Novel Red Zone Metrics Part 1

By John Bush

Novel Red Zone Metrics Part 1 with an Introduction to this Series

How do we as fantasy player use data to make decisions?


  • Step 1: Strategy
  • Step 2: Identify key areas
  • Step 3: Data targeting
  • Step 4: Collecting and analyzing data
  • Step 5: Turning Insights into Action

Author Elana Roth would give us the 5 steps in a generic view of using data to act! My strategy is using the methodology of biological science as a guide to fantasy football analysis. The key data areas are critical to understand but have we all the areas to deal with FF problems? The whole point of my FF research is to broaden my research journey to undercover new insights. I am trying to find the unknown unknowns. The only way to approach that path is doing a focus on as much data targeting and analysis as possible. The more you experiment the more your chances are to find an unknown unknown! These insights are the grist for rankings and draft picking. My textbook includes an area that is unknown as well as my take on more common aspects of FF insights. FYI 2nd Edition in Mid-July. This series is another attempt to clarify an aspect of the data area for FF insights.



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Kindle Edition


Many pundits have discussed the TDs as not being reliable to project player future success or that TDs are somehow not as much a reflection of the skill of a player. The flipside of that idea then implies that a player’s yardage is a better reflection of player’s future. I have not gotten deep into either of these concepts yet. However, I wanted to investigate Red Zone Points from TDs only and eliminate yardage from player activities of passing, rushing and receiving.

QBs will be analyzed by a judgment of TDs/Fantasy Points per Passing Attempts and RZ TDs per rushing attempts. RBs would be judged then by RZ TDs by both their Rushing Attempts and their Targets. Finally, TE and WR would be judged by RZ TDS per RZ Targets. I now provide a lens into this very specific metric as well as the team, position, and player level. Part 1 digs into these metrics at the Team Level.



Novel Red Zone Metrics Part 1

Novel Red Zone Metrics Part 1 material begins my multi-part journey into a deep dig of red zone stats across teams, positions, players, and activities such as red zone passing TDs, rushing TDs, and receiving TDs.

Total Team Touchdowns per Passing Targets

These next 3 figures present

  • Summary of Team Total Passing Targets
  • Team Targets, Completions, % Completions Per Targets, Target-Based Touchdowns and TDs Based Fantasy Points Per Team Targets
  • Multi-Plot of % Target Based Completions vs TDs Based Fantasy Points Per Team Targets.

Figure 1. Summary of Team Total Passing Targets

I begin looking at each Team’s Total Passing Targets (TTT). I suggest seeing the 2017 landscape and consider what was vs what can be. The NE and NYG are the top two. Given the 2018 season, I expect NYG TTT to increase assuming Barkley can catch out of the backfield! I would be conservative and add 10% or so into NYG TTTs! NE should be steady. TB and PIT are the next tiers in TTT! The risk for TB given the suspension for Winston. PIT steady! Note we see SF and DET are up next tiers. SF goes up if Jimmy G is who he seems to be. DET is steady to up slightly! On the bottom end, CHI has to improve and IND if they have Luck. Concerns continue with BUF, TEN, and CIN passing! The NYJ will surprise and move up the food chain!


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Figure 2. Team Targets, Completions, % Completions Per Targets, Target-Based Touchdowns and TDs Based Fantasy Points Per Team Targets

This figure presents the full slate leading to the new metric I have calculated (TTT TD based FP/Targets). I have sorted the 2017 Teams by this Metric. I have divided the 2017 season into Team performances as viewed by the TD based FP by Targets. Tier 1 was PHI and SEA. They were well above the league. They can be considered efficient. The 2nd Tier is large and is made up of 10 Teams. CIN, HOU, and KC have a risk. I expect GB with Rodgers to move up! The bottom group included IND CHI DEN NYG and CLE they should move upwards. BUF, ARI, TB, and NYJ are expecting to drop down.


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Figures 3. Multi-Plot of % Target Based Completion vs TDs Based Fantasy Points Per Team Targets.

This visually based plot is nice for us to see scoring efficiency vs passing success. The teams high in the TDs/Targets of PHI/SEA were nicely efficient to score. Do note that SEA lost JG the TE and he was a great scorer for SEA. Nice for GB in 2018 then, When I discussed the bottom teams of IND, SF, CLE, CHI, and TEN etc. were fairly good in just overall completions vs poorly in scoring. They could not bring it home in the red zone!


Novel Red Zone Metrics Part 1 slide3.jpg

NON-RED ZONE (NRZ) Team Analysis

These next two figures focus on Team level NRZ activity. I produced this to set up comparisons to RZ metrics.

Figure 4 Team Level NRZ Metrics (Targets, Completions, % Completion, TDS, and NRZ TDs/NRZ Targets

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Figure 5. Visual Plot of NRZ Metrics.

This view is the same view as Figure 3 except focused on NRZ activities. Note that most teams (Blue trend line and Green raw data line) % completions are NRZ TDs/Targets is quite dramatic! Note that OAK was the top team followed by KC, SEA, WAS, DET, and NYJ! These teams were great at scoring outside the NRZ via passing. In 2018, CARR (OAK), SMITH (KC to WAS), SEA (Wilson), DET (MATT S), NYJ (McCowan? Surprise in here)! Note at the bottom we see NYG (OBJ back) and expect more NRZ scoring. Note NE was down here, and they really need a longer play guy! I just highlighted. Digg deep into here. Nice nuggets!


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RED ZONE (RZ) Team Analysis

Figure 7 RZ TD/Targets Metric vs RZ TARGETS, Completions, % Completions and RZ TDs


This table allows a glance at the RZ metrics I am tracking. The RZ TDs/Target metric was the one I use to sort the table for high to low Team production. PHI was the top and as the super bowl winner that seems interesting. MIN, CIN, JAX, MIA, and NYG were next at the top. Some surprising teams in here.



NYG is a major one and think where they should be with OBJ back. NYG is a sneaky pick at QB and maybe at 3/4 WRs below the top 2. Watch training camp news in 3 WR sets for a sleeper out of here. CIN is also a surprise team, their issue was getting to the RZ as they had 57 Targets only vs like NE or PIT at nearly double that number. MIA and JAX were also surprises and getting Tannehill back can surprise in 2018. Bortles also can keep it going. Concerns at the bottom for SF and IND. QBs will be tested! KC will get a new QB and risk exists. Continue to dig for fun facts in all this data-pool.


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Total Team vs RZ and NRZ % Completions

Figure 8. Tabular Version of 2017 Total Team vs RZ and NRZ % Completions

I sorted based on raw % RZ completions by Team. Maybe unsurprisingly NO led the way in 2017. There has been pundit discussion about NO rushing more. I agree that was the case. However, this data support NO as the best at passing in the RZ for a score. Brees continues his top-tier work. MIA, MIN, PHI, NE, GB, and NYG were all tops in RZ completions!


Note again we find metrics that support an NYG improvement, yet Eli is going late in Best Ball drafts. PHI was again high up in this metric and if Wentz can stay healthy, he can move PHI up the ranks. GB was doing well without Rodgers, so I expect GB improvement in 2018 based on this metric. The bottom feeders in 2017 were IND, CLE, OAK, HOU, KC, DET and TB. Given the 2018 changes for IND and these issues in TB and CLE, these teams are of higher risk, especially at the QB! Note Smith at KC to WAS, he was poor in finishing the drives via completions in the RZ. He is not a slam dunk QB late.

Many late QB have warts somewhere in their game for 2018. Be thoughtful!


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Figure 9. 2017 Team Level % Completions in RZ, NRZ and Total.

Visualizing the balance of a team’s activity is a key aspect for further thoughts into the 2018 season. This plot allows a visual view of NRZ vs RZ sorted from high RZ to low. The yellow line follows the % completions in the NRZ areas while red line hits the RZ % completions. Note the balance of each team.


Some Teams are good in the NRZ but are sad while in the RZ (lack of finishing a drive). IND, CLE, OAK, HOU, KC, DET and TB were those teams in 2017. In HOU, IND, and OAK it is expected these teams QB position will be on the uptick. KC, CLE, and TB have a higher risk in 2018. DET is the surprise, I suspect but cannot prove the lack of a rushing attack allow defenses to focus on passing in the RZ. I predict if KJ can add rushing success to DET then the Tate/Golladay/Jones trifecta of player will be elevated in 2018.


I have focused in Best Ball to collect all DET WRs pieces as possible. Looking at the top group, NO, MIA! MIN, PHI, NE, GB, NYG and more all did very well in 2017. Expect NO, NE, GB, NYG, and PHI to stay or improve. Risk hangs over MIN and MIA and a decline would not surprise.


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Figure 10. Scaled to Average % Completions 2017 Team RZ, NRZ, and Team Totals

Scaled data is a method to enhance understanding of the bigger picture. Numbers that are now defining if a Team is above or below the league average. That allows a deeper understanding of the data! Positive numbers are above while negative numbers are below average. Now looking top to bottom we see that NO was in its own Tier and was 20% above league average! That is very strong and suggests Brees has not slowed down in passing skills.

MIA was next and was surprising with the 2017 injuries and replacements. Landry was a critical piece in here and is going to impact MIA in 2018. I expect a drop. The next tier was MIN and PHI. MIN get a new QB and PHI get Wentz back. That change implies PHI is steady, but MIN is risky. NE. NYG and GB are the 3rd Tiers and we expect GB and NYG in 2018 to go up with NE trying to be steady. How bad were IND and CLE? They were triple bad vs the 3rd team from the bottom OAK.


Assuming LUCK is back that leave CLE as a big question. In Best Ball draft players are really stretching their hopes into higher draft picks for the CLE crew. High Risk. Oak and HOU are next going up and we should expect improvement in HOU, but OAK may be steady as the new coach may favor the run over the pass more than in 2017. The risk is in taking OAK WRs. Caution.


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Figure 11. Graphical Scattergram Plot of Scaled to Average % Completions 2017 Team RZ, NRZ, and Team Totals

The scattergram continues the top down vision across the 2017 Team landscape. The RED points are RZ vs YELLOW NRZ points for each Team. I have covered much in the preceding. I look for big differences in a Team vs the NRZ/RZ closer points. Let’s find the closest point TEAMs in Figure 11. This Team characteristic suggests the same level of efficiency across the entire field. It does not speak to the amount of efficiency success. See NO vs CLE both close in RZ/NRZ but very different in success with NO Tops vs CLE at the bottom.

  • NO
  • MIN
  • NE
  • SEA
  • DAL
  • WAS
  • TEN
  • BAL
  • BUF
  • SF
  • PIT
  • CHI
  • LAC
  • TB
  • OAK
  • CLE

Big Differential Teams are those that are good in one aspect and bad in the other aspect. They lack all the pieces to be consistent. Not many below are getting new QBs or QB back from injury!

  • MIA
  • PHI (Wentz rebalances the team in 2018?)
  • GB (Rodgers Back to Rebalance the Team in 2018?)
  • DEN (New QB)
  • LA
  • JAX
  • CAR
  • PIT
  • ATL
  • NYJ (New QB?)
  • ARI (New QB)
  • DET
  • KC (New QB)
  • HOU (Watson Back)
  • IND (Luck Back?)


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Figure 12. TDs FP per Targets for RZ, NRZ and Team Total (TT)

I convert TDs to 4 FPs, calculated the Total FP and divided that by the receiving targets. That team level data is presented in Figure 12’s table. We just left the Team efficiency and effectiveness in the RZ or not. This metric gives the final metric of a team’s offensive drives. The teams are sorted by the RZ targets.


The RZ vs NRZ differences are presented as its the differences in a Team’s scoring activities. PHI was the top in RZ TD FP per RZ Targets. Very efficient team and they were 13% more efficient than the second based team of MIN. PHI was also the tops in TT TD FP per Targets across the 32 NFL teams. The Top RZ Teams were:

  • PHI
  • MIN
  • CIN
  • JAX
  • MIA
  • NYG

These were teams that finished drives and had the passing players to accomplish that goal! Add to the rankings of these team’s players The bottom RZ Teams were:

  • IND
  • SF
  • TEN
  • CLE
  • CHI
  • BAL
  • KC

These team left points on the field. Concerns exist then. TEN, CHI and BAL stick out here as they will not be changing QBs early in the season. IND, SF, CLE, and KC will all be changes or getting QBs back. Expect improvements.


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Figure 13. Graphical Composite Plot of 2017 Team TDs FP per Targets for RZ, NRZ and Team Total (TT)


This visual plot below allows deeper views into the 2017 season. I like to weigh the entire landscape to detect outliers. The red line represents the RZ TD based FP per RZ targets while the yellow is the NRZ metrics and finally the blue is the total team metric. What teams seem to be the outliers? It seems interesting that the 2 to 6 teams the highest in RZ TDFP/RZ Targets had some of the lowest total team and thus NRZ metric amount. Those teams were MIN, CIN, JAX, MIA, and NYG. Also, BUF seemed to be that type of team with an RZ to NRZ high difference.

I suggest that that difference is not usual and if are outliers. These teams had Nice RZ performance than their overall metrics might suggest. This may be the source of TDs being tagged as not so predictable?



There are multiple ways to think about these teams. For example, in NYG, Eli was able to use his TE and RBs very efficiently to balance missing OBJ etc. CIN had AJ Green and others to use to make them RZ efficient. JAX had a great defense etc. that may have opened up the passing in the RZ. MIA had a high WR triple threat of Parker, Stills, and Landry to balance the loss of Tannehill. Note the different questions specific to each team. Fantasy Football is a complex system and thus is not easily tested or understood. Deeper research is required once interesting teams are highlighted. Teams with nice NRZ vs RZ (Blue line above the Red Line) were SEA, LA, OAK, WAS, PIT, DET, LAC, and KC. These teams were unlike the others and they were biased in the opposite way.

Why were they missing out of their RZ opportunities? Questions specific to each team thus are brought forward. I leave this for you to consider. I will explore in 2019 for my textbook.


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Figure 14. 2017 Team Ratio of RZ vs TT TDFP/Receiving Targets


I decided to dig into some of the questions from Figure 13. I wish to use a ratio metric to see the amount of that plotted difference in Figure 13. This ratio will point to the interesting teams for further discussion. I also calculated the scaled ratio to league average metric to see the true Team data from 2017! The best teams with the highest RZ to TT metric was NYG hands down. How was Eli able to use his remaining weapons to be RZ efficient! That seems to be a hidden story from 2017! Thus, assuming OBJ etc. is back it will be reasonable to assume Eli/NYG will improve in 2018. ELI was 68% better than Bortles (#2 2017 Scaled RZ/TT Metric).


Bortles was also very efficient in 2017 as well. He as nearly 80% better than MIN Keenum! Bortles was able to use LF as a rushing tool to move into the RZ and there he was the second best QB in this metric! Again, we have crickets in pundit land! KC was the worst Team at -1.7. Smith was the worst QB in the league in this metric! Be more cautious with Smith than many are right now! DET and LAC were also poor in a scaled RZ to TT ratio. Stafford and Rivers leave points on the board. They are in 2018 lower ADP than others. Is this metric part of the reason? I suggest you shift QB rankings with this data in mind!


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Figure 15. Bar Graph of 2017 Team Ratio of RZ vs TT TDFP/Receiving Targets

Visual analysis of the ratio data from Figure 14. Use to confirm the highlighted Teams from 2017. Note NYG and how this team in 2017 stood out vs all others. They were not even close. Reminds me of a racehorse wanting to race but could not quite get out of the gate! Could NYG be the most surprising team in 2018? Note JAX was second in ratio to RZ and TT! This suggests Bortles is better than his ADP now especially in Best Ball leagues. I also “see” the issues from KC, DET, LAC, PIT etc. Why are these teams having an issue? Be cautious in 2018.


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Part 2 continues this series.

I will dig into positions within teams and uncover any issues.

Please come back to continue this metric-based journey!


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