Week 2 Targets and Touches

 

Week 2 Targets and Touches

Week 2 Targets and Touches My weekly routine is analyzing various fantasy football metrics including:

We must deal with decisions under uncertainty in fantasy football.

**A range of potential futures can be identified at level three uncertainty. A limited number of key variables (See Above) define that range, but the actual outcome may lie anywhere within it. There are no natural discrete scenarios.

** https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/strategy-under-uncertainty#

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Week 2 Team Target Averages and Scaled Targets vs League Average

Week 2 Targets and Touches. I always begin in a top down approach and in this case, I focus onto the top target generating teams. Clear value in PPR scoring! Additionally, I highlight the extremes from High (green) and Low (red) of all the NFL teams. 

I also consider the amount of data and our floor is not solid with just one week of data but the more weeks then better these metrics will become. I used totals as an way to highlight Team targets over the last 2 weeks. 

CIN is the clear top at 94 targets. Burrow is pass-happy and will add value to pass-catchers including TEs (Drew Sample was a great TE2 add this week). If Burrow can move forward he will be a solid playoff QB. Watch.

ATL was number 1 in week 1 and the 2nd targeting team. This explains Hurst and Gage’s values. Watch for pass-catcher RBs.

DAL, PHI, KC, MIA, NYG, and CAR are also pass-happy teams. Add Value to their pass-catchers! 

The flip-side are LAC, SEA, SF, NE, CLE, LAR, MIN and BAL. Substract value from their WRs. Rushing is more favored. Stock-pile handcuffs etc. 

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Scaled Team Targets Week 1 and 2

Week 2 Targets and Touches. The visual view of the team targets is shown below. I like to graph data points in comparison of the league average. This allows me in a glance to see what teams were above or below average of Targets.

Additionally, I colorized the bars with green vs red. Consider the reasons why and is this week going to be different? Finally, use Vegas and Defense against the Positions (DAPS) to investigate that question! 

I present Scaled Team Targets first to showcase Teams across the league vs league targeting averages. We must know the extremes for our finger-tip knowledge in line-ups and DFS plays. 

KEY Extremes in Green and Red Bars of High to Low Targeting Teams. Move in PPR to the green and away from red team on average. (Yes other data can and will modify that trends but we start their first)

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Week 2 vs 1 Team Target Differences (DIFFs)

I also include in my report the weekly differences in team targeting. This data stream becomes stronger as the season progresses.

Improvement vs Declining Teams and the reasons:

  • Injuries
  • Game Scripts
  • New Players In or Old Player Out
  • Weather (later in season -snow/rain/winds)

Green Teams Improved in Targets Week 2:  JAX/CIN/NE/MIA/KC/PIT/NO/CAR/DEB/LV 

Red Teams Declined in Targets Week 2: IND/TEN/ATL/CLE/GB/DET/BUF/SEA/CHI/SF

Watch for changes in Week 3 game scripts or players to add or negate to these Team trends

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Targets Per Second (Team Speeds)

I now add to the report a team’s targeting speed. Faster teams should give more targets vs slow teams. Move to pass-intense faster targeting teams in PPR and DFS plays.

The Average Team Targets/Second and Weekly DIFFs are show below. 

The fastest targeting teams are NYJ/DAL/KC/ATL/WAS/PHI/NE/CIN/CAR/MIA/HOU

Slower targeting teams are LV/CHI/BAL/JAX/CLE/GB/SF/SEA/LAR. 

Note MIA vs JAX targeting speeds – 🙂 

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Team Average Week 1 and 2 Targeting Speeds

Bar Graph landscape to highlight the level of differences between teams. The extremes are NYJ and DAL vs CHI and LV. 

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Targeting Speed Efficiency for Teams By Positions and Ratios of Use

I next add to my report, a numerical Targeting EFF metric from 100 to 0 *high to low*. Metrics have been colorized to showcase the team and positions within the team. I have seen the importance of documenting a Team’s positional targeting usage in PPR! 

Additionally, I like to “see” a snapshot of the Team’s WRs vs RB and TEs. A ratio of WR/RB and WR/TE give me a reason to add or drop players or as trade information beyond the stale old trade calculators. 

I look at the positional TGT EFFs and get a sense of the Team’s targeting and then confirm with a ratio view. For example, ARI does not use it TEs much as thus its WR/TE is a whopping 2.9. (CAR GB and NE also are like ARI in TE usages). Scan ratios then is also a way to take in the data.

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The Highest WR/RB ratios are found in BAL/NE/NYJ/PIT and TEN. Move to the WRs in PPR and avoid PPR catching RBs on those teams for now. 

The Lowest WR/RB Teams are IND/LV/NO/NYG/SF. They use the RB >> WRs in pass catching. Note Dion Lewis in NYG should be valuable. 

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The Bar Graph that follows the table data is for a visual view of the positional relationships. Again in the week’s process, I suggest starting using this report to add clarity. Also, It is way to drill top down into the week’s data. We in science love to go top down and in fantasy football it is also the way I use and suggest. The top down balances the weekly nature of this game. 

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Target Team Positional Efficiency and EFF Ratios

I discussed my approach and these graphs are a simple way to focus onto Team Positional Balances! High to Low. Move toward teams using key positions in your line-ups and DFS plays. 

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Targets and Efficiency Within Positions.

Week 2 Targets and Touches. You must also consider the player’s ranking among their position.

High in the team but Low in the league is different than High and High.

That should give your concerns for league lower players in targets. Weak teams are known to have poor depth in PPR scoring. Scan through the positions and find the unusual and extremes. ** Assuming FF data is normally distributed?

 In an extreme value analysis, extreme events are defined to be those observations in a sample which are unusually high, or low, and are therefore considered to occur in the tails of a probability distribution.

Standard statistical methods are designed to characterize the mean behavior of a process or data sample and are therefore not generally useful for capturing this tail behavior.

https://www.lancaster.ac.uk/maths/research/statistics/extreme-value-statistics/

 

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Running Backs

These RB tables have been sorted by Total Season Targets (weeks 1 and 2). I note interesting events (see above). N Hines 8 vs 1 ? McCoy 1 vs 7 targets? CEH 2 vs 8? Etc. Find and think! 

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Tight Ends

 CIN Drew Sample 1 vs 9 targets weeks 1 to 2. J Graham 7 to 1? Schultz 4 to 10 etc.

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Wide Receivers

J Jones 12 vs 4 (injury)  DHOP 16 vs 9 (variation expected?)

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Team Position and Player Targets and Positional Targeting Efficiency 

Week 2 Targets and Touches. I next drill down into these metrics within each Team. Fantasy Football is a weekly Team based game! Secondly, I added the Targeting Efficiency (targets by snaps – normalized on a scale of 100 to 0) for each player.

These tables contain:

  • Team
  • Position
  • Player
  • Positional Target Efficiency
  • Targets Week 1 and 2
  • Total Targets

I like to use as a reference in context of Vegas and DAPs. Is the pecking order real? Is the production real and will be replicated this week? Find the unusual and extreme.

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Running Back Touches with % Rushing vs Receiving (ATTs vs Targets)

RBs are the most difficult position in fantasy football because they rush and catch. That fact then opens more possibilities within the game. Therefore, we must consider that extra dimension for RBs especially in PPR leagues. 

The easy RB to figure is one that does both catch and rush. The harder RBs are the pass-catcher only types as they are game script dependent. Thus if you have a pass catcher RB (ie Edmonds 71% catching), you must consider the way that game might go. See Vegas and DAPs as a foundation for your thinking. 

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Week 1 and 2 RB  Raw and Scaled Rushing Attempts

The LAC are the poster team for rushing RBs with 72 Rushing ATTs! vs HOU at 27 (Sad). Focus on the extremes and move to rushing teams in Non-PPR leagues. 

 

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Bar Graph of the Team’s Rushing ATTs

Left side are the rushing teams (green) vs Right side weak rushing teams (red).  

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RB Team Based Rushing Attempts Vs Passing Targets

These tables place the RBs into their team and RB crew. Allows a very specific view of handcuffs, usages, weekly improvements or declines. Again we are search for extremes in use and weekly productions.

For example ARI has Drake as the rusher vs Edmonds as the passing RBs 

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Player Level Total RB Rushing Attempts with Targets

 These metrics are sorted by Total RB Rushing Attempts High to Low. I also added targeting information as well as an X marks the spot of players that are skewed to rushing with low targets.

Note extremes for example JT in IND 9 vs 26 Rush ATTs week 1 vs week 2.

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Week 2 Targets and Touches

Thanks for reading and use wisely. Do not share with your league-mates 😉 

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