Week 11 Snaps Report

Week 11 Snaps Report

The Week 11 Snaps Report gives fantasy players a view into the Team’s system, positional usages, and player activities. Does the team use RBs more than WRs? Does the team rely on their WRs? These are key questions for lineups, DFS plays, and waiver wire selections. These metrics strengthen as the season goes on. Please come back and continue following my work!


Landscape Data Informatics for my

Week 11 Snaps Report

I believe one way to fight the various biases we as Fantasy Players have to deal with is to use landscape metrics. This prevents the more common “Silo Effect” most “experts” deal out.

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Not only is fantasy a weekly game it is a complex system. System-level predictions are tough.  However, innovation often comes from combining data from several sources. I interpret this as a call for fantasy players to use multi data approaches for this game. See the link for starting your exploring. 


Consider the landscape views of multi-data veins that invite mining for informatic gold. This is my journey within Fantasy in a nutshell! I wish to “show” others my approaches as well.


Team Average Snaps Week 1 to 11

As we have 11 weeks of data for the teams, I wanted to up the game here by combining multi-data sources and use ratio metrics for hypothesis formation. I begin by the landscape view. More snaps associate with team speeds. I also present the current Team Bias in passing (orange) or rushing (purple)

Fast Teams (NE/TB/SF/PHI/SEA/LAR) are more rushing centric vs slower snaps teams are passing more.  I assume the level of pass vs run is based here on team speeds **.

The fast teams as determined by Average Team Snaps (Blue to Red- High to Low) are marked in Green.

The slower teams Red stained teams are the slower teams (WAS/PHI/NYJ/OAK/MIA/TB/ARI). They are biased in passing. PPR players highly supported in these teams. 

Analysis of the Bias Metrics vs Snaps Averages

Analysis of the Bias Metrics vs Snaps Averages suggests Rushing in Fast teams. 64% of rushing teams are in the top 13 Snaps Average vs only 18% of passing biased teams. That metric shifts in the slower teams with those 19 teams that have only 36% rushing vs 82% are passing based. Big differences! Can we use this relationship in 2020 drafts? I will explore and report in my textbook FYI.

**(Data from my textbook studies). Week 11 Snaps Report



The Plot Visualizes the Spread of the Team Snap data vs % Passing and %Rushing Bias.

This Bar Graph plots the Average of Team Snaps from High to Low (Rushing to Passing centric Teams). The key extremes would be the bright green and deep red teams.  Good DFS reference and redraft lineups guide.




Team Bias Rushing vs Passing%

Is there a connection to team snaps and a team’s style of offensive? Most pundits dwell at the player level but I practice a TOP down approach because I think it allows players to set the landscape for their decisions.

The distribution of Team Biases either Rushing or Passing is shown vs the Top to Bottom Yes, it appears that a team’s Snap average can be used to associate a style of play. (Above)

Nearly 64% of rushing based teams are within the top 17 teams in snaps (7 out of 11). More Snaps more rushing.

Interestingly, fewer snaps point to passing based activity and are found in those lower SNAP average teams (82% of the bottom teams are passing based teams). 

Use this information above/below to formulate DFS and Lineups with my analysis of Vegas Lines, Defense Metrics and my Rankings.  (article link at end of this article)



See the Bar-Graph for the trends of Rushing and Passing crisscrossing across the HIGH VS Low Team Snaps.


Late vs Early Difference Snap Averages 

These data also give a view of the entire season (LATE last 5 weeks vs EARLY first 6 weeks) in Team SNAPS Teams that are getting faster may be winning more in the next upcoming games vs slowing down and losing more. Additionally, the LATE vs EARLY metric catches this big turn. 

TB/PHI/CIN/NYG/ATL/MIN/NYJ/PIT have sped up in Snap based Team Speed. I expect nice passing levels based on this trend. (Green Stars)

 NE/LAR/IND/BUF/BAL/GB/ARI/WAS have slowed down and thus we can expect more rushing? (Red Stars)

What teams are speeding up or slowing down? The number of SNAPs can point to rushing vs passing Team Biases (Below). Additionally,  I watch for team trend shifts to trade, drop, or acquire players. These metrics pinpoint various time frames of Team Snaps. Weekly Snaps will have a variation that can hide the bigger trends.

The most recent differences are documented by DIFF2GM metrics. Look for recent trends!

Key into these larger trends. Watch for player changes that may have fueled this metric. FYI there may be some association with SNAP Speed and Losing Games. 

SNAPS Report Week 11 spare f


Plot of Late vs Early Difference Snap Averages

Landscape metrics are shown to allow a focus on the latest trends. Regression to the mean is embedded in these data FYI.

DEN/CHI/OAK are the three teams showing upticks in Snaps Long and Short Term.




Team Positional Level of Average Team Snaps and DIFFs

I always suggest players use a broad view of a Team’s activities. I continue in this Snap Report by looking at the positions. Note, all metrics are colorized High to Low.

These tables from the Week 11 Snaps Report include:

  • Team
  • Position
  • Week 1 to 11 Snaps
  • Average of Player Snaps Per Week of last 4 Weeks (recent)
  • % Team Snaps (%TS) 
  • Bar Graph of the Positional Usages

These tables focus on the position level of each team. I suggest positional snap averages give a nice distribution of snaps to begin an understanding of Team positional usages. Playing players from low vs high positional usages can win or lose a DFS play for this week’s matchup. 

You must go through these figures a few times and focus on the extremes. As I tell my students that there is a Big Difference with being familiar with vs knowing. Move toward knowing. Next year maybe write an essay on each team for 2020.

The key metric for me is the %TS (team snaps) for each position for each Team. That metric is a placeholder for a Team’s positional usage. Usages should associate with opportunity for scoring Fantasy Points

For Example DET RBs not used! 21% is weak! Bal RBs are underused at 26% but loves its TEs at 41%! Find these keys to unlock your plays!

The BAR Graph presents the Orange Line (%Team Snaps) vs Blue Bars (Last 4 weeks Snap’s Average). Note the Orange patterns! I see similar patterns in several teams. (Textbook research time- does a style of %TS mean anything?)


  • CHI and ARI look-alike as does JAX DEN and TEN
  • DET MIA NYJ TB and LAC group together. 
  • Secrets to be discovered here~ 



































Positional Usages 

Before we go into the player data, I leave you with the positional usages for RB, TE, and WRs as groups. What teams are the best and worst for positional usages.  In DFS, I move to the high usages teams and then drill into the players (further data coming). I use these for that same process in lineup set-ups.

Sorted Highs to Lows in the tables and plots as well. Deeper questions as to why? Watch for new trends?














Players Snap Based Usages 

Use these usages %TS as a way of thinking about how a player is used. I find the extremes and use that data to move toward or away from players. I will let users scan the data and decide what key facts/players are germane to your teams. Find your own connections. Good tiebreaker as well. 

Note the teams that have success using in their player distributions. Are the snaps because of poor play etc or deliberate to winning? Deeper questioning!

All Positions include weeks 1 to 11 average snaps, a last 4-week average, and a Team Snap %.   

I sorted High %TS to Low.  The colorization allows the focus on the extremes.



Running Backs

Suggest you focus your handcuffs on the high use teams now before the playoffs! 







Tight Ends







Wide Receivers











Player Snaps Within Teams Weekly and Snap Averages -Environment Analysis. 

The following tables present the player Snaps in their position within Teams, Weeks 1 to 11 Snap and their recent 4-week Snap Averages. I colorized the Snaps within each team. This colorization allows a scan across and down the players and positions. 

I added player SNAP Share metrics (% Team Snaps -%TS) to “see” the last 4 weeks of the season so far. These metrics capture the Team usage of all players. Watch for changes but use these Snap Shares as a foundation of your analysis. 

Additionally, I use the deeper Team Player Snaps environment analysis for my lineups in seasonal and DFS as well as drop adds, handcuffs identification and previous week game scripts for positions usages. I look for upcoming late-season blooming players.

I really suggest you finalize your teams by week 12 to 13 going into the playoffs. Use this data to help formula your trades and acquisitions. 















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Please use SNAP data with my other posts.




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