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Week 16 NFL DFS + Fantasy Football Matchups Breakdown Column With Matt Savoca




Matt Savoca gives you the most in-depth data for Week 16 NFL DFS slate and breaks down NFL DFS Picks for DraftKings + FanDuel lineups

Table of Contents

(44.5) Houston Texans (25.75) @ Cincinnati Bengals (18.75)

All Graphs Reflect Last Five Weeks of Data, Click Graphs to Enlarge

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Team Passing and Pace

First, let’s take a look at the possibility of this game turning into a shootout, as teams that play faster and pass more than average tend to score more fantasy points. When both teams play aggressively, it often creates a game environment perfect for fantasy scoring. Games have a higher probability of going over their Vegas total as well. Ideally, we’re seeking matchups where both teams are in the upper-right quadrant of the chart below (see the chart’s caption for more details).

The X-axis is Early-Down Air Yards divided by team game-script-adjusted plays per second (using Awesemo’s Game-Adjusted Pace from the Advanced Stats Page). The Y-axis is True Neutral Early-Down Pass Rate, a key indicator of a team’s desire to have a pass-oriented game script. The matchup-specific teams’ logos are displayed amongst all other teams in order to contextualize team pace and passing versus league averages (the dotted lines on the chart).

Any game where one team’s led by Deshaun Watson has a chance of shooting out, especially with the Bengals’ defense rating among one of the worst in the NFL (though they looked strong in their Week 15 upset of the Steelers). If the Texans’ offense can make a few splash-plays early and take a quick lead, the Bengals will likely be forced to keep pace, which could lead to a very fantasy-friendly game environment.

Recent Quarterback Performance

Next, let’s examine the starting quarterbacks’ recent play. Sustained drives in the NFL rely on efficiency at the quarterback position, so I’ve identified my three favorite performance indicators for assessing quarterback performance. The three bar charts below (see the graph’s caption for more detail) represent a quarterback’s per-drive efficiency (left bar-chart), per-play efficiency including rushes/scrambles (center) and per-pass efficiency (right). Higher rankings from both players indicate a higher likelihood of efficient play, thus a higher probability of fantasy points.

The three columns represent my three primary performance indicators for quarterbacks. Furthest left (tDSR) is True Drive Success Rate, a drive-based efficiency metric that measures a quarterback’s ability to turn drives into touchdowns, regressed based on sample size. The middle column, Expected Points Added (EPA), is a measure of per-play efficiency and includes scrambles and designed runs. Finally, on the right is per-pass efficiency, Completion Percentage Over Expectation (CPOE) based on the publicly available completion percentage model included in the NFLFastR package.

From an efficiency standpoint, there’s not much of a difference between Brandon Allen and Ryan Finley, as the above chart clearly indicates. Watson hasn’t been as efficient as he’s typically been on a per-drive basis over the last five weeks (likely due to an understandable adjustment-period in the absence of wide receiver Will Fuller), his per-pass and per-play efficiency is still well above league average, indicating we could likely see some positive regression in his drive-based efficiency in the weeks ahead. Watson’s in a great bounce-back spot this Sunday.

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NFL DFS Player Pool Picker: Upside Analysis

Now, let’s examine the primary skill players from each offense. Unless participating in a large-field tournament, these are the players you should be focusing on in your daily fantasy football lineups. For each player, I’ve made it easy to compare their per-game fantasy (in column 3) to their DraftKings main slate salary ranking (in column 2). Column 4 ranks the player using my favorite position-specific volume metric: Expected fantasy points, which is curated by PFF.  The Defense vs. Position column is an extremely helpful position-specific and player-group specific (for example, the metric delineates between primary wide receiver vs. secondary wide receivers) metric that helps determine if an offensive player is likely to be in a position to exceed their expected fantasy points. Higher numbers in the Defense vs. Position column indicate easier matchups for the offense, while numbers closer to one indicate a stout defense against that specific position-group.

The final column, Expected Projection, combines a player’s expected fantasy points with their weekly matchup into one helpful value metric. Apart from the Awesemo projections (which should still carry the most weight in determining player value), Expected Projection is one of my favorite metrics to help me narrow down my favorite plays of the week. The rankings are always specific to the slate, meaning if a player or team is ranked No. 1 at a specific metric, they may not necessarily be ranked No. 1 in the NFL, but they are for this specific slate.

Cincinnati Bengals NFL DFS Core Offenses

Team Player Positional Salary Rank FPTs/Gm Rank Expected FPTs/Gm Rank Defense vs. Position Expected Projection
CIN Ryan Finley, QB #21 #20 #31 #23 6 Fpts (QB21)
CIN Tyler Boyd, WR #25 #29 #40 #12 12 Fpts (WR22)
CIN Tee Higgins, WR #30 #24 #32 #24 12.5 Fpts (WR18)
CIN Giovani Bernard, RB #25 #18 #24 #31 16 Fpts (RB5)
CIN A.J. Green, WR #55 #67 #86 #26 6 Fpts (WR59)

Tyler Boyd and Tee Higgins look like values compared to their salary-based expectations, and Giovani Bernard looks like one of the best value-plays on the entire slate, as his expected projection is far better than his salary-based expectation. Bernard will be plenty popular, and doesn’t have the highest ceiling, but has one of the safest floors at the position this weekend.

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Houston Texans NFL DFS Core Offenses

Team Player Positional Salary Rank FPTs/Gm Rank Expected FPTs/Gm Rank Defense vs. Position Expected Projection
HOU Deshaun Watson, QB #3 #3 #5 #22 23 Fpts (QB2)
HOU Brandin Cooks, WR #13 #20 #29 #22 12.5 Fpts (WR18)
HOU David Johnson, RB #14 #13 #20 #22 11.5 Fpts (RB15)
HOU Keke Coutee, WR #21 #13 #31 #9 13.5 Fpts (WR17)
HOU Chad Hansen, WR #35 #7 #25 #28 14 Fpts (WR12)
HOU Jordan Akins, TE #14 #26 #30 #21 7 Fpts (TE14)

Besides Watson, who’s absolutely viable if choosing to pay-up at quarterback, David Johnson, Keke Coutee, Chad Hansen, and Jordan Akins all look like strong plays against a Bengals’ defense that has struggled with big-bodied outside receivers over the course of the season. Brandin Cooks‘ salary seems a bit steep, but he’s absolutely in play, as the Bengals’ rank bottom-12 in schedule-adjusted fantasy points allowed to /primary wide receivers.

NFL DFS Backfield Values: Running Back Opportunities

Nailing the running back position in your fantasy football lineups is vitally important. There are many metrics that help fantasy gamers determine which running backs earn higher-value touches than their peers, but my favorite is Quality Opportunities per game. It only counts running back looks that come via the receiving game (as receptions are much more valuable on a per-play basis than rushes) or via goal line rushes (inside the opponent’s 10-yard line) since there isn’t anything more valuable than a touchdown.


This chart measures Quality Opportunities per Game (Targets or Goal line carries), and its variant, Quality Opportunity Share, which are both key performance indicators for running backs. This metric helps filter so-called “empty touches” from a running back’s workload and highlights the opportunities that are most likely to be successful for fantasy football. It includes injured players, in order to help contextualize players who might be receiving a smaller or larger workload based on personnel shifts.

Derrick Henry never pops in this metric, but he’s so efficient on a per-touch basis it simply doesn’t matter that he’s not seeing a large number of high-value opportunities. It’s nice to see D’Andre Swift pop in quality opportunities per game. In tournaments, Swift will make a ton of sense in lineups that don’t include Henry. Expect Adrian Peterson and Kerryon Johnson to continue to get their reps behind Swift, however. This backfield’s not all his, just yet.

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NFL DFS Receiving Values: Skill Position Opportunity Ranges

The wide receiver and tight end positions are two of the higher-variance positions in all of daily fantasy football, so it’s important to consider both floor and ceiling when predicting a player’s usage. Players with unexpectedly high opportunity in the passing game often have the best chances to outperform their projections, so I created a chart that measures a player’s opportunity range of outcomes (see the caption of the graph for more details on how this is calculated.). The horizontal bars represent a player’s expected opportunity range, while the center dot represents their average opportunity.

This chart measures the mathematical uncertainty in each player’s true weighted opportunity metric (the metric is explained in the caption of the Game Opportunity Chart above), providing an insight into a player’s range of outcomes related to receiving opportunity. For each player, the team-color dot is the “true” metric, while the red dot indicates the “observed” stat. The bars represent a player’s 95% credible interval, which we can use to measure the uncertainty (both positive and negative) related to their expected workload through the air.

Chad Hansen seems like an excellent tournament play this weekend, as his opportunity upside exceeds any other Texans’ receiver, including Brandin Cooks, on the Texans’ side of the ball. The same can be said for Tee Higgins in Cincinnati. Higgins was a clear favorite of Ryan Finley‘s in Week 15, and would get a slight boost if Finley starts again for the Bengals this Sunday.

NFL DFS Auxiliary Offensive Players for Large-Field Contests

In daily fantasy football, depending on the size and type of contest you’re playing, it’s often advantageous to expand your player pool to less-utilized offensive players. In this section, we examine every part-time player’s usage based on playing time, opportunity and (again) defensive matchup. For each player, I create an Opportunity Score, which scales a position-specific opportunity metric between zero (least valuable) and 100 (most valuable). Scores above 50 tend to indicate starter-level opportunity, while scores over 80 indicate star-level usage. Finally, I categorize the players as an NFL DFS value, a mass multi-entry option (MME-only) or a player to avoid altogether.

Cincinnati Bengals NFL DFS Tournament Picks

Team Player Snap Share Opportunity Score Defense vs. Position Outlook: Value, MME-only, Look Elsewhere
CIN Drew Sample, TE 86% 68 #17 MME-Only
CIN Samaje Perine, RB 31% 7 #31 MME-Only
CIN Trayveon Williams, RB 10% 35 #31 Look Elsewhere
CIN Alex Erickson, WR 16% 8 #26 Look Elsewhere
CIN Mike Thomas, WR 14% 22 #26 Look Elsewhere
CIN Cethan Carter, TE 26% 13 #17 Look Elsewhere

Houston Texans NFL DFS Tournament Picks

Team Player Snap Share Opportunity Score Defense vs. Position Outlook: Value, MME-only, Look Elsewhere
HOU Duke Johnson, RB 71% 78 #22 Look Elsewhere
HOU Darren Fells, TE 46% 13 #21 Look Elsewhere
HOU Kahale Warring, TE 15% 22 #21 Look Elsewhere

Drew Sample doesn’t have spectacular target share numbers, but he’s a full-time player going against a mediocre defense and is far from the worst punt-play at the ever-volatile tight end position. Bernard’s backup Samaje Perine makes sense as a large-field contrarian option, as the Texans have been historically bad against the run all season long.

Final Thoughts

Deshaun Watson‘s by far the most exciting player from this game, but it’s hard to ignore the value of Giovani Bernard on the Cincinnati side of the ball. Watson stacks are completely viable in small-field tournaments, as he and his receivers rank inside the top-5 in the Awesemo top-stacks tool this weekend.

Prediction: Texans 26, Bengals 17

Looking for more NFL DFS picks and daily fantasy football matchups content? We have loads of articles, data and more on the Awesemo NFL home page. Just click HERE.

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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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