NFL Data Deep Dive: Recapping Week 2 Through Opportunity, Efficiency, Target Share

The NFL Data Deep Dive goes beyond the box score, looking into the advanced metrics you need to know in order to make informed fantasy football decisions. After games wrap each week, Matt Savoca will visualize and break down the most important numbers in the ever-changing NFL landscape, including proprietary metrics related to efficiency and productivity at the team, quarterback and skill-player level. This is all in preparation for his weekly Matchups column, published every Thursday and available in podcast form every Friday afternoon on the Awesemo Podcast Network. Let’s dig into the data for Week 2 as we look ahead to Week 3 for NFL DFS + Fantasy football

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Team Efficiency

The above chart is a measure of team efficiency, with offensive per-drive ability on the Y-axis and defensive per-drive ability on the X-axis. It uses the expected points model popularized by the nflfastR data package.

When you think of top-tier NFL teams in 2020, the 1-1 Colts aren’t exactly the first franchise you’d think of. And yet, they’re grouped in the upper echelon of squads in both expected points-based and expected points added-based efficiency analyses. Though we’re still in the early stages of these metrics stabilizing, as these numbers are certainly opponent-based, the Colts boast PFF’s No. 4overall graded roster through two weeks. Buffalo, also influenced by a light early schedule, looks efficient out of the gate (upper-right quadrant of both charts). We’ll need to do the same contextualization with Houston’s numbers, dealt a double-whammy in Weeks 1 and 2, falling to the Ravens (No. 2 in net expected points added) and Chiefs (No. 9). This week is far from a reprieve, particularly for the offense, as the Texans travel to Pittsburgh (fourth best in defensive expected points added) in Week 3.

The Eagles stand out as a major disappointment, as both efficiency metrics identify the Eagles as being outplayed on both sides of the ball (bottom-left quadrant of both graphs). They’re currently in an under-achievement battle with the similarly-struggling Vikings, ranking 31st in net EPA. The Vikings, by a hair, are worst.

Team Aggressiveness

True Neutral Early-Down Pass Rate is an indicator of team aggressiveness. It measures the number of times a team chooses to pass the ball on first or second down while within 10 points in the first three quarters, or within one possession in the fourth quarter or later. The raw number is fitted to its historical mean by using empirical Bayes estimation. The error bars represent the 95% credible interval of the metric. These bars will shrink (increasing the model’s certainty) as the season progresses.

As expected, Carolina is pushing the limits of how pass-happy a team can be. Offensive coordinator Joe Brady is dialing up a ton of passing plays for Teddy Bridgewater on early downs in close-game situations. And while it hasn’t translated to massive fantasy days yet, it’s encouraging for the viability of Bridgewater’s top receivers, D.J. Moore and Robby Anderson, even without star back Christian McCaffrey for the foreseeable future. The Steelers also seem ready to utilize Ben Roethlisberger in his traditional pass-first role, as they place fourth in this aggressiveness metric. Another exciting note for spotting potential future shootouts: The NFC East has three teams in the top 10 of early-down aggressiveness. At the other end of the spectrum, the Titans and Los Angeles teams are a combined 5-1 and are designed to run the ball down teams’ throat in close games, ranking 29th, 31st, and 32nd in this metric. The Bears, 30th, are most likely masking their quarterback’s passing inefficiency.

Quarterback Efficiency

This graphic measures quarterback efficiency in terms of their ability to use their given drives to consistently score points. True Drive Success (Y-axis) rate uses empirical Bayes estimation to create a fitted version of raw drive success rate, which is raw scoring drives divided by total offensive drives. Expected Points Added per drive (X-axis) is a measure of a player’s ability to provide more (or less) than the expected points, based on nflfastR’s expected points model, on a given set of plays. It is normalized to a value between zero (worst) and one (best). This graphic measures quarterback efficiency in terms of their ability to use their given drives to consistently score points. True Drive Success (Y-axis) rate uses empirical Bayes estimation to create a fitted version of raw drive success rate, which is raw scoring drives divided by total offensive drives. Expected Points Added per drive (X-axis) is a measure of a player’s ability to provide more (or less) than the expected points, based on nflfastR’s expected points model, on a given set of plays. It is normalized to a value between zero (worst) and one (best).

Things that you shouldn’t need a chart to tell you: Russell Wilson is cooking, Patrick Mahomes is still great, Lamar Jackson is back in MVP form and Aaron Rodgers is playing like this year’s MVP. What you might not expect is that Jared Goff and Gardner Minshew are crashing the elite quarterback party. Goff ranks second in true drive success rate and seventh in EPA per drive, while Minshew ranks an equally impressive second in EPA per drive and eighth in true drive success rate. Honorable mention: Josh Allen, who’s been effective in his handling of two bottom-tier defenses, ranking sixth in true drive success rate and eighth in EPA per drive.

The bottom tier of quarterbacks (lower left quadrant of the graph) still contains MVP Tom Brady and now Pro Bowler Deshaun Watson clumped among struggling young arms Sam Darnold, Baker Mayfield, and Dwayne Haskins. Mayfield was markedly more efficient against the Bengals in Week 2 than he was against a stout Baltimore defense (No. 2 in net EPA allowed per drive), and Brady was clearly much more comfortable in the Arians Week 2, grading out as PFF’s No. 2 passer against the Panthers (No. 28 in net EPA allowed per drive). He’s very likely to move up in both metrics as the weeks go on.

Skill-Player Opportunities for NFL DFS

True Weighted Opportunity Share is a variant of Weighted Opportunity Rating, a metric created by 538’s Josh Hermsmeyer to combine a player’s share of team targets and share of team air yards into a single metric. True Weighted Opportunity share uses empirical Bayes estimation to estimate a player’s ‘true’ target share, based on the team’s sample of targets, and a player’s ‘true’ air yard share, based on the team’s sample size of air yards. As the season progresses, the error bars, which represent the 95% credible interval for the metric, will shrink, indicating the more certainty in the model. Raw weighted opportunity rating is indicated by the red dots.

Factoring in air yards, Adam Thielen is the undisputed leader in share of team pass volume through two weeks. While the Vikings are an extremely low-pass-volume offense (25.5 pass attempts per game, No. 31 in the NFL), Thielen is buoyed by an insane 51% true share of team air yards, translating to a true weighted opportunity rating that’s nearly four points higher than anyone else in the NFL right now. Julian Edelman has seen the exact same number of targets as N’Keal Harry through two weeks, but the traditionally short-area receiver (career ADOT of 8.0) has a 2020 ADOT of over 24, top 30 among all players with five or more targets. Edelman is ninth in total air yards through two weeks. D.J. Moore is turning into a bona fide NFL alpha receiver; he’s fifth in true target share and in true air yard share, good enough for third in this true weighted opportunity metric. The Seahawks’ big two receivers both make the top 20. They’re the only duo to accomplish that feat so far this season.

High-Value Opportunities

Quality Opportunities and Quality Opportunity Share are measures of high-value usage for fantasy football. A target, on average, is worth significantly more than a non-goal-line rush in any type of scoring system that uses point per reception, so Quality Opportunities combine targets and goal-line rushes into one high-upside opportunity metric. An advanced version of the metric also includes light-front carries and only on-target (catchable) targets. The chart will be updated as the data becomes available. True Quality Opportunity Share measures the player’s percentage of team Quality Opportunities and fits it to a historical mean using empirical Bayes estimation.

Your raw quality opportunity champ so far is DeAndre Hopkins, but more interestingly, two Monday night players come in at two and three. One could make an argument for Alvin Kamara as the most valuable asset in fantasy football for the rest of the season, with injuries to Saquon Barkley and Christian McCaffrey. Kamara is 17th in the entire NFL in raw targets this season, tops at the running back position, and he’s fourth in the NFL in opportunities inside the 10. Only Ezekiel Elliott, Josh Jacobs and Dalvin Cook have more. Darren Waller has an incredible 22 targets through two games and has so far commanded nearly 40% of the 2-0 Raiders’ high-value opportunities. Equally impressive is the Steelers’ Diontae Johnson, who is top five in both quality opportunity metrics above. He’s being used as the No. 1 receiver in Pittsburgh, relegating JuJu Smith-Schuster to a secondary option, a situation where Smith-Schuster’s previously thrived. The real beneficiary here is Roethlisberger, who appears to have two stud options to throw to in the receiving game once again, which will boost their offensive numbers in the weeks to come.

Condensed Passing Offenses for NFL DFS Stacks

The condensed offenses chart uses True Weighted Opportunity Share (see above) to find passing attacks that are focused on distributing significant pass volume to their top two or three play makers. The width of the bars represents each team’s relative pass volume, measured by comparing raw passes per game and raw air yards per game. Wider bars represent higher-volume pass offenses. When used in tandem with identifying fantasy-friendly future game scripts, the condensed offenses chart can help fantasy football players make informed decisions about theoretical variations in volume or receiver stacks to target. As an example using the chart above, a lineup with DeAndre Hopkins should expect to capture roughly 54% of the Cardinal’s passing game. Alternatively, if one were to predict that Hopkins is to see less volume than he did in Week 1, according to this chart the next reasonable assertion would be that Kirk and then Larry Fitzgerald would be in line to receive a bulk of that opportunity.

We highlighted D.J. Moore and Robby Anderson in last week’s writeup, and their numbers continue to capture a massive share of Carolina’s total pass offense. Edelman and Harry are one of five tandems to account for half of their entire team’s weighted opportunity, and from a volume perspective, are being used in the same vein as D.K. Metcalf and Tyler Lockett in Seattle. Rookie speedster Henry Ruggs (18th in the NFL in true air yard share) combines with Waller to make up nearly 50% of Las Vegas’ pass volume as well. Other notable duos in the top 10 include John Brown and Stefon Diggs in Buffalo, who found their groove with Allen, and Keenan Allen and Mike Williams, who make up a very significant share of the Chargers’ low-volume attack (indicated by thinner bars). Because of the sheer amount of pass volume (targets and air yards), the receiver groups in Atlanta (No. 4 in top condensed receiver trios), along with Philadelphia, Dallas and newly aggressive Cincinnati will continue to be extremely valuable on a weekly basis.

On the opposite end, the Jaguars are spreading the ball around more than any other squad. Along with one of the lowest-volume offenses in the league (indicated by thinner bars), the Jaguars receivers will need to rely on elite efficiency (like D.J. Chark has had so far this season) or touchdowns to be consistently fantasy viable. At the very least, Minshew’s surprisingly high-quality play helps the receivers make the most of their limited volume. With the loss of Courtland Sutton, many will flock to Noah Fant as an obvious next man up in the receiving game, but K.J. Hamler also stands to gain a significant amount of Denver’s pass volume without Sutton in the lineup the rest of this season.

Bell Cow Backs

The final chart is a measure of running back effectiveness over usage-time (total touches). Any players who appear above the blue shaded area are outperforming the historical trendline for the position, while players below the line are underperforming compared to their historical counterparts. Each season, only a few backs emerge from the pack and shift towards the top-right of the chart. These backs should be considered the true “workhorse” NFL backs and can be used as a starting point for identifying cash-game “locks” at the running back position.

As we say goodbye to Barkley for 2020 and McCaffrey (for now) as elite options at the running back position, Elliott, Kamara and Derrick Henry ascend to the elite tier of available rushers. Clyde Edwards-Helaire, mentioned in last week’s article, didn’t have a transcendent day on Sunday but remains the primary backfield option in potent Kansas City offense that had an off day despite a win on Sunday. After a Week 1 with zero broken tackles and a near non-existent passing game role, Austin Ekeler was a tackle-breaking machine on Sunday and re-asserted himself into the top tier of running back options in fantasy football.

Antonio Gibson is happening. He’s breaking tackles at a significantly higher rate than the trendline. I’m continuing to bet on him even on Washington’s low-efficiency offense. Kareem Hunt and Nick Chubb are the most talented running back duo in the league, breaking tackles at a plus-rate on over 20 touches apiece. Finally, a tip of the cap to Melvin Gordon, who has been extremely efficient on increased volume in Denver despite the loss of their starting quarterback and star wide receiver. He’s one of the most valuable backs in fantasy football, regardless of situation, until Phillip Lindsay returns to the fold.


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Author
A middling athlete who was offered his first sports analytics position at age 14, I've been working on NFL and fantasy football data science since 2017. With a particular passion for data visualization and dashboard building, I love to make data accessible by using graphs and charts to communicate ideas that are difficult to explain with words alone. You can contact me by e-mailing [email protected].

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