Hello Everyone – You’re watching 365 data science use cases. This is an awesome initiative
by 365 data science to share real data science stories with the youtube community. If you
want to see more of these remember to subscribe to this channel. I’m Ken, and today I’m
talking about my favorite data science use case: sports. 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’ll use this video to
talk about the 3 ways that data science is applied in the broader field of professional
athletics. Check out my channel linked below and in the top right corner of the video if
you want to learn more about sports analytics or about how to break into data science.
Now, data science has only recently come into the mainstream of sports; however it’s been
around since the early 2000’s. The most popularized example is through Michael Lewis’
book Moneyball. It also as a movie adaptation if you don’t want to go ahead and read it.
In the book, the Oakland A’s are faced with massive salary cap cuts, so they had to be
very efficient with their spending. The protagonist, Billy Bean, had to figure out a way to win
without access to star players. This is one of the main ways that data science
is currently used in sports. How can a team generate the most wins possible with the budget
constraints that they have. Long story short, the A’s had an incredible
playoff run that year, well beyond what could have been expected with the budget that they
had. At the time, this was extremely against the
grain. They were interested in players that seemingly no one else wanted. Honestly, there
is still some of this sentiment in professional sports. Sports are inherently traditional
and have a lot of superstitions, so many people are uncomfortable with the new insights that
data science provides. On the other hand, it looks like teams that don’t adopt the
data will start racking up losses at an alarming rate.
Another way that data science can be used in sports is on the fan engagement side. It’s
rare that you will see a broadcast without some statistics sprinkled into the dialogue
or a fascinating visual plastered on the screen. People often forget that data science is about
storytelling. We can use the data to give fans a better understanding of the game and
make them more engaged with the content. Fans are constantly finding new ways to engage
with live events, and one of the most unique ways is through fantasy sports.
Fantasy sports itself is almost a 20 Billion dollar market. There are a tremendous amount
of websites specializing on how you pick your own team and optimize your roster.
This brings us to the third way data science is used in sports: predicting player and game
outcomes. In 2018 the US government legalized sports betting federally. This means that
each individual state now has the right to allow it and set up regulation around it.
With this has come a boom in interest in using data to predict the outcomes of games.
Both the fans and the people running the sports book are constantly tying to improve their
models to make just a little bit extra money. I see this as a huge area of growth for data
science in the sports realm. Can your model predict game outcomes better than the average
person? If so, you might be able to make a couple of bucks here and there.
So where is data science in sports going? 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 or on
the field 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 maybe you can be a part of this discovery process.
Thanks for staying till the end of the video! For more videos with me, check out my YouTube
channel, where I talk more about sports analytics. I also talk about how to break into the data
science field and I share a couple of my own personal projects there.