Football’s Analytics Wave is Reaching its Peak
A dive into what’s to come and the impact on future contract negotiations
If you have been watching football recently, you likely noticed the futuristically-styled AWS commercials that highlight a wide range of new player tracking metrics such as Catch Probability, Injury Risk, and Touchdown Probability. As fans, we have never had more seamless access to advanced analytics. Sites like NextGen Stats (NGS), Pro Football Focus (PFF), and Football Outsiders (FO) make it easy for stat gurus to gain deeper insight into what is happening on the field. This development is synonymous to the introduction of advanced batted ball and pitching data to baseball fans through baseballsavant in 2015. This was unique, as it was the first opportunity for everyday fans to browse and digest advanced statistics that were already being heavily utilized by major league teams through their own internal models.
Prior to this point, agents were in the dark when it came to advanced metrics, as representation agencies typically did not staff the same number of analysts that teams did. Therefore, agencies who did not have their own data scientists had to rely on consultants or data vendors for analytics information. As a result, contract negotiations turned murky. In former years, negotiations traditionally revolved around what the player “had” accomplished. Now, there was a new element revolving around what the underlying advanced metrics said the player “should” have accomplished or “will” accomplish. Thus, past performance was pitted against the prospect of future success.
With the NFL launching Next Gen Stats last season, we’re in the process of witnessing a similar progression of what we saw unfold in baseball. Football fans now have access to player tracking statistics through NGS, overall grades through PFF, and defense-adjusted yards above replacement through FO. These metrics offer cutting edge insight into how a player is performing. Similarly to how batted ball data can be used in baseball to diagnose a players struggles or success, SEP and DVOA can be used to gain a more wholistic perspective on a players value.
The resulting dilemma is near identical to the dilemma baseball players and agents faced during the expansion of the analytics era. The scope of each negotiation is subject to change, as some teams are already putting a substantial emphasis on collecting advanced tracking metrics. As teams continue to build out their analytics departments, the information gap between teams and agencies will continue to grow. As a result, it is unlikely that any agency will have the tools necessary to gain a substantial bargaining edge on a team throughout a negotiation.
While a bargaining edge is unlikely, it is crucial for agents to be aware of the vast range of statistics that are now shaping the way teams view their players. Outside of watching film, these stats have traditionally flown under the radar. Teams will ultimately utilize these data feeds to create their own internal models, however, agents have access to surface level metrics through 3rd party vendors like PFF, FO, and NGS. Through these platforms, advanced metrics can be used to dabble with different performance models to help gain a better perspective on a players performance.
Taking statistics individually and balancing them against the metrics of other vendors can help paint a better overall picture of how a player is performing. For example, Drake London has a PFF RECV grade in the 97th percentile. However, his overall grade when considering various other metrics is a 68, which ranks among the 80th percentile. This is mostly due to the fact that he ranks near the 29th percentile in SEP and 27th percentile in CATCH%. Let’s break this down by evaluating 10 receiving statistics, which are listed below with the source. I have also included the glossary to each vendor where I pulled the numbers from; PFF, FO, & NGS.
- DVOA: FO
- DYAR: FO
- RECV: PFF
- DROP: PFF
- aDOT: PFF
- CATCH%: PFF
- CTC%: PFF
- FUM: PFF
- YAC over xYAC: NGS
- SEP: NGS
Not every stat is created equal, as some stats carry significantly more weight than others. For example, FUM is likely not as important as a receiver's overall SEP. Additionally, aDOT is likely not as important as RECV. However, each stats holds a particular value and thus, each metric needs to be assigned a weight.
To start, we can group stats together when they revolve around a similar aspect of the game. DVOA, RECV, DYAR and SEP all revolve around a receivers general ability to run a pure route, get open, and make the catch. Specifically, DVOA takes into account situational factors that help balance out game script dependent plays. Overall, these statistics are good anchors to input to rest of the independent statistics. CATCH%, CTC%, aDOT, and DROP all relate to a receivers ability to make the catch. Lastly, FUM and YAC+/- relate to a receivers ability to create value once the ball is in their hands.
- In the 1st category, lets give DVOA at 20% weight, RECV a 40% weight, DYAR a 25% weight, and SEP a 15% weight. Leaders listed below:
- Tyreek Hill: 91
- Stefon Diggs: 91
- Justin Jefferson: 90
- Jaylen Waddle: 88
- Cooper Kupp: 87
2. In the 2nd category, lets give CATCH% a 20% weight, CTC% a 25% weight, aDOT a 20% weight, and DROP a 35% weight. Leaders listed below:
- Devin Duvernay: 85
- Noah Brown: 81
- Cooper Kupp: 80
- Tyler Lockett: 79
- Tyler Boyd: 79
3. In the 3rd category, lets give YAC +/- a 85% weight and FUM a 15% weight. Leaders listed below:
- Tyler Boyd: 89
- DeVante Parker: 88
- Deebo Samuel: 86
- Rashod Bateman: 86
- Jerry Juedy: 86
Obviously, different players excel in different facets of the game. There is no surprise the Deebo Samuel ranks highly among receivers in terms of yards after the catch. Additionally, it is not surprising to see Justin Jefferson, Stefon Diggs, and Cooper Kupp at the top of the general receiving categories that put emphasis on route running and ability to get open. Next, we can assign values to these categories and combine them to create an overall grade.
- 1st category: 50% weight.
- 2nd category: 30% weight.
- 3rd category: 20% weight.
Following the calculation for the overall grades, the top 5 ranked as follows so far this season:
- Stefon Diggs: 84
- Cooper Kupp: 82
- Tyreek Hill: 82
- Tyler Boyd: 81
- Devin Duvernay: 79
Next, I wanted to compare these grades to current contracts using both 2021 and 2022 stats. For the first chart, I am comparing the players 2021 performance to their 2022 salary. The players overall grade is represented on the Y axis while the players salary is represented on the X axis. There were a handful of players who signed contracts this off-season; Diontae Johnson, Cedrick Wilson, Davante Adams, Christian Kirk, Zay Jones, Hunter Renfrow, Tyreek Hill, A.J. Brown, and Deebo Samuel.
I also plotted the receivers performance so far this year in comparison to their salary. Being above the trendline suggests a player who has provided a value on their contract compared to average. On the contrary, being below the trend line suggests that the player has underperformed in comparison to their contract. Some players worth highlighting who are far outperforming their value (rookie deals): Tee Higgins, DeVonta Smith, Jaylen Waddle, and Justin Jefferson. Some disappointing underperformers are Diontae Johnson, D.J. Moore, and Courtland Sutton. As we can see, Stefon Diggs, Cooper Kupp, and Tyreek Hill are worth every penny.
In summary, all these grades do is take into account data from a few of the top public sources of football metrics. I want to note that none of this information is determinative. Most teams have far more advanced models that take into account NGS information that the public does not have access to. However, while not determinative, this information is very much useful. In regard to the sources of the data, PFF and FO have proven to be trustworthy sources of information; so much so that PFF grades appear on player intro cards at the beginning of games. As the landscape for football contract negotiation starts to shift, metrics like these will be key bargaining chips used by both parties.
