Do you know anyone with the title “data scientist” embossed on their business card? Me neither, which is to be expected from a career with so much demand. Companies across the sports industry are amassing petabytes of data on the performance of athletes and the consumption habits of fans. Without data scientists, much of this information is wasted—stored for decades and looked at by no one. People who mash data with business are increasingly valuable in the world of sports. League administrators are constantly trying to level the playing field, forcing franchises to win on the edges. It’s about market inefficiency exploitation, and data scientists are some of the most skilled players on the field.
Data scientists, according to Springboard, are “the bridge between the programming and implementation of data science, the theory of data science, and the business implications of data.” That’s a wordy way of saying that data scientists use math, programming, and business knowledge to answer really tough questions. In the business world, answers to questions like “Should we move manufacturing from Springfield to Shanghai?” or “Is it time to reorganize the company around function (expertise) instead of product?” often come from the executive boardroom. These leaders harness information, experience, history, and intuition to make big bets on a company’s future. Data scientists are tasked with answering similarly tough questions but do so using a different toolset.
Instead of collecting data from studies the way executives do, data scientists go find raw information and use it to form answers to not-so-simple questions. Scour company databases to find meaningful data sets. Can we mash online customer addresses with year-to-date purchase totals to help focus location-based advertising dollars? Look for new sources of information to enhance existing databases. Why isn’t our team of physicians accessing HealthData.gov datasets to study trends outside of sports? Utilize regression and other machine learning modules to discover insights into products, services, and fans. What are the negative effects on team morale if we spend less money on expensive hotels? Collaborate with business and technology teams to implement models and studies that will help prove your conclusions before you scale proposed solutions. Run data study projects from start to finish and follow up on the actions people take to see what did and didn’t work.
Data scientists are comfortable using computer programming languages to build the tools they need to find answers. They should be able to create predictive modeling processes that look at a problem and suggest why those outcomes are what they are. The best among them can use the numbers and charts they’ve gathered to story tell about the findings. Scientists engage in a systematic activity to gather knowledge and make predictions. Data scientists in NFL jobs and NCAA jobs do just that, parsing information in ways that explain what’s happening on the field and suggest what might happen if we alter some variable of the equation.
The most successful data scientists are bilingual, possessing the ability to understand programming languages and speak fluent business lingo. The skills to balance these two sides of the job lets data scientists handle end-to-end projects in a way that few others within a company can.
Truly comprehending data is about more than Google searching and Excel filtering. Most data scientists acquire degrees in data mining, machine learning, statistics, or operations research. A love of math and computer science and a desire to solve every puzzle are all must-haves. Programming languages like R and Python allow data scientists to do complex statistical computing, while Structured Query Language (SQL) gives them the raw tools to find the data needed for predictive models and other research. Possessing a comfort level behind the keyboard to dig for details makes them an extremely valuable part of the team.
We hear it all the time: information is power. That power is only evident when it’s used to tell stories and shape decisions because its rawest form is often confusing, boring, and overwhelming to the masses. Data scientists that can tell these stories with great written and verbal communication possess the ability to shape franchises and help win championships.
Can you propose practical solutions that team leaders will understand and actually implement? Are you willing to stand before a dozen executives and tactfully tell them the franchise they run should make major changes? Do you have the moxie to sell a solution using numbers, words, graphics, and maybe even some empathy and humor? Data scientists create reports, results, and research and transform them into crystal-clear proposals and tangible action steps than can energize team decision-makers. Do that, and you’ll go far as a sports data scientist.
