In the World of Sports Analytics, the Math Nerds Prevail
Did you know that mathematicians are increasingly useful for the professional sports industry? If your student loves sports, and in particular, if your student loves tracking player stats and arguing about who the best quarterback is based on those stats, your student may be interested in a career in data science. But what does math have to do with sports? And when did data science become such a force in this industry? To answer those questions, let’s talk about my fantasy football season.
The Math in Fantasy Football
Every week during the NFL football season, I put on my “data scientist” hat and sit down to determine who are the optimal players to start in my lineup in fantasy football. I’m competing with college friends, seeking to win bragging rights. Of course, no matter how self-confident we are in our predictions, none of us can see the future. Instead, we rely on our best guess for how well any given player will perform in a given game, based on a mix of intuition and projection statistics (mostly ESPN generated, in my case.) Of course, statistical probability can’t account for all chance occurrences, like the unfortunate scenario in which your starting quarterback sprains his ankle. But statistical probability can help us make educated assumptions and guesses, aiding us in our decision making process.
I mentioned that I use ESPN projections mostly. And that means I let other stats experts crunch numbers to provide me with three important metrics: the projected average number of points my quarterbacks are likely to score, and then the percentage chance that each quarterback will boom (score above the projected average) or bust (score below the projected average.) In statistics, standard deviation lets us determine the exact percentage chance that an outcome will be different than the projected average. (We use the standard deviation all the time, for example to determine what percentile an SAT test taker scores in, or to determine if an infant is underweight.) For my purposes, if I’m in a tight match-up this week, I may choose to play the quarterback who has the lower projected average, but who will score more in a boom scenario than the other quarterback. While understanding how these percentages are calculated can help me, no amount of statistical analysis can tell me how risky I should play things. At the end of the day, statistical models are helpful aids but they can’t replace human judgment!
How Do Experts Calculate the Projections?
In the early days of sports forecasting, very simple statistical analysis was used to guess at how players would perform in future games. In an article for Built In, Keith Goldner, vice president of data science at FanDuel (a popular fantasy football site) explains:
It was basically looking at player averages and hoping that was going to be predictive of future performance.
These kinds of stats are easy to compute: adding up the total number of touchdown passes your favorite quarterback threw, divided by the number of games played, for example, will give you the “average number of touchdown passes per game” in last year’s season. But how much will that stat actually tell you about what is likely to happen this year? These early forms of statistical analysis were not really satisfying in their predictive power.
With the introduction of computers, statistical analysis became much easier, faster, and cheaper to create. Suddenly it became much easier to ask and answer questions like, “against the Patriots’ defense in a first-in-goal situation, do we have a higher completion rate from passing or running?” Being able to track this kind of data gives coaches crucial information as they seek to optimize their team’s performance. Michael Leone, a data scientist at SportsGrid explains that “the edge in fantasy sports, a lot of times, is taking that data and information and being able to parse out what’s meaningful, what’s not meaningful, and make projections and derive actionable information from that. I think that’s why it leans more toward math people in recent years.”
Are There Really Jobs Like This?
Okay, so math people might have an interest in sports, but are there really jobs for them in the industry? Increasingly the answer is a resounding yes. At every point in the process, people who can work with data are valuable. Scouts want a competitive edge in determining who to recruit for the college basketball team, trainers want to know how best to structure practices to capitalize on hidden trends for a team and its players, players want to know how to optimize their sleep schedule, their diet, their exercise regimens, coaches want assistance in knowing what calls to make, managers want business insights, commentators want fancy graphs, and fans want as many projections as they can get!
Getting started in the world of sports analytics is not like becoming a doctor by going to medical school. Most sports analysts rely on a lot of skills that they taught themselves, from how to use Excel to how to program code. There’s a lot of freedom here but it can also feel daunting. Sam Gregory has an excellent article titled “Getting into Sports Analytics” in which he shares advice based on his own experience breaking into the industry. I think his best advice is also the hardest: “You are never going to be the best so don’t wait until you are.” It is so easy to compare yourself to others, who are better mathematicians or better coders or better…and then to feel discouraged. Instead, start with what you know. Track player stats in an excel document and start determining player averages for the season. Play a season of fantasy football with friends and try to peek under the hood at the projection models used to forecast. Buy a book on statistics for sports or Python programming for beginners. Experiment, learn, and have fun with the process. As you build your skill-sets, you’ll get a better sense of what is possible for you to learn.
How Can Parents Help?
Parents here’s the best part. All the skills that are useful for sports analytics, are useful for a whole lot of other industries as well. That means that if your student can be encouraged to pursue these interests out of a genuine love for sports, they will be equipped for more jobs than simply those in the sports industry. If the jobs dry up in sports analytics, there are plenty of other industries ranging from healthcare to business management to public policy where these skills can be used. As Gregory notes, “If you learn how to code, how to use data and how to communicate mathematical concepts effectively you will be more employable in any field.”
Okay, enough talking about math, it’s time to go watch some football!