Hi, I’m Ken Jee, and in this article, I will share insight on my favorite data use case: sports analytics.
In my day job as the Head of Data Science at Scouts Consulting Group, I help improve the performance of athletes and teams by analyzing the data collected on them. I love my job, and I am excited to join the 365 Data Use Cases series to share 3 ways in which data science is applied in the broader field of professional sports.
You can check out our video on the topic below or scroll down to keep on reading.
Winning the Game with Sports Analytics
Data science has only recently entered the mainstream with sports analytics.
However, it has really been around since the early 2000s. The most popularized example is Michael Lewis’s book "Moneyball" which also has a movie adaptation. In the book, the Oakland Athletics face massive salary cap cuts. So, they needed to be very efficient with their spending. The protagonist, the team's general manager Billy Beane, had to figure out a way to win without access to star players.
In reality, this is one of the main ways we currently use data science in sports - to help a team generate the most wins with the budget constraints that they have.
To cut the long story short, thanks to data science and sports analytics, the Oakland A's had an incredible playoff run that year, well beyond what could have been expected with the budget they had at their disposal.
At the time, being interested in players that seemingly no one else wanted was extremely against the grain. But, in all honesty, there is still some of this sentiment in professional sports. Sports are inherently traditional and there are still a lot of superstitions involved. So, many people are uncomfortable with the new insights that data science provides. On the other hand, it looks like the teams that don’t adopt the data will start racking up losses at an alarming rate.
Sports Analytics and Fan Engagement
Another way that data science can be used in sports is on the fan engagement side. It is rare that you will see a broadcast without some statistics sprinkled into the dialogue or a fascinating data visualization plastered on the screen.
People often forget that data science is about storytelling. And we can use the data to give fans a better understanding of the game and make them more engaged with the content.
In fact, fans are constantly finding new ways to engage with live events, and one of the most unique ways is through fantasy sports.
Fantasy sports in and of itself is almost a $20 billion market. So, it's not surprising that there is a tremendous number of websites specializing in how you pick your own team and optimize your roster.
Sports Analytics for Game Outcome Prediction
This takes us to the third way data science is used in sports: predicting game or player outcomes. In 2018, the Federal Government legalized sports betting federally. This means that each individual state now has the right to allow it and set up regulation around it. This change brought a boom in interest in using data to predict the outcomes of games.
Both the fans and the people running the sports books are constantly trying to improve their models to make a little extra money. In my opinion, this as a huge area for the growth of data science in sports. The question is: "Can your model predict game outcomes better than the average person?"
The Future of Data Science and Sports Analytics
Teams now have access to incredible amounts of data that they have only begun to sift through. Both the NBA and the NFL have tracking data that allows teams to know where all the players and the ball are on the court at any given millisecond.
The trend is for teams, fans, and companies to look at sports data at an increasing rate.
We are just beginning to understand what is possible with all of this data, and I believe you can be a part of this discovery process, too.
I hope you enjoyed reading this article. Check out my YouTube channel if you want to learn more about sports analytics and how to break into data science. Looking to start a career in data science? Sign up for my course, Starting a Career in Data Science, where I guide you through all the steps to landing a data science job. You'll learn how to create your data science project portfolio, build your resume, get an interview through networking, succeed during the phone interview, solve the take-home test, and ace the behavioral and technical questions.