The first layer of Dynasty Strategy:
Identify Historical Trends.
Follow Historical Trends.
As the NFL data pool continues to grow with tools like GPS tracking or play-mapping, fantasy analytics has stayed fairly rudimentary. Much of the dynasty analytics community is focused on rookie prospect evaluation. Once the player has been around long enough, college stats become less relevant and the market begins to focus on Average Draft Position (ADP). While ADP is efficient, the quest to outperform ADP must begin with an objective piece outside of the market framework. FFA's Dynasty Trajectory Scores offer a data-driven backbone for Dynasty Asset evaluation. Use them to provide valuable context for your startup draft board or insert them into your dynasty projection models.
Calculating the trajectory of a rocket requires recognition of several layers of variables. We can separate these into two main categories: Internal and External factors. Atmospheric factors are a critical part of the equation. Weather conditions, barometric pressure, and launch site latitude will all affect the trajectory. These situational External factors are akin to the NFL individual's teammates and coaching staff. We've seen Cooper Kupp double his productivity when the Rams added Matt Stafford and lost Robert Woods. We've seen Bills WR Stefon Diggs takeoff after being paired with Josh Allen. We've seen Bengals RB Joe Mixon struggle and succeed with the firing and re-hiring of offensive line coach Frank Pollack. These atmospheric elements play a big part in a player's productivity.
The most important components of trajectory are the characteristics of the rocket itself. How much thrust can its engines deliver? How much payload will it be carrying? These Internal Factors are not always constant: A rocket's mass changes as it burns fuel, shifting the calculated trajectory. This occurs with NFL athletes as well. As they burn more fuel, two inherent characteristics see a decrease in value:
1. Productive Value decreases
2. Market Value decreases
This change in value is intrinsic to the individual athlete. It occurs with every player across a reasonably consistent range. Quantifying this range is the initial focus of these Phase 1 Trajectory Scores. We will root out the additional noise of External Factors, such as NFL roster construction and playcaller history, in favor of isolating the Productive Value of the individual. External factors can be integrated during future phases.
As with Tsiolkovsky's ideal rocket equation, the projected performance range of a particular NFL player can be derived from simple Newtonian physics. Instead of:
Force = Mass x Acceleration
Future points = Previous points x Age
The premise is incredibly simple. The core of a player's intrinsic fantasy value is his on-field productivity. That data is enough to calculate the future trajectory of his career. By taking the average fantasy points per game of his last two seasons, we get a good idea of a player's recent value. A single season is not a large enough sample size and including a third season goes too far into a player's history - too much has changed since then. Two seasons gives us enough information to reduce the External factor noise. We get to see who he is in different seasons, with a different mix of teammates and an offseason to get healthy, master a playbook, or get a better playcaller.
The PPG from the past two seasons will allow us to put him in a cohort with other historical NFL players. From there, we can compare how players of a similar caliber performed in their subsequent seasons. We can see into the future based on the average productivity of similar athletes.
AJ Brown has averaged 15.6 PPR Points Per Game over the past two seasons. That puts him into a stellar cohort with 18 other WRs who also put up ~16.0 PPG during their age 23 and 24 seasons. This Trajectory Cohort features:
Identifying similar names and seeing multiple Hall of Famers is extremely exciting! These receivers played with a wide range of QBs, in a variety of offenses, amongst diverse levels of target competition. We have deep threats, route technicians, and jump ball winners. And we can mute all this extra noise by studying the collective. We identified this cohort by taking a snapshot in time. We can also roll the tape forward to get a picture of AJ Brown's future. How did these 17 WRs (DK Metcalf excluded) perform at ages 25, 26, and 27? Did they capitalize on their momentum? Did they continue to command big time volume?
Yes! In fact, these 17 WRs averaged enough PPG to continue to perform as WR1s (>16.0 PPG) in their 25 and 26 year old seasons. The table below shows the average 3 Year Trajectory (Ages 25+26+27) of every player based on their previous 2 Yr PPG (Ages 23+24). The raw numbers look very good for AJ Brown, whose Trajectory cohort is highlighted in yellow.
I've calculated this Trajectory data for every player at every position since 1994. Now you can compare any NFL player to the historic trends of similar players. Not only does the Trajectory Score verify the age-production curves of my past research (Age Adjustment and Dynasty Endgame), it takes it a step further and allows for specific comparison to athletes of a similar talent level. Fake football. Real science.
Carl Pickens is ecstatic.
Derrick Henry is on the other side of his path. Age 27/28 is the typical peak of the RB trajectory. Timing that peak can be intimidating. Dynasty owners typically fall under two extremes. They sell every RB before age 28, or they hold their guy until the wheels fall off. The Trajectory data allows us to be more specific as we draw a differentiating line. Instead of selling every single RB or holding every single RB, pay attention to their Trajectory Cohort. With a 2 Yr PPG of 24.0, Henry falls into the ELITE group of RBs. The 20+ PPG cohort is our highest tier and also holds 17 players in it:
As you can see, this 20.0 Trajectory Cohort (yellow) continues to average RB1 caliber performances (16.0+ PPG) for the next two years. While the group just below (18.0 Cohort) will typically fall out of the RB1 range immediately. Large sample analysis will miss this distinction and have you fading everyone as soon as they hit age 28. Lumping the 18.0 Cohort in with the 20.0 Cohort paints a disheartening doomsday picture. Dividing those groups with this lens allows us to understand the circumstances in which hanging onto a RB should actually be worth it.
You can sell Derrick Henry, but getting two seasons of RB1 performances and an additional RB2 season (20.0 Cohort) is radically different than getting one RB2 season at 15.1 PPG, and then vanishing (18.0 Cohort). Recognizing and comparing players to their appropriate historical peers will change the way that we identify dynasty values.
Derrick Henry knows which Trajectory Cohort he's in.
Expect this to become a staple in my language when discussing dynasty player values. It is the base layer of asset evaluation. They are not rankings. They are not rules. They are data aggregates that provide context. Some players will stand out like Aaron Rodgers or Saquon Barkley. Let the scores influence you to the extent that you are willing to trust the historical average. In Rodgers' case, the historical average is the product of a very small sample size that includes only Steve Young and Peyton Manning. In the case of Patrick Mahomes, Josh Allen, etc., no other quarterbacks have ever produced the caliber of fantasy output at their age. Not Brady, not Manning, not Brees. This current group of elite QBs is redefining fantasy expectations. They are pioneers. Kyler Murray's trajectory data is built off Mahomes' performances. It's a wonderful time to be alive.
The Trajectory Score itself should be evaluated in relationship to PPR PPG, as it is a weighted average of the 3 subsequent seasons of PPG for all the players in a given cohort. You are welcome to view, analyze, and download all of the Trajectory Data for the top 445 players.
Houston, we have liftoff.