Week 7 Targets and Touches
This report, Week 7 Targets and Touches uses metrics on Team, Position, and Player targets and rushing attempts. The more opportunity is the higher the chances for collection of your Team’s fantasy points. This information is meant for deeper thinking and note-taking. If you have deeper benches these metrics allow you to jump into new player shares early for gambling. That is how I use deep benches. I continue to rotate players at the bottom.
I would use these Team metrics with my rankings and snap reports to finalize the week 3 lineups. This process is not automatic and easy. I am urging all players to dig deep into their decisions. There is plenty of software to automatically set-line ups. I suggest using a notebook and record your process. This lineup diary can be useful for you to spot weaknesses or strengths in your game by the week or season.
Rankings Link: fantasy-football-week-8-rankings-with-uncertainty
Snaps Report Link: Snaps Report
A Visual Plot of Target Averages Vs Weeks 1 to 7
I have discussed my top-down approach to data analysis. These bigger views allow a deeper feel for the deeper data. Below is the table of Team Target Averages using the first 7 weeks of target data. Week 7 Targets and Touches.
The following table in metrics Blue to Red (Highs to Lows) shows each Team’s Weekly Target Averages by Team. Additionally, the Scaled Target Averages show each Team’s metric vs the League average.
LAR is the top Team for Team Targets at 5.9 which is 1.5X above the league average. This is an extreme team. The opposite is TEN/SF at 3.3 Targets Per Player Per Game. They are a -1.1X below league average. In PPR move to Higher Teams.
Plot to See Team Scaled Target Averages Weeks 1 to 7
Visualize the High vs Low Passing Teams. Commit to memory for increased speed to analyze lineups and DFS plays.
Team Speed for Targets per Snap Table and Bar Graph
The speed of targets can be measured by targets per snap within a team. Here the 7 week average of each team’s targeting per snap was calculated and is displayed in this table. I scaled the metric to the league average.
The plot graphs the high and lows in targeting speeds
NYJ/MIA/KC/OAK/CIN/NO/MIN/ATL/NE/WAS were the highest teams in targets per snaps
HOU/BUF/SF/BAL/ARI/JAX/PHI/SEA/IND/DET were the teams with the lower targeting per snaps.
The issue is as well know teams can generate different levels of SNAPs and also be extreme in passing. Those variations can confound the analysis.
In general, I move to the higher target teams (LAR/CAR/ATL etc ) and then use the targeting rate as a secondary factor.
Weekly Target Averages, 7 Week Average, DIFF ib Last 2 Games, and Late vs Early Targets over the season.
Tracking Team changes are useful for your lineups and DFS plays. The colorization of week 1 to 7 target averages for each team allows a scan of the trends for that team.
Find the more predictable teams and those with high variation. For example, SF is steadily at the bottom of the league with 3.2 targets per game. NYG is generating 5 targets per pass-catchers per week. The higher team are stained in green and lowest in red. Move within PPR toward the higher target teams.
The DIFF metric is also included and that measures changes within a team’s last 2 games. Note CHI/IND/OAK/BUF/LAC are a higher targeting team int he last 2 weeks. TB/PHI/CLE/CAR are the teams that have declined within the last 2 weeks.
The last column is the Late vs Early Targeting metric that captures the last 3 weeks vs first 4 weeks. I sorted the team column beside the DIFF and LATE/EARLY by high LATE/EARLY numbers.
- Note CHI/LAC/TB/MIN/NO/HOU/MIA/DET have improved in targeting recently.
- DEN/ARI/LAR/WAS/ATL/BUF/PIT/OAK have declined recently.
- Why? New Trend?
A Plot of DIFF and LATE/EARLY Metrics By Teams
Teams With Positional Targeting Metrics Weeks 1 to 7
I provide a broad landscape of data. These tables include:
- Weeks 1 to 7 Target Averages.
- Average Targets.
- DIFF (Difference in Average Targets for that Team’s Position from the Last 2 Games)
- LATE vs Early DIFFs
- %TT (Percentage for that Position’s Targets vs Total Team Targets-Usage Metric)
- A plot of each team’s positions and their %TT
I use the DIFF to see recent changes. Why? The %TT metric displays the positional usages and level of importance for the Team. An injury in a weakly used position is not as critical as a strongly used position. Highly used positions can be deeper into the significance of players. Also, a change in players in weakly used positions can inspire change and you can catch on before others.
Focus on the extremes and unusual %TT!
Summary Data Table and Plots of Positional %TT
These figures highlight the landscapes for each position. The extremes will be of critical interest. Highest in Green and Lowest in Red! Move in PPR to the higher targeting teams.
Player Centric Target Metrics within Team Environments
Following up on the positional analysis above, I turn to player level metrics for a deeper understanding. I sorted players by %TT within the position. Find the extremes and pecking order for injury. Great reference for Week 7 in case of injury, just come back here and see the next man up. Also in high use situations, stashing key handcuffs is a solid approach in deeper bench teams.
- Weeks 1 to 7
- Average Targets
- Late vs Early
- Plot of Players vs Position and %TT
Player Average Targets and %TT by Position
I also like to see the player within the position outside the team. What is the player’s success relative to others in that position in terms of %TT? One might propose that this list is the order of handcuffs as well. CMC’s handcuff could be worth more than 2.5% than Karma’s handcuff etc. Just a mental exercise to craft stashes in deeper leagues.
Variation Stats which players are dependable? Who are the players that have improved or declined? Why? Caution?
I love to generate the various levels of Target metrics to “catch” all the data for interest. I use to think about my teams, drop/adds/and lineups. I chase multiple data streams for a full landscape in my thinking.
One might jot down key observations by teams, positions and or players. Then try to fit these elements together. Practice and note in your playing diary (Handicap your own decisions). Please let’s move away from the tire line “They told me to do it”!
Own your line-ups!
Team Level Running Back Average Touch Metrics
As RBs can rush as well as pass-catching, we must look into the combined role they have on a team. The “touches” metric tracks this RB activity and enables a deeper view of the Team’s RB usages.
This table presents the Team Level Running Back Average Touches from Weeks 1 to 6, Average Touches, Scaled Touches, and DIFFs. These metrics are a landscape view of the leagues and teams. High Touching Teams would have significant RBs in PPR while Low Touching Team would not. Focus on RB committee structure and pecking order in case of injury as these new players should inherit a nice situation.
High RB Touch Teams- Green Stained DEN/DAL/SEA/JAX/HOU/TEN/ARI/
Low RB Touch Teams- Red Stained KC/BAL/DET/BUF/OAK/NYG/MIA/MIN/ATL
A Plot of Average Touches and DIFFs
DIFFs capture recent changes – Purple Line. Above 0 increasing Touches/Below 0 decreasing RB Touches. DAL is a high touches team that increased in DIFF! vs SEA High Touch Team with large decrease in touches. Look for extremes and unusual patterns. I added an orange star to show the teams High in Touches and who have improved recently.
Players RBs (Within Teams) Touches Weeks 1 to 7, Average Touches, DIFFs, Late vs Early Metrics
Looking within Teams for RB pecking orders, DIFFs in touches, and Averages in Touches. These give us clarity for the Teams RB crew. Look for new improving players or declining players. Note the extremes and then add or drop players accordingly.
Players RBs (Touches Weeks 1 to 7, Average Touches, DIFFs, Late vs Early Metrics
Players have been sorted by Average Touches from High to Low (Purple to Yellow). Focus on all the layers for handcuffs and those RBs with independent value as well! Use DIFFs to catch new players on the field etc.