Data as a Model – Football Yardage

Data is an abstraction or a physical activity. When describing data we are measuring one element that may actually have multiple variables that influence its outcome.

On Monday, the LSU Tigers will play the Clemson Tigers for the College Football National Championship. Before, during and after the game, reporters, fans, and announcers will compare many metrics. They will discuss turnovers, first downs, penalties, etc. but the most common statistic (beyond the score) will be offensive yards. Offensive yardage represents many things: the quality of the offensive line (or its lack of execution), each coach’s play-calling, and the quality of the quarterback/receivers play. For example, Joe Burrow’s highlight against Georgia represents a 71 yard pass and that is all. The duration of the play, etc., are compressed into one small data point.

1st & 10 at LSU 20

(3:57 – 3rd) Joe Burrow pass complete to Justin Jefferson for 71 yds to the Geo 9 for a 1ST down

So, when you are watching the game, remember the announcers often describe an action by a single variable, one which is influenced by many things. And for some items, “data” fails to describe the variables that create these memorable moments.


Eating Chocolates and Performance Metrics

We have all seen or heard this quote from Peter Drucker.

The focus on performance is a byproduct of a data rich world.  Deploying “the internet of everything”, provides the ability to improve system performance at a greater degree of granularity  if we all can agree upon the desired outcome.

A fan of slapstick/physical comedy, I always enjoyed this skit. Lucille and Vivian are unable to keep up with their chocolate wrapping assignment.  They eventually “hide the evidence” that the system is failing, as their confidence turns to panic. (The woman manager actually created a perverse incentive, i.e., no unwrapped chocolates. To avoid being fired, they actually do a worse job than being truthful about their work, or the manager observing to see if they were preforming as expected.)

The manager saw the chocolates were gone. She was delighted, but did not understand the system’s real performance. One could argue that her measurement tools were weak, but her eyesight was sufficient to allow her to believe that no other testing was necessary, the objective was met, no unwrapped chocolates in the other room. Lucille and Vivian do not confront the manager. Their mouths are full of chocolates, thus agreeing to be overworked yet again.

So, when examining ways to manage performance measurements, industrial processing does a good job of discussing flow charts, etc., but it may not necessarily capture the ingenuity of the work bench! And this is where the second Drucker quote serves as a useful counterpoint.

Lessons In Mentorship From Peter Drucker - Credera

But there may be a better quote… “just remember  performance measures are like a box of chocolates.”

Forrest Gump Quotes About Running. QuotesGram

Don’t Tell Me!!!

Wise men don’t need advice. Fools won’t take it.
Benjamin Franklin

The older I get, the more I see this message true. It is easy to assume we are all experts. For a researcher, this is not a good attitude. We all know the one way to do any research activity (process, data, approach, etc.), but in doing so, we often forget the joy that comes from learning something new. It is in that learning, based on recommendations, comments, critiques, etc., that we grow as researchers. But it is in the teaching to others where we learn more.

Photo by Priscilla Du Preez on Unsplash

What If the Horseshoe Falls Off?

There is the old nursery rhyme about how a kingdom is lost because a horseshoe falls off.  The poem refers to paying attention to little things that can make a difference, as the casual relationship of minor things failing can evolve into major problems (the Space Shuttle Colombia is but one of many examples). While one could argue its importance on military logistics or other more mundane tasks (such as learning the basics when mastering any skill), the same logic could be applied to not only the development of data but to data applications.

In the age of “Big Data”, we see where more information can provide insights that were unavailable just five years ago. The use of Artificial Intelligence and Machine Learning will transform how we collect, manage and process data, providing insights that will assist researchers and decision makers. However, the casual relationships between collecting/using data with any unintended consequences remain.

For example, one could argue that I represent three people: a physical me who eats, sleeps and walks around, while there is a legal me, who signs legal documents and has financial interests. There is an emerging digital me, where I live and work in a virtual world. My information is collected, processed, and analyzed, as I become “a product” sold to others. In many ways, the data collected from millions of digital actions are creating better horseshoe nails for business, governments and others, but will this lead us to lose the kingdom of our individualism?

Why Does Adopting New Information Take So Long?

do we know the question?

As a researcher, I have often heard people lament, “We studied this in the past and nothing was done”, or “Why are we not using this approach”, or some variation concerning the fact that data and information are not being used after the being developed, purchased, or studied.  The question is that we think, using our crystal ball, we have built a masterpiece, and wonder why people don’t adopt our insights.  We often forget that this “knowledge” could be slow to be adopted by others for many reasons.

Failure of adoption:

  • The first is simply the WHY?  Sometimes when doing research we understand more about the question that the person who needs the answer.  So while we prepare our work, we forget our client will only use what they can understand with some level of confidence.  How often have we seen a more senior person misspeak based on information not properly summarized for them?
  • Secondly, there remains the ever consuming “tyranny of the urgent”, in that the research is needed in a timely manner, but the research is not needed beyond the “now”.  The reasons can vary from staff turnover, policy change, new leadership, the findings were not what was expected, to a thousand different reasons.  Furthermore, data is perishable, something that is often forgotten by the researcher, but not the client.
  • Thirdly, the experts may not agree with your opinions.  My wife is a fan of Downton Abbey, and during season 3, Sybil Branson died after childbirth.  The tragedy was there were two doctors arguing over her treatment, and the older doctor stated to the other doctor he is to not interfere.  In many ways, we can find people with good intentions failing to achieve an expected outcome because they are using older models from the past.  They remain uncommitted to learn, and without the application of new information, their working knowledge could, and does, fail, in providing actionable insights, or even providing the wrong information.  Presenting this expert with new information may only lead them to become more entrenched to their position.
  • Finally, our research may not actually answer the question being asked!

I suspect the following challenges will remain with us for a long time, based on  NCHRP Active Implementation: Moving Research into Practice, posted at

For the research community, the ghost of people not adopting our great ideas haunts the adoption of our “great efforts”.  But we must understand what the client may do with the research once it has been delivered, which may depend upon how we communicate before, during and after the research process!

Crossing over the U.S-Mexican Border

Recently, Teen Vogue highlighted how crossing the U.S. border remains a daily reality for most people. The author focused on four stories: travel for work, school, shopping and family, although there are other reasons, such as medical or tourism. The article sited a Bureau of Transportation Statistics website which complied numbers, mode and locations crossings into the United States. And BTS does not report illegal crossings…that is Customs and Border Protection’s information.

So in 2018, the largest U.S. crossings with Mexico are shown on the following map

So, there are a few crossings in Southern California, El Paso, and the lower Rio Grande Valley with the largest passenger crossings, mostly by automobile or walking. The largest bus crossings are in Laredo, but the largest Pedestrian and automobile crossings occurred in San Ysidro and El Paso.

No one simply travels across the border just to say, “well I was in “so-and-so””, (well, unless you are interested in joining the Traveler’s Century Club.) Sometimes, we forget that for each data point, there is a reason why someone wanted to enter the United States. But for local and national groups, it is just as important to know the total number and location of how people entered the United States to assist in planning infrastructure and operational needs or to quantify the border’s economic contribution activity.


How the graph was created: Downloaded a custom table from BTS Border Crossing Database, converted the file into Excel to fix the geography, and than imported in Tableau.

Related databases:
The BTS information does not include passenger flights, which are reported here

There is some information on commercial freight traffic, such as trains and trucks,which is presented below for 2018. For a fuller comparison of commercial freight transportation across the borders, go TransBorder Freight Data.

Does Counting Matter

I am reminded of this Peanuts cartoon. Linus tells Lucy not to count the snowflakes for he already knows the answer. (We can argue that maybe Linus is a seasoned researcher, but I’m sure he was outside earlier doing what Lucy was doing.)

And what is counting, but simply putting a sequence such as “1, 2, 3,…”.  I could type any number on a keyboard and generate data.  When we look at data, sometimes we can get so absorbed in knowing a number that we forget why knowing a number matters.  What benefit is it for Linus or Lucy to know the numbers of snowflakes? No one measures a single snowflake, but rather snowflakes, as the aggregate matters.  For example, one snowflake weighs nothing, but too many can collapse a roof.

There remains a need to count and observe the world, and I am guilty of looking for data when I do research.  So, statistical and data approaches are warranted to make sure we have sound information to make a decision.  While it is easy to measure snowflakes or other actions, sometimes transportation and economic data is not as clearly observed.  Understanding what is needed to be known helps us see the world before we go outside to count snowflakes.