Week 7 Snaps Report
The Week 7 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 7 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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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.
Team Average Snaps
As we have 7 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). Assume the level of pass vs run is based here on team speeds **.
I colorized the team names (Green to Red) based on the weekly team snaps (Purple to Yellow). I also present the overall snaps average (green to red) and snaps scaled to average (blue to red). Scaled metrics visually give you an impact value of the metric! FYI NE is 8.1 X over the league average vs PIT was a -8.8 X below the league average. Caution on PIT players for sure. Use to adjust your views of teams.
** In summary, I favor pass-catchers from faster teams and rushing RBs from slower teams
Scaled Average of Weeks 1 to 7 Team Snaps
- The high fast teams are in green: NE/BAL/IND/LAR/SF/SEA/ARI/DET/TB/HOU/PHI/DAL
- Losses -0/2/2/3/0/2/3/3/4/3/4/4
- (Total 30 Average 2.5 Losses)
- Slow Teams are
- (Total 44 Average 4.0 Losses)
Late vs Early Snap Averages vs DIFFs Last 2 games
These data give a view of the entire season (LATE vs EARLY first 7 weeks) in Team SNAPS as compared to the Last 2 Weeks (DIFFs 2 Weeks). As above teams that are getting faster may be winning more in the next upcoming games vs slowing down and losing more.
The LATE vs EARLY metric catches this big turn. MIA/HOU/MIN/NE/JAX have sped up in snaps vs BUF/CLE/LAR/KC which have slowed down.
JAX/NO/TB/DET/NYG/LAR/CLE has sped up in the last 2 games played (DIFF Last 2 Games). HOU/BAL/ATL/GB/SF have slowed down! Watch for new trends in these teams.
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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.
Visual Late vs Early SNAPs of the Teams
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.
Key into these larger trends. Watch for player changes that may have fueled this metric.
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 7 Snaps Report include:
- Week 1 to 7 Snaps
- Average of Player Snaps Per Week
- % Team Snaps (%TS)
- 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 for this week’s matchup.
You must go through a few times and focus on the extremes. As I tell my students that there is a Big Difference with being familiar with vs knowing. Move toward knowing. Next year maybe write an essay on each team for 2020. Essay needs the knowing! (In my Textbook)
For Example ARI TEs not used! 23% is weak! Bal RBs are underused at 23% but loves its TEs at 38%! Find these keys to unlock your plays!
Team Snap Based Positional Usages
Use these usages %TS 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. Good tiebreaker as well.
Note the teams that have success using in their distributions. Are the snaps because of poor play etc or deliberate to winning? Deeper questioning! All Positions include weeks 1 to 7 average snaps, a grand 7-week average, and a Team Snap %. I sorted High %TS to Low. The colorization allows the focus on the extremes.
Suggest you focus your handcuffs on the high use teams. Example Chase Edmonds is from ARI 36% TS! Caution on DET pickups this week! Det is the 3rd worst RB usage team.
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 7 Snaps and their grand Snap Averages. I colorized the Snap levels within each team. This colorization 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.
Player % of Team Snaps (%TS), DIFFs Last 2 Games, (DIFF), and Late (Last 3 Weeks) vs Early (First 4 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! The L vs E DIFF metrics show a bigger picture for each player.
Again make notes and place key players on your teams and watch list!
Player by Team Positional Snap Use (Week, Average, %TS, DIFF, and Late vs Early)
I sorted the players from high team usage (%TS) blue to low team usage red. Use the DIFF to focus on recent changes in increases or decreases in Snaps. Watch for changes in the wider Late vs Early Snaps.
I also highest each player by 12s so the strong Green Players are top 12, light green next 12, etc. This is a quick view of players’ hierarchy!