Week 3 Snaps Report
The Week 3 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 works!
Landscape Data Informatics
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 link for starting your exploring.
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Consider the landscape views of multi-data veins invites mining for informatic gold. This is my journey within Fantasy in a nutshell! I wish to “show” others my approaches as well.
Team Snaps, Points, Red Zone Attempts, Scoring Efficiency, and Scoring By Snaps (All In One)
As we have 3 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.
This Table Below Includes:
- Week 1 to 3 Team Level Snaps
- Avg of Snaps
- Avg of Team Points Scored
- Red Zone Attempts over the 3 Weeks
- Red Zone Scoring Efficiency (Points Scored vs Red Zone Attempts)
- Score By Snaps (ratio of Points/Snaps *10 for Scale)
Note: I colorized the Team Names by Scoring EFF. Green to Red!
In summary, I favor players from Teams with High Score by Snaps and Scoring Efficiency. I would apply caution to the players from the opposite type of teams.
A Plot of Snaps vs Points Scored with Trends
BAL/ NE/ SF/ KC/ DAL/ LAR/ SEA/ High Snaps and Points
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A Plot of Scoring Efficiency vs Score by Snaps
Key High Score By Snap Teams: NE/KC/DAL/BAL/SF/MIN/NO/CAR.
Super Bowl Winner in here? Does Week 3 Score By Snap Predict the rest of Season? (2020 Textbook Subject!)
Caution on MIA/NYJ/DEN/CLE/CHI/CIN/OAK/PIT
Team Positional Level % of Team Snaps
I always suggest players use a broad view of a Team’s activities. I start in this Snap Report by looking at the positions. Note all metrics are colorized High to Low.
These tables from the Week 2 Snaps Report include:
- Week 1 to 3 Snaps
- Average of Player Snaps Per Week
- % SNAP Based Positional Usage
- Ratio Team WR Use vs RB Use
- Ratio Team WR Use vs TE Use
These tables focus us into the position level of each team. I suggest positional usages vis snap data is a data stream os importance. Looking for teams that highly use certain positions vs underusing others.
Make Notes on the extremes. Ratios can pinpoint those Team’ positions as well as consider the entire team’s positional usages in total. Move to the high usage and away for lower usages.
A Plot of Running Back Team Snap Use
The top RB using teams are stained in Green vs the low use teams in red.
- High Use Teams whose RBs are getting the work!
- Low Use RB Teams.
A Plot of Tight End Team Snap Use
- High TE Usages
- Low TE Usages
A Plot of Wide Receiver Team Snap Use
- High WR Usages
- Low WR Usages
WR/RB and WR/TE Snap Ratios
The two ratio metrics define the team usages vs the key WRs (In PPR). These, in a nutshell, pinpoint the team extremes. The plots focus onto the RB, TE, and WRs team usages across the league.
WR/RB Snap Ratios
I colorized the Team Bar Markers Green (High Use WR vs RB) and Red (Low WR vs RB). You can consider high ratio teams will be pass happy team vs rushing heavy teams being in the low ratio teams.
Pass Happy Teams DET/PHI/MIA/SEA/KC/CLE/HOU/TB/LAR/DAL
Rush Heavy TEams TEN/GB/JAX/SF/ARI/IND/CHI/CAR/NO/BUF/WAS/DEN
WR/TE Snap Ratios
This ratio gives use more positional information than just rushing vs passing. Given the TE is a specialized player, some teams use them and others do not for pass-catching.
The High Ratio Teams do not use TEs a lot – blue colorization. I would be very cautious of using TEs from these teams. (ARI/CLE/CHI/DEN/LAR/CIN/DAL/NYJ/CAR/MIA/LAC). Remember that TE block as well and these metrics can not differentiate the two flavors of TEs. It’s just a trend.
The Low Ratio Teams uses their TEs much more and potential key pass-catching TEs can be on these teams. (NO/WAS/BAL/OAK/MIN/NE/KC/IND/HOU/TB/ATL)
Player Snaps Week 1 to 3, Average Snaps and Week 3 vs 2 Snap DIFFs.
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.
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 player 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. 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 3 /Avg/ DIIF Week 3-2
RBs Extremes DIFFs Snaps Week 3 vs 2
Tight Ends Snaps Week 1 to 3 /Avg/ DIIF Week 3-2
Tight Ends Extremes DIFFs Snaps Week 3 vs 2
Wide Receivers Snaps Week 1 to 3 /Avg/ DIIF Week 3-2
Wide Receivers Extremes DIFFs Snaps Week 3 vs 2
Player Snaps By Teams (Player Environment Analysis)
Player Environment Analysis within Teams allows a deeper focus onto the players 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 3 Snaps Report.
I use the deeper Team Player Snaps environment analysis for lineups in seasonal and DFS, drop 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.