By John Bush


Targeting Concentration Per Team- #Average Team Targets By Week

  • Landscape view of each team’s total number of players targeted by the week. 

  • Sorted by Count of Players Targeted Total Weeks 10 to 13

  • How player concentrated are each team’s targets?

DEN, LAR, ARI, BUF, LAC, and TB have spread the target to large numbers of players per weeks.

NYJ, IND, CAR, KC, MIA, and TEN have concentrated their passing targets. 




Targets to Positions Per Weeks 9 to 13 

  • AVG Targets to Positions By Team

  • Sorted by Week Avg Data



  • PIT is extremely high in RB Targets

  • NO, CAR, LAR, and SF are high in RB Targets

  • PHI is extremely poor in RB Targeting Recently

  • NYG, GB, OAK, ATL, and DAL are weak in RB Targets



  • HOU is extremely high in TE Targets (hence the run to Anderson this week after CJ hit the IR)

  • SEA, TEN, KC, PHI, IND, NYG, and NYJ are high in TE Targets

  • NO is extremely poor in TE Targeting Recently

  • GB, LAR, CHI, JAX, DEB, DET, and TB are weak in TE Targets



  • MIA HOU are extremely high in WR Targets 

  • JAX, PIT, BAL, NYJ, LAC, DET, and GB are high in WR Targets

  • CAR and SF is extremely poor in WR Targeting Recently

  • IND, LAR, CLE, CIN, and MIN are weak in WR Targets



Average of Targets Per Snap Weeks 9 to 13 by Position and Team




Positional Targets Averages and Usage By Team and Weeks 9 to 13 (Recent)

Running Back


PHI, TB, NO, OAK, KC, ARI, GB, and BAL Have used the RB in the Passing Game

NYJ, DAL, MIN, TEN, ATL, HOU, CIN, and LAR have used the RB very little in the Passing Game




ARI, KC, NE, HOU, SEA, PHI, TB, and IND are using the TE higher than most

GB, NO, CAR, MIN, DAL, CIN, BAL, and PIT have not been using the TE a lot! 


Wide Receivers


NO, NYJ, ATL, DEN, NYG, and MIN are at the top of the league!

IND, SEA, PHI, SF, and OAK have been under-utilizing the WRs. 


Players Normalized (100 to 0) Targets Per Snap By Week and Avg! 

My normalized numbers allow judgments across teams and positions in trades, adds, drops or lineups! Not if a player has one snap and is the targeted they will earn a perfect score of 100. See Taiwan Jones! You must downplay such extreme data as the sample size is too small.

By Placing the Data on a 0 to 100 (worst to best scale) we can judge across the positions as well as internally within a team or position!

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Trade help? I would not trade an RB or for a TE with a lesser average unless they figure to explode in the playoffs!

The data needs context! You must think about what data I present! I would certainly use recent data to predict this week but a landscape view is the context for a deeper based though out plan!


Running Backs






Tight Ends



Wide Receivers



Team Positions and Players Normalized Targets Per Snap (100 to 0)  Week 9 to 13 Averages

Team context is the final view needed for your decision! What is the position of the player you are considering? What is their recent role and usage?

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