Week 4 Snaps Report

John Bush
October 3, 2019

Week 4 Snaps Report

The Week 4 Snaps Report gives players a view into the Team’s system. 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 4 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.

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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 Snaps Weeks 1 to 4, Average Snaps, and DIFFs

As we have 4 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).

Week 4 Snaps Report

This Table Below Includes:

Team

Week 1 to 4 Team Level Snaps
Avg of Snaps
Scaled Snap Averages
DIFF (Snaps difference between the last 2 weeks of team play)

Note: I colorized the Team Names by Snap Average. Green to Red!

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

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A Plot of Snaps vs Points Scored with Trends

What teams are speeding up (passing) or slowing down (rushing). I watch for trend shift to trade, drop, or acquire players.

The plot reveals teams that increased snaps vs declined in snaps (Left-Green to Right-Red).

Team Positional Level % of Team Snaps

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 4 Snaps Report include:

Team
Position
Week 1 to 4 Snaps
Average of Player Snaps Per Week
% SNAP Based Positional Usage
Bar Graph of the Positional Usages

These tables focus on the position level of each team. I suggest positional usages vis snap data is a data stream of importance. Looking for teams that highly use certain positions vs under-using others.

This data stream gets lost in the weekly matchups. Use these landscape metrics first to frame the weekly match-ups. Use the plots to slow scan this data to get the team backgrounds in positional usages.

ARI_ATL_BAL_BUF

Comments ARI is different in distribution from the entire league

ATL_BAL_BUF seem similar

CAR_CHI_CIN_CLE

CAR and CHI alike

CIN and CLE are alike

DAL_DEN_DET_GB

DET extreme RB usages Poor

DAL looks like CIN and CLE

GB like CHI and CAR

HOU_IND_JAX_KC

HOU KC like DET

JAX like GB

LAC_LAR_MIA_MIN

MIA, MIN like KC and DET

NE_NO_NYG_NYJ

NE like BAL

OAK_PHI_PIT_SEA

OAK, PHI. PIT, and SEA Like NE

SF_TB_TEN_WAS

TB and WAS Like DET

Find your own connections. Note the team’s success in their distributions. Are the snaps because of poor play etc or deliberate to winning? Deeper questioning!

A Plot of Running Back Team Snap Use

The following tables present the position, weeks 1 to 4 snaps, Snaps Averages, % Usages in all the Teams.

The top RB using teams are stained in Green vs the low use teams in red.

ARI/CHI/TEN/JAX/CAR/DEN/GB/SF/CIN
High Use Teams whose RBs are getting the work!
KC/DET/PHI/MIA/TB/MIN/HOU/OAK
Low Use RB Teams.

A Plot of Tight End Team Snap Use

TB/OAK/HOU/KC/WAS/NO/MIN/BAL/NE/PHI
High TE Usages but for blocking or pass-catching?
ARI/CHI/CIN/CAR/DEN/CLE/LAR/DAL/JAX/NYJ/TEN/LAC
Low TE Usages

A Plot of Wide Receiver Team Snap Use

DET/DAL/MIA/ARI/LAR/CIN/CLE/NYJ/PHI
High WR Usages
NO/TEN/WAS/SF/JAX/IND/GB/BUF/BAL/OAK/NYG
Low WR Usages

Positional Usages Ratios

Make Notes on the extremes. Ratios can pinpoint those Team’s dependencies as well as consider the entire team’s positional usages in total. Move to the high usage and away from lower usages.

WR Estimated Usages

Below is a way to estimate WR usages using both the WR vs RB and WR vs TE ratios. A simple summation should account for Team tendencies in passing to WRs. I sorted here by the Estimated WR Usage metric and annotated whether the team was High or Low in WR usages. See the plot below for a visual of this metric

WR vs RB Team Usages (Passing vs Rushing Balance)

High Passing vs Rushing Teams – DET/MIA/PHI/KC/MIN/DAL/TB/HOU/SEA/LAR/NYG/OAK. Note we assume more SNAPs equal more passing but team’s such as MIN confound these metrics. Yes, Diggs and Theilen are out there but no targets? It seems a deliberate non-use of the WRs. Caution.

I expect the low metric teams are RB heavy! Confirm the targets etc for final judgment.

WR VS TE (TE Importance in Passing)

The High WR vs TE team does not use TE! Caution! Focus on those teams lower in this ratio! However, TEs can be used for blocking vs passing so we must use other data streams such as targets for a full view.

Player Snaps Weeks 1 to 4

These tables give a weekly view of each player followed by their average and team importance as measured by Snap counts. I like to use the DIFF metrics to spot changes that can point to increased scoring opportunities. What players are more involved than previously. I have also generated a %Team Snaps metric as each player is judged vs the team total player snap 4 week total.

I use these metrics to confirm lineups, search for DFS cash vs tournament plays, and potential drop adds acquisitions for this week. These metrics have been sorted by the Snap Averages and give you the “weight” each player has on his team.

Injuries in these top players should have higher effects. I would handcuff these top players vs the mid-tier players. We all wish we had this data before our drafts!

Conclusion:

Use DIFFs to see positional shifts and tie that to this week’s predictions. Use the %TS for true snap based usages. The more data the better and I expect clarity in any extreme situations. Search for extreme increases and drops.

Running Back Snaps Week 1 to 4, Averages, %Team Snaps and Diffs in Snaps.

I noted green Z and red x symbols to note players increasing or declining in SNAPs. Find the extremes and determine why. Is this a “new” trend or a mirage?

Running Back Extremes DIFFs Snaps Between the Last Weeks of Team Activity.

Sorted by Increased Snaps to Declines in Snaps

Tight Ends Snaps Week 1 to 4, Averages, %Team Snaps and Diffs in Snaps.

Tight Ends Extremes DIFFs Snaps Between the Last Weeks of Team Activity.

Wide Receivers Snaps Week 1 to 4, Averages, %Team Snaps and Diffs in Snaps.

Wide Receivers Extremes DIFFs Snaps Between the Last Weeks of Team Activity.

Player Snaps By Teams (Player Environment Analysis)

Player Environment Analysis within Teams allows a deeper focus onto the player’s SNAP metrics. This data analysis can not be used correctly by 5 minutes of scanning. If you do not have the time for the task please use the player SNAPs data above. Week 4 Snaps Report.

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.