Positional PPR Points 2013-16 Part 1

Russell Wilson

By John Bush

2013-16 Positional PPR Points Part 1

Overall Team Yearly Performances

In fantasy football, players are on teams, thus team analysis is a necessary backdrop for our 2017 drafts. In figure 1, I present a team view of all 32 team’s PPR points scored from 2013 to 2016. The data is color-coded each year going from the top and bottom PPR scoring teams that year.

I have color annotated the team names with a light red color to point out teams that have been in the bottom of overall Team PPR production in each of the 4 years. Those teams are BUF, JAC, LAR, MIN, and SF. As a whole, these teams have been under-performing for years. Be aware of drafting lower level players from these teams.

The opposite of the under-performing teams was those teams that have been performing higher than most other teams across the 4 years. They are colorized in light green and are NE, NO, PIT, SD, and SEA. Players from those teams need a deeper look for sleepers.

Use this figure’s data conclusions to highlight the player teams both high and low. I would suggest using this initial data as a tiebreaker.

Note that the PF abbreviation stands for PPR Performance Scoring. It is my home made metric to look across positions using one scale 0 to 100!

Figure 1 2013-16 Team Positional PPR Points

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The Percentage of the Team’s Total PPR Production 2015 to 2016. 

Last year’s data is best look at the team PPR Scoring level going into 2017 drafts. Figure 2 presents the increased PPR scoring level from the 2015 season by team. The list was sorted from highest % PPR point increase to the lowest. Green colorization represents the most improved teams and red colorization points out the teams with the worst performance from 2015 to 2016.

The scaled data column (second one) gives a more accurate improvement score for each team. The 32 team average increase was a 72.1% increase. That number was subtracted from the first column improvement score and the resulting scores was color coded from blue (best) to red (worst). The top 7 teams (DAL, ATL, IND, BAL, GB, NO, and MIN) are listed and are those teams whose players should be given extra credit in setting up your 2017 cheat sheets.

Figure 2 Improvement Scores for Teams from 2015 to 2016

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2013-16 Team Positional PPR Points

A 4-year view of all NFL team’s fantasy points gives us a grand look into team production.  The teams that have higher production of Fantasy Points will have players that scores more points than average. Expect lower ranked player could have a better change of being sleeper draft picks.

In Figure 3, the data includes total Fantasy Points over the 4 years, the % of the league grand league total, and the scaled league percentage of all 32 Team’s Total Fantasy Points. These data were color coded by red for teams below average and green or blue to denote the teams that have scored the highest fantasy points. These top groups of teams should be noted in the 2017 drafts, as have higher chances to generate better draft picks on average. This finding is for sure a good tie or tier breaker between similarly grouped players

Figure 3. Four Year Team PPR Totals and Percentage of League Grand Total

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Positional Analysis from 2013 to 2016 by Team PPR Production

The next series of figures have data that allows investigation into each team’s positions by individual years and totals. The raw data is present firstly and color-coded, followed by the 2015 to 2016 increases and scaled change, and finally we look into the 4-year totals and scaled averages.

Questions to be asked by my readers as they root around in the data:

  • What is the team’s positional usage as measured by point production?
  • Are there teams, which always use their positions highly or not at all?
  • What was the increase in usage from 2105 to 2016?
  • What are the trends in yearly increases?
  • What is the overall 4-year usage of each position from each team?
  • What teams should be expected to have higher usage in 2017?

The Quarterback position from 2013 to 2016.

The yearly usage data from 2013 to 2016 is shown in Figure 5-4. The first conclusion is that 2016 was a banner year in QB scoring. That trend has continued for the last 4 years and previously as has been discussed.

The 4-year QB positive teams as determined by the data seen in Figure 4 are CAR, NO, NE, SD, and SEA while CLE, HOU, LAR, and MIN are in the bottom for all of the last 4 years.  It’s hard on your players if their teams have a weak QB.  Draft accordingly in 2017. I do expect David Carr to move the LAR out of the basement and would not be concerned to draft LAR players.

Figure 4. PPR Production from the QB

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Recent PPR production was assessed in the QB position from 2015 to 2016. The % of PPR production increase was calculated and the league average was at 62%. That number was subtracted for each team’s PPR point totals and a scaled 15 to 16 change was done. Figure 5-5 presents a color-coded table, sorted by highest to lowest team PPR PF generation.

The top seven teams with highest increase from 2015 to 2016 were DAL, ATL, IND, GB, BAL, NO, and LAR. The odd ducks in these seven were BAL and LAR. We would consider late round drafts for these team’s QBs.

The bottom group includes BUF, NYJ, CAR, HOU and SEA. I would consider drafting these team’s QBs late if at all. I may not have many Newton or Wilson shares in the 2017 season. The other teams can be considered for a do not draft list unless you are in 2QB league or one team has an extremely easy strength of schedule.

Figure 5. QB Performance Analysis from 2015 to 2016

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Moving outward into the data stream, we present the 4-year PPR PF QB team totals. The data is shown in Figure 5-6. The top teams over the 4 years are NO, CAR, GB, SEA, IND, and NE. Note the poor 2015 to 2016 numbers of CAR and SEA seen in Figure 6. Have Wilson and Newton peaked?

Note many of the bottom teams are the usual suspect teams.  However,  LAR and BAL are in the bottom group but showed last year improvement. Can more improvement be expected?

Figure 6. 4-year QB totals, % of League and Scaled Averages.

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The Running Back Position from 2013 to 2016.

The running back position is really 2 positions in one within a PPR landscape: a pass catching RB vs. a rushing RB. We will explore that concept in other posts down the road I suspect but we wanted to remind you that this PPR point production data is all encompassing.

As with the QB position, the first set of data presents the yearly PPR PF by team RB from 2013 to 2016. The color-coding is green vs. red colorization.  The yearly averages are below each column and show very little growth in the RB position in terms of yearly team based PPR PF scoring.

The most significant data is the yearly high vs. lows within all teams. Looking for the team’s that had RB in the top yearly levels we find ATL and only NO as the teams with consistent results. We would certainly bump up RB2s and look deep at the RB3s as well.

The almost made it teams were BUF, CIN, and PHI. These three teams have been consistent as well and a deeper look into their RBs is suggested.

The bottom based RB usage teams are CAR, JAC, and LAR along with IND, MIA, and SF almost standing out as the poorer RB based teams. We suggest less time spent on their RB3s. Consider the RB1 players only and maybe the RB2s as well.  Best not to fish too deep into these team’s RB depth charts.

Figure 7. PPR Production from the RB

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As with the QB position, looking into the last 2 years for Team RB improvement, those teams whose RB crews had higher 2016 PPR PF production are worth our focus.

In Figure 8, the 2015 to 2016 improvement of the RB PPR scoring was calculated and presented. The range of improvement was from +171% to -111%. That is a broad range and continues the narrative of higher risk with the RB vs. QB positions.

The top teams for RB improvement was SF, OAK, IND, ATL, TEN, NO, DAL, PIT, ARI, WAS and JAC. Note the cluster of teams from 107% to 58.8 %. SF really improved and given the extremely poor 2015 RB PPR points, that number is not as significant as it appears. Note that the 2015 SF RB PPR points score was one of worst scoring by a team RB crew over the 4 years.

Figure 5-8 RB Performance Analysis from 2015 to 2016

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Moving back out in the RB data, each team was assigned their 4-year PPR PF score. Next, each team’s % of the league total was determined and posted within Figure 9.

The top teams are at the top of the league in RB PPR PF over these four years are NO, ATL, NE, KC, BUF, PHI, and DET. Note that only NO and ATL are in the top’s in 2015 to 2016 improvement as well. Draft these team’s RBs would seem as a good move in the 2017 drafts.

On the other hand, the teams bringing up the rear are JAC, SF, CAR, IND, and TB. Only SF and IND saw an improvement from last year (Figure 8). Given the track record of these teams, be cautious in your 2017 drafts.

Figure 9. 4-year RB totals, % of League and Scaled Averages.

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 The Tight End Position from 2013 to 2016.

This position is worth less than the other three positions in terms of yearly PPR TE PF production. However, given that most FF leagues require a TE to be used, we must explore the data. Figure 10 presents the yearly TE PPR PF scoring by each team from 2013 to 2016. Note the yearly scores are unchanged over the 4 years.

The teams at the top of the TE positions for all 4 years were only PHI, SD and WAS. These teams are our focus for 2017 drafts as the team history suggests heavy usage. The bottom teams were ARI, BUF, NYJ, OAK with GB and JAC being honorable mentions. A draft plan to avoid or downgrade the TEs from the teams seems reasonable.

Figure 10. PPR Production from the TE 2013 to 2016

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As part of your strategy for drafts you must focus on last years TE PPR improvement scoring. Figure 11 has that last year scoring data by team. Remember you should look across all the 3 figures for each position to add clarity to the past.

Analysis of the data from the figure showed the top improving teams from 2105 to 2016 were HOU and IND by a wide margin.  I think these TEs from those 2 teams can be a bargain this year.

The teams of MIN, SEA, TB, PIT, KC, ATL, and BAL had positive improvements as well and their TEs should be on your watch list especially in TE heavy leagues. The bottom teams in TE PPR PF were TEN hands down, followed by NE and CLE. We would be cautious about TEs from these teams.  The next group at the bottom were SF, CHI, NO, CIN, GB, and NYG. Be thoughtful about your TEs from these teams as well.

Figure 11. TE Performance Analysis from 2015 to 2016

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Finally, come up for air and looking at the combined 4-year level, the teams 4 year TE PPR PF were summed, and the % of the league total was calculated and scaled. Figure 12 has the tabular data for these numbers that were sorted top to bottom and color-coded.

The top teams in 4-year TE PPR PF totals were NE, NO, SD, PHI, WAS, IND, TEN and CAR. These teams have a long track record of TE success but as discussed before, NO, TEN and NE had a down year from TE injuries or loss of key players. I think caution is in order for these 3 teams’ TEs.

Figure 12. 4-year RB totals, % of League and Scaled Averages.

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Analysis of the WR Position 2013 to 2016

The total FP scored each year from WR is higher than the other positions. Thus the importance of drafting “KEY” WR remains a critical aspect of drafting. Figure 13 presents the 2013 to 2016 yearly Team WR PPR based PF scoring. These data has been color-coded to see the patterns.

Most of your league may not use a broad-view of the positions. I wrote this post to highlight a team and positional view over a time period of 4 years. Use this data to add to your quiver decision data arrows.  This is not the end but it’s the end of the beginning for your 2017 draft research.

The top WR based teams across all 4 years were DEN, GB, NYG, and PIT. All you would suspect the WR1s from these 4 teams are highly valued.  Note the ATL, ARI, and NO would be in the next tier down. We suggest your deeper searches for WR2s, 3s etc. should be confined to these teams

The opposite teams, the one you may wish to downgrade are BUF, CAR, KC, and LAR. They were consistently on the bottom. Teams that were running close to these bottom feeders are CLE, SF and TEN. We would downplay some of the building hype for WR from these teams. Caution is suggested. I expect LAR to make a major move upwards. I hope to discuss that next year in the updated metrics post for 2018.

Figure 13. Team WR PPR Based Scoring from 2013 to 2016.

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Drilling down into the data, as well as I have in the other positions, it is essential to see the latest level of team WR improvement from 2015 to 2016. That data is shown in Figure 14.  The range was from +229 to -310.7 that is a highest range of scoring even higher than the RB position. As stated elsewhere, the WR position in 2016 was unlike the preceding years and you need to be aware of that issue. This data range suggests extreme levels of variation within the position occured in 2016.

The top improving teams at the WR position was GB and MIN that were nicely above the other teams. The question is can those teams repeat and maintain an improvement. If so, the WRs from each could be nice acquisitions in your 2017 drafts. The next tier of improved teams were CHI, DAL, TB, NO, TEN, BAL and NE. Watch free agency and rookies drafts to look for reasons to support those teams continued improvements.

The top bottom feeder in the NFL league was by an overwhelming marginal poor year was HOU. Unless the QB situation changes, I am lowering our 2017 expectation of the HOU WRs. Also in strong second place of poorness was the NYJ team. The loss of Brandon Marshall cannot help at all.  Next up are the PIT and JAC teams who had certainly under-performing years. The next 6 teams are not so bad but were in a down year in 2016 as well.

Figure 14. Improvement PPR Based Scoring of Teams WR 2015 to 2016

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Figure 15 presents the 4-year total team analysis data. The top teams over the 4 years were DEN, GB, PIT, ARI, ATL, NYG, NO and MIA. Only GB, NO and ATL teams had a nice 2015 to 2016 improvement. Upgrading the WRs on those teams is warranted.

The bottom 4-year teams at the WR position are KC, SF, BUF, TEN, LAR, CLE, MIN, and CAR. Note nice improvement for TEN and MIN from last year. Maybe those WRs are moving forward for the 2017 season. Be watchful.

Figure 15 Four Year PPR Based Scoring for all Team WRs

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Conclusions

Given the level of results, apply those that seem to make sense for you. Never be a slave to metrics. 

Use the top down views of the team’s positions to break ties in your cheat sheets.

Develop deeper target shopping list using these data’s results to find those dark horses among the herd of donkeys! 

Next Up Article is Part 2 of this Series (Saturday 8/12)

Part 2 is based on fantasy points scoring totals using PPR scoring of each team’s QB, RB, TE, and WR within the context of the entire team over the last 4 years. I think breaking into divisions and conferences gets you a little deeper to think about the journey each team will take within the 2017 season. These metrics are color-coded (green good to red poor) and 4 year totals to the right.

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