Week 10 Snaps Report

Week 10 Snaps Report

The Week 10 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 10 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.

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 10

As we have 10 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. The data begin by the landscape view. More Snaps associate with team speeds. Fast Teams are more rushing centric vs slower snaps teams are passing more. (Data from my textbook studies). I assume the level of pass vs run is based here on team speeds **. 

I colorized the team names (Blue-passing to Orange-rushing) based on the weekly team snaps (Purple to Yellow).  Also presented is the overall snaps average (green to red).




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



TEAM SNAP AVERAGES vs Team 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. Nearly 66% of rushing based teams are within the top 14 teams in snaps (8 out of 12). More Snaps more rushing.

Interestingly, fewer snaps point to passing based activity and are found in those lower SNAP average teams (78% of the bottom teams are passing based teams). 

Use to formulate DFS and Lineups vs Vegas Lines, Defense Metrics and my rankings. 


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



Late vs Early Snap Averages 

These data also give a view of the entire season (LATE last 5 weeks vs EARLY first 5 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 HOU MIN MIA PIT NO DAL has sped up in Snap based Team Speed. I expect nice passing levels based on this trend. 

LAR BUF BAL ARI WAS have slowed down and thus we can expect more rushing. 

What teams are speeding up or slowing down? The number of SNAPs can point to rushing vs passing Team Biases (Following Figure). 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.

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. 



Last 2 Games Differences In Team SNAPs



Last 5 Games Differences In Team SNAPs



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 8 Snaps Report include:

  • Team
  • Position
  • Week 1 to 10 Snaps
  • Average of Player Snaps Per Week
  • % Team Snaps (%TS) 
  • Late vs Early Snap Average Changes 
  • 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.

For Example ARI TEs not used! 23% is weak! Bal RBs are underused at 22% but loves its TEs at 36%! Find these keys to unlock your plays!

The BAR Graph presents the Orange Line (Positional Average) vs Blue Bars (%TS of Positions). 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 and TB group together. 
  • Secrets to be discovered here~ 























Extremes in 2 vs 5 Game SNAP Differences

Before we go into the player data, I leave you with the extremes for RB/TE/WRs. Why the extremes? New trends? Thus a shuffle of the Players and Positions. Watch for 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 10 average snaps, a grand 10-week average, Late vs Early 5 Game Differences and a Team Snap %. I sorted High %TS to Low.  The colorization allows the focus on the extremes especially in the Late vs Early Differences. 



Running Backs

Suggest you focus your handcuffs on the high use teams. Look also at higher used player improving (Late vs Early).

For example Bell, Carson, Cook, Barkley, Drake, and Williams all high with a high Late vs Early Metric (Purple). 

Gurley, Kamara, Johnson, Connor, Ingram have declined (Yellow).








Tight Ends

Ertz, Hooper, OJ Howard declined

Griffin, Fells, McDonald, Henry improved







Wide Receivers

Declining : Robinson/Woods/Samuel/Sutton/Aghlor/Kupp/Fuller/Sanu

Improving: Gallup/Jones/Parker/Tate











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 10 Snaps and their grand 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 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 10 to 12 going into the playoffs. Use this data to help formula your trades and acquisitions. 












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 Player by Team Positional Snap Use %TS vs Late to Early Snap Differences

I sorted the players using the Late vs Early Metrics. Purple colorization points out players that have dramtically increased in TEAM Snaps while Yellow colorization of players highlighten those who have dramatically declined in Snaps. Also, remember the %TS colorization was Green (Highly Team Used) vs Red (Lower Team Usages by Snaps). 

Thus, players with high%TS and High Late to Early (Purple) would be the the “best” players under these metrics! 


Running Back


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Tight Ends 



Wide Receivers 




Please use SNAP data with my other posts this week.




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