Week 12 Snaps Report
The Week 12 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!
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Landscape Data Informatics for my
Week 12 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.
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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 informatic gold. This is my journey within Fantasy in a nutshell! I wish to “show” others my approaches as well.
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As we have 12 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. I also present the current Team Bias in passing (purple) or rushing (orange) in a few figures. Consider the overlay of team system of play vs their snap speeds. (more research for 2020)
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Analysis of the Bias Metrics vs Snaps Averages
Fast Teams (NE/TB/SF/PHI/SEA/LAR) are more rushing centric vs slower snaps teams are passing more. I assume the level of pass vs run is based here on team speeds **.
The fast teams as determined by Average Team Snaps (Blue to Red- High to Low) are marked in Green. These are biased toward passing. (ATL TB JAX PHI PIT CAR DET MIN SEA CIN NYG).
In the table below, the slower teams (Red stained) are (WAS LAR OAK TEN NYJ HOU ARI BUF MIA DEN). They are biased in more to rushing. PPR rushing players highly supported in these teams.
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The Plot Visualizes the Spread of the Team Snap Data vs % Passing and %Rushing Bias.
This Bar Graph plots the Last 6 weeks of median Team Snaps from High to Low (Passing to Rushing centric Teams). The key extremes would be the bright purple (passing bias teams) and deep blue (rushing based) teams. Good DFS reference and redraft lineups guide.
I also included the DIFF metric to note Team speeding or slowing down in recent weeks. Changes in coaches? players? etc). The teams were sorted by the last 6 weeks of Snaps.
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Team Bias Rushing vs Passing%
Is there a connection to team snaps and a team’s style of offensive? Most pundits dwell at the player level but I practice a TOP down approach because I think it allows players to set the landscape for their decisions.
The distribution of Team Biases either Rushing or Passing is shown vs the Top to Bottom Yes, it appears that a team’s Snap average can be used to associate a style of play. (BELOW)
Analysis of the Bias Metrics vs Snaps Averages suggests passing in Fast teams. 50% of passing teams are in the top 11 Snaps Average Teams vs only 12% of rushing biased teams.
That metric shifts in the slower teams with those 21 teams that have only 66% are rushing based vs 38% are passing based. Big differences! Can we use this relationship in 2020 drafts? I will explore and report in my textbook FYI.
Use this information above/below to formulate DFS and Lineups with my analysis of Vegas Lines, Defense Metrics and my Rankings. (article link at end of this article)
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Players Snap Based Usages
I lean to the % of Team Snaps (%TS) as a metric that cuts through any human biases based on name recogniation etc. Use of this metric %TS leads to a way of thinking about how a player is used. Find the extremes and use that data to move toward or away from players. I will let my readers scan the data and decide what key facts/players are germane to your teams. Find your own connections. Good Tiebreakers as well.
Note the teams that have success using in their player distributions. Are the snaps because of poor play etc or deliberate to winning? Deeper questioning!
All Positions include weeks 7 to 12 average snaps, a last 6-week average (recent snaps), Team Snap %, and DIFFs of seasonal vs recent Snap medians.
I sorted High %TS to Low. The colorization allows the focus on the extremes.
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Running Backs
Suggest you focus your handcuffs on the high use teams now before the playoffs!
Negative DIFFs Gurley/Bell/Fournette/Cubb/Jacobs etc
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Tight Ends
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Wide Receivers
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Player Snaps Within Teams Weekly and Snap Averages -Environment Analysis.
The following tables are team-based.
The first table presents the player Snaps in their position within Teams, Weeks 1 to 12 Snaps and their Snap Averages. I colorized the Snaps within each team. This colorization allows a scan across and down the players and positions.
The second table contains my neat metrics of 6-week recent snap median, player %TS, and DIFFs. I added player snap share metrics (% Team Snaps -%TS) to “see” the last 6 weeks of the season so far. These metrics capture the Team usage of all players. Watch for changes but use these Snap Shares as a foundation of your analysis. The DIFF metric notes changes in player usages within their most recent games.
Interestingly, I use the deeper Team Player Snaps environment analysis for my lineups in seasonal and DFS as well as drop adds, handcuffs identification and previous week game scripts for positions usages.
I look for upcoming late-season blooming player, players new on field, and rookies coming on late this season. Look for the new/novel/etremes/pattern shifts/.
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Positional Player Metrics from the Last Game Played
These positional based tables of players that were sorted by Week 12 or the game played. I present the top group in the most recent times. Surprises? Sanders the 3rd highest RB. Drake was the 4th RB etc.
RBs
TEs
McDonald the 5th TE, Know 6th TE, etc.
WRs
DJ Chark the top WR in Snaps, Beckham was number 2 WR, etc.
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Improving Players By Snap Increases
I assume more snaps can lead to more scoring opportunities. 3 Blocks of the top DIFF players.
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Declining Players By Snap Increases
Losing snap shares is not positive to scoring?
