Targets Per Minute Week 3 to 1 2017

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By John Bush

Targets Per Minute Week 3 to 1 2017

Targets Per Minute Week 3 to 1 2017 analysis starts with a landscape view of each team’s total number of players targeted by the week. How concentrated are each team’s targets?


Targeting Concentration Per Team

Interestingly, in the following table, the least concentrated targeting team was CLEand the most targeting team was PIT.  I used this data to look for adding players via drop-add or in trades.

The teams at the bottom of the table are distributing their target to fewer players. Finding a 2 or 3 deep player there may be more valuable than commonly thought!

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Targets Per Snap Averages, Scaled Figures and Bar Graph of Each Team Current Scaled Average of Passing Speed 

This table lists from High to Low, Average Targets Per Snaps for each team. I took those numbers and scaled to the league average. Scaled contains these numbers above and below league average. This allows easy detection of those teams who are faster passing teams (ie MIA, CHI, NYG, and ATL) vs the slower passing teams (IND, DEN, BUF, WAS BAL and HOU)

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TEAM Normalized Positional Targets Per Snap and Week 2 to 3 Changes.

In order to adjust for the differences between RBs, TEs, and WRs in Targets per Snap, I normalized the data so that an RB receiving a 50 is the same as a WR receiving a 50.

The tables present these normalized numbers by weeks 1 to 3, followed by a 3-week average and finally a % change from week 2 to 3. Below the table is the graph of the 4 team’s % change.  IE in ATL it is clear the TE Hooper was not again not highly used as he was in week one and the WRs were at their 3 weeks highest levels! Look at HOU’s TE guess who is adding Ryan Griffin?  Use to understand the past weeks game better.

Look closely at all the critical team depth charts with all this data in mind! This is required metrics for your bye week replacements coming up! 


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Top Team Positional Users by Positions

The tables are segmented by RB, TEs, and WRs. Each table is followed by an area graph for a visual view of the data and a Top or Bottom Team focus. I scaled the data to the positional averages to add meaning to these graphs. Clearly, see the tops and bottoms in the NFL!


Running Backs

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Tight Ends

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Wide Receivers

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Normalized Players Targets Per Snap by Teams and Positions with a Scaled to the Average Calculation

Scaled the Data (0 to 100) allows us to look at the positions and compare players. If the top TE is compared to the top RB then both players would be rated 100. Simple numbers might be like 5 to 10 and would not factor in the natural bias to WRs etc.

My numbers allow judgments across teams and positions in trades, adds, drops or lineups! Grouping within teams gives you that comparison as well in the event of injury etc.


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