How to Use Big Data in Fantasy Football

Football season is underway. Rosters are set, teams are planning for their matchups and stressed out general managers are scouring waiver wires for injury replacements to help round out their squad and ensure success during the season.

We’re not talking about the NFL. We’re talking about the other version of big-league football, the kind folks play on their phones and laptops using spreadsheets and data sets.

Around the world, multitudes of people are engaging in fantasy football, an analytical and numbers-driven phenomenon that allows fans to assume the roles of a team’s coach and executive board. Tasks like player evaluation, salary management, teambuilding and even game day strategy are handled by a single fan who acts as owner, architect and tactician over his or her unique team.

Finding Your Fantasy Football Edge

Like in the NFL, stakes are high in fantasy football. Many players can spend months studying websites and sports news, planning for the draft, analyzing new coaching schemes or roster moves, all to find a winning edge when the season kicks off.

That’s called using big data.

Big data consists of large and complex data sets that are used to draw useful insights about something complicated. It’s been used for things like customer buying trends, internal processes and business operations.

Football has as many data subsets as any business function. Finding your edge with big data is about targeting the best subsets and using them to make informed decisions.

Here are a few surprising insights gathered from fantasy football’s big data pool.

• Don’t worry about yards or touchdowns when you’re drafting players – This might seem like a total 180-degree violation of fantasy football bylaws, but there’s some brilliant truth to this. Yards and touchdowns are among the most difficult stats to predict, which means they’re among the most difficult stats for players to consistently replicate. Instead of studying a running back’s previous yardage and touchdown totals, focus instead on how many opportunities he’ll get to run the ball, and in what situations those opportunities will arise. Ditto receivers – instead of looking at previous catches and scores, try and discern how often a quarterback will throw in that receiver’s direction during the coming season. If you figure that out, all the other metrics will take care of themselves.
• Ignore a player’s average yards per play – It can be enticing to spend a late round draft pick on a running back who averages 5.4 yards per carry, because a player’s average yards are based on things like long runs or big losses, and there’s no consistent way to predict either of those outcomes. A runner might average 8.5 yards per carry in game one, and then 1.5 yards per carry in game two, giving him a healthy 5.8 rushing average that doesn’t tell the whole story.
• Your goal is to score points, so focus on points – The football research website Advanced Football Analytics has a metric called Expected Points Added Per Play. It’s a complicated mishmash of several situational circumstances and historical trends, but it equates to the number of points a player will accumulate on any given play. For example, say a starting running back gets hurt in the NFL. You might find his replacement in your fantasy league’s free agency pool, but you notice the replacement is only averaging 3.1 yards per carry and hasn’t scored a touchdown since 2014. But, the Expected Points Added Per Play metric tells you that this replacement’s statistical output was a product of his situation (being used only to block on passing plays, for example). Now that he’s in the starting lineup, his role (and fantasy football value) is expected to change in a really positive way.

Big data is all about insight. If you know where to look, a lot of your draft and game day decisions can become a matter of math instead of instinct. So bring a different cheat sheet to your fantasy football draft this season – and win with big data.