Week 6 Snaps Report

Week 6 Snaps Report

The Week 6 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 6 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. 

make-better-decisions-combine-datasets

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


Team Average Snaps 

As we have 6 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. Fast Teams are more passing centric vs slower snaps teams are rushing heavy. (Data from my textbook studies).

I colorized the team names (Green to Red) based on the Average Snaps High to Low (Purple to Yellow). I assume the level of pass vs run is based here on team speeds. 

** In summary, I favor pass-catchers from faster teams and rushing RBs from slower teams

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Average of Weeks 1 to 6 Team Snaps

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Team Snaps Weeks 1 to 6, Average Snaps, DIFF Late vs Early, and DIFF in Last 2 

What teams are speeding up (passing) or slowing down (rushing). I watch for 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.

Therefore, I present DIFF Late vs Early which looks at the first 3 weeks of snap average vs the last 3 weeks of snaps. This metric is a landscape view of a change in snaps given potential changes in each team from an early vs most recent. I sorted the table from late vs early Snap averages.  HOU/IND/JAX/GB/NE/OAK/SEA/MIN (green) have sped up (more passing?). DEN/WAS/PIT/CLE/CAR/NYJ/ARI (red) have slowed down (more rushing?) 

The DIFF last 2 metrics will catch the most recent snap changes for faster detection of any team changes. I would combine your scan of weekly snaps, average snaps, DIFF Late vs Early and Last 2 Diffs. I would do that order of data consideration to fit the team snap activity into a prospective. 

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The Plot of DIFF Late vs Early and DIFF Last 2 Games

The plot reveals teams that increased snaps vs declined in snaps vs Snap Averages -Speed Left to Right (Fast to Slow Teams). The level of Broad DIFFs vs Narrow DIFFs can spotlight issues for further questions. Why? 

High DIFFs Last 2 Games are HOU/SEA/NE/DAL/TB/MIA/TEN/CLE

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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 5 Snaps Report include:

  • Team
  • Position
  • Week 1 to 6 Snaps
  • Average of Player Snaps Per Week
  • DIFF Last 2 games played
  • 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 to weekly matchup. 

The DIFF metric catches any recent extreme changes. These can spot problems that are actionable to deal with. 


ARI_ATL_BAL_BUF 

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CAR_CHI_CIN_CLE

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DAL_DEN_DET_GB

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HOU_IND_JAX_KC

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LAC_LAR_MIA_MIN

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NE_NO_NYG_NYJ

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OAK_PHI_PIT_SEA

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SF_TB_TEN_WAS

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Team Snap Based Positional Usages with WR/RB and WR/TE Ratio Metrics

Use these usages as a way of thinking about how a team is operating. 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 are germane to your teams. Find your own connections. Note the teams that have success using in their distributions. Are the snaps because of poor play etc or deliberate to winning? Deeper questioning! 

Bonus as I included WR vs RB usages and WRs vs TE usages. Extremes are worth noting for taking advantage of.  WR/TE for example in ARI is 1.44 and thus suggests poor TE usages. Find the outliers and use in DFS and weekly lineups. 

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Plots of WR/RB Ratios WR vs RB Usages

KC/DAL DET/HOU/PHI/MIA/TB/OAK/CLE – WR heavy usages

ARI/JAX/DEN/TEB/SF/BUF/PIT/NO/CIN/GB/ATL/CHI – RB heavy usages

Find the players that benefit using these conditions.

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Plots of WR/TE Ratios WR vs TE Usages

DAL/CIN/CHI/CLE/LAR/ARI/DET/LAC/CAR are the High WR vs TE from weeks 1 to 6. I do note some teams looking for changes. 

NO/NE/SF/WAS/NYG/SEA/MIN/HOU/NYG/PHI/IND/BAL are high TE usages teams vs the WRs. Worth monitoring the TE2 as injury backups. 

i fig week 6 snaps


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 6 snaps and Snap Averages. I colorized the Snaps within each team. This allows a scan across and down the players and positions. 

I use the deeper Team Player Snaps environment analysis for lineups in seasonal and DFS, drops adds, handcuffs identification, previous week game scripts for positions usages and seeing the upcoming late-season bloomers.

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. The DIFF metrics can spot new trends for your use. do not miss that player you need.

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Player % of Team Snaps (%TS) vs DIFFs Late (Last 3 Weeks) vs Early (First 3 Weeks)

I believe that %TS metrics hit directly to usages from the Team. Highly used players are a move toward situation vs low used players which are a move away from. Use in DFS and weekly lineups, as well as trades and or drop, adds. 

I also included a larger view of player snaps by the DIFF Late vs Early stat. These stats give us players moving up or down in team snaps. Why? The best players are high in %TS and high in DIFFs! 

For Example, in ARI, KeeSean Johnson is the 5th %TS player and has improved by 45 DIFF. I place him on watch status as a player that moves up with injury etc. These are the players to find and note. Not all are a must-add but are just on the watch list! 

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 Running Back Team Snap Use Measured by %TS vs DIFFs

I sorted the players from high team usages purple to low team usages yellow. Use the DIFF to focus on recent changes in increases or decreases in Snaps. Watch for changes. 

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Tight End Team Snap Use Measured by %TS vs DIFFs

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Wide Receiver Team Snap Use Measured by %TS vs DIFFs

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DIFFs Late vs Early Extremes by Position

I use these metrics to “see” potential acquisitions for my team.

Note Walton McKissic Snell Henderson Penny Ingold Hillard are on my slate for further research. 

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Note Seal-Jones/Dwelley/Williams/Culkins as watch players

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Sherfield/Pringle/Doss/Miller/Wilson

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Please use SNAP data with my Rankings this week.

fantasy-football-week-7-rankings-with-uncertainty

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