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Footballguys DFS Tools Series: Target Stats

How to get the most out of Footballguys' Targets Stats for daily fantasy football.

The previous article in our DFS Tools Series touched on the fact opportunity stats are the most highly correlated to daily fantasy scoring for all offensive players besides quarterbacks. We want our running backs to see plenty of snaps, carries, and receptions, while targets rule the day for wide receivers and tight ends.

If you had the good sense to invest in an Insider Pro subscription, I trust you already know targets measure how many times a pass catcher is thrown the ball by his quarterback. But what new subscribers (and maybe even some of you old timers) may not know is that Footballguys houses the best target data archive on the web. You’ll see in the video below, our target stats can be sorted by any combination of year (all the way back to 2002), week, position, and team.

To frame the target report’s usefulness for DFS purposes, I’ll take you back to Week 12 of last season. The Seahawks were facing off with the Steelers, who had allowed the fifth-most passing yards through the first 11 weeks. A quick query on the Game Log Dominator (another killer DFS tool but not the star of today’s show) indicated Pittsburgh was making a habit of getting burned by the opposition’s most heavily targeted wide receiver. A.J. Green dropped an 11-118-1 receiving line on the Steelers in Week 8, followed by Michael Crabtree’s 7-102-2 in Week 9, and Travis Benjamin’s 7-113-0 in Week 10.

Clearly, targeting Seattle’s top wide receiver in this matchup seemed worthwhile. The problem was at this point in the season, the Seahawks didn’t really have a top wide receiver. Our Historical Stats (which can be conveniently tailored to DraftKings & Fanduel scoring formats) showed no Seattle wide receiver ranked higher than Doug Baldwin’s 34th in cumulative fantasy points through Week 11. And even that ranking was inflated by Baldwin’s out-of-nowhere 7-134-1 line in a surprising Week 9 shootout against the Cardinals. Before the Arizona game, Baldwin averaged 3.8 targets, 36.3 receiving yards, and caught only one touchdown in his six previous games.

Still, the over/under in Pittsburgh vs. Seattle had reached a moderately high 47 points by game time and the Seahawks passing attack was trending up after explosive performances against the Cardinals and 49ers in their last two games. I decided it would definitely be worth mixing one of Seattle’s wide receivers into my GPP lineups. But should it be Baldwin, or Tyler Lockett, who was fresh off a two-touchdown game the previous week?

This is where the target report really shined. To get an idea of how the Seahawks were spreading targets around, I decided to look at only their last four weeks. Is four the amount you should use every time? Probably not. Our Danny Tuccitto makes a strong mathematical case it takes seven games for targets to stabilize, but it felt like Seattle had been shifting more towards the pass in recent games. So while four weeks may have been a bit arbitrary, it felt like a safe sample. Semi-related side note -- when considering a window to analyze target data, it’s important to factor in when an event occurred that could change how a team allocates their targets (i.e. injuries to quarterbacks or receivers, return from injury for quarterbacks or receivers, and head coach/coordinator changes).

Here’s a look at exactly how the Footballguys target stats pointed me in Baldwin’s direction back in Week 12, complete with some useful tips for massaging the data in Microsoft Excel to show team target market share percentages:

Baldwin, you may remember, exploded for six catches, 145 yards, and three touchdowns in Week 12, for a league-leading 41.5 fantasy points on DraftKings. His ownership percentage in the Millionaire Maker was 2%, which gave an enormous edge to the few entrants who took a flier on him.

Before you bash me for being results-oriented, allow me to come clean:

  • I totally cherry picked this example to illustrate how Footballguys target data helped me win in DFS last year. Somehow I don't think it would have worked as well to write about the time target data pointed me to Cecil Shorts' 4.6 fantasy points in Week 10.

  • Looking back at the last four weeks of a team’s target data (in this case a three game sample) will NOT usually be highly correlated with picking the top scoring wide receiver, especially one who 98% of the field doesn’t see coming.

  • Target data on its own did not land me on Baldwin in Week 12. I also analyzed game logs, historical stats, Vegas lines, and defense vs. position stats. Never rely on a singular statistic to tell the entire story.

  • Of course, I never rostered Baldwin expecting him to score 40+ fantasy points, but he needed less than half that many to pay off his $3,800 price tag (WR42). My baseline projection for Baldwin was 12.4 fantasy points, already a 3.26x salary multiplier -- very close to the 4x multiplier I’m usually aiming for in GPPs.

This was an extreme example and whether or not I used the best process to arrive at Baldwin is certainly open for debate. But the decision to mix him into my tournament lineups made Week 12 especially profitable for me, and without the Footballguys target data clearly painting Baldwin as having the most opportunity to exploit a struggling pass defense, I'm not sure I would have pulled the trigger. At the very least, the target stats got me thinking in a different direction than 98% of my opponents -- usually a good thing in large field GPPs.