine everyone in NYC, Chicago,
Philadelphia, and Dallas without a job. That’s almost 14 million. I
mention this because the tone of the news regarding employment has
improved since the beginning of fall 2010. It’s almost like we’ve been
on this long road trip and we can finally see the skyscrapers in the
distance. There’s miles to go, but hope is in sight.

I mentioned a few posts ago about the value of the A employee.
I said they are changing the dynamics of the job market and I praised
them. But I can’t pin them down. Sometimes they are the 35 year veteran
and other times they are the 35 year old hitting their stride. Youth
often prevails and so does being a woman. You just never know.

Anne Fisher over to Fortune.com highlighted a great resource for seeing who’s hiring called A Real-Time Look at Who’s Hiring and Where. Vault.com has a tool called Vault Employment Tracker. It’s a simple database organizing all the job announcements.

Date http://www.vault.com/images/icons/desc-arrow.png

Company 

Layoffs 

Hires 

Notes

Industry 

Info

01/18/11

AM General

300

0

AM will layoff 300 workers after the military reduced its order for Humvees.

Manufacturing

More

01/18/11

Unified Solutions Inc

228

0

Manufacturing – Other

More

01/18/11

Karachi Electric Supply Company (KESC)

4000

0

The layoffs are part of a cost-cutting drive to make the power utility financially viable, sources said on Friday.

Energy

More

01/18/11

Johnson Controls Inc.

0

250

The auto parts planct broke ground on its battery recycling plant in Florence, S.C.; it’s set to be finished next year.

Manufacturing – Other

More

01/18/11

Sam’s Club

0

170

The company is looking to hire every position from cashiers to supervisorsat its new location on  in Riverview, FL.

Department Stores

More

01/14/11

Berkeley College

150

0

Affected employees–non-faculty-members–will be let go by June.

College and University Education

More

01/14/11

Lockheed Martin Corporation

1000

0

LM, which is closing its Eagan plant in 2013 is cutting 250 jobs altogether, down from the original 350 anticipated.

Aerospace and Defense

More

01/14/11

Public Health-Seattle and King County

123

0

Nurses, social workers and other staffers were let go as a result of a 50% budget cut to an aid program for low-income pregant women and babies.

Insurance

More

01/14/11

Sterling Life Insurance

80

0

Sterling is adjusting to reduction of enrollment in one of its key products.

Health Insurance

More

01/14/11

SoloPower

0

170

SoloPower said Thursday that it is opening up a new, solar panel manufacturing facility in Oregon

Manufacturing

More