Week 13 Snaps Report

Week 13 Snaps Report

The Week 13 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 13 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 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 13 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 Snaps Averages

Previously, I have discussed rushing vs passing bias at the team level vs the snap averages. I would love to predict a teams activities with their snaps.

The top teams in Snap speeds were NE/ATL/TB/PHI/BAL/SEA vs the slowest teams being WAS/OAK/TEN/DEN/NYJ.  Note the style of team play (Rushing vs Passing)

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The Plot Visualizes the Spread of the Team Snap Data vs Recent Changes (late vs early).

The graph below gives a view of team speeds High to Low (left to right). Also, changes in the team’s speed was calculated as late vs early metric. Has a team altered its speed of play? Does that change associated with new events etc?

The extremes in Late vs Early Snaps were PIT/LAC/ATL/CAR/DET (More Passing?). These teams have sped up. TEN/HOU/BAL/MIN had slowed down recently (More Rushing?).

I also have noted in my “secret” research that snaps speed associates with top positional Fantasy Points Per Game as shown in the data within the multi-plot below. At the bottom of the graph, I have annotated the top 6 Teams in FP/G within the QB/RB/TE/WR with one-letter symbols.

16 vs 8 is the distribution of the top 6 per positions (24 total). The 16 were found in the top 12 teams in snap speeds while the 8 of the top positions were in the 20 other teams (slower snaps).  The ratios would be 1.33 (fast snap teams) vs 0.4 (slower snap teams).

That can be thought of as 3X more likely for fast teams to have superior performing players! (Never seem this finding before!) 2020 research topic!

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Positional Team Usages Measured by %Team SNAPS

In the playoffs subtle metrics can lead to solid results. Time is now to “see” how teams use their positions. Tiebreaker material for lineups and DFS this week.  Unless your are down significantly, stick to your players that got you into the playoff. If you are lucky enough to have closer choices than these metrics can help.

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These metric tables cover 4 teams presenting average positional snaps from weeks 1 to 13. I however, used the last 5 weeks to establish the current team positional profiles. I also list the % Team Snaps (%TS) to give glance at the big picture for each team.

Additionally, I plotted the teams, positions, and %TS in a Bar Graph to see the takeaways. Sometimes the matchups are a result of team usage systems not in the other team’s defenses.

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Players Snap Based Usages 

Use these usages %TS as a way of thinking about how a player is used. 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/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 8 to 13 average snaps, a last 6-week average, and a Team Snap %.   (%TS)  

I sorted High %TS to Low.  The colorization allows the focus on the extremes. 

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Running Backs

 

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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 and Bar Graphs present the player Snaps in their position within Teams, Weeks 8 to 13 Snaps and their recent 6-week Snap Averages. I colorized the Snaps within each team. This colorization allows a scan across and down the players and positions. 

I added player SNAP Share metrics (% Team Snaps -%TS) to “see” the last 5 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. 

Additionally, 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.

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Week 13 Snaps Report

Please use SNAP data with my other posts.

defense-against-the-positions-weeks-11-to-16

fantasy-football-week-14-rankings-with-uncertainty

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