I’m procrastinating finishing the Eagles draft history analysis because the last two positions to do are tight end and running back and both are pretty uninteresting positions to look at (mostly because the relatively small number of players drafted at each position makes conclusions on the data useless). But that will be finished soon and I have a request to look at the Eagles tendencies which I will include.
One thing I have been working on and wanted to get out was a listing of the best positional metrics that determine player value and are intuitive to track. This last part is an important distinction as there are some really great metrics used, but they aren’t intuitive to see when watching a game. Most of the metrics below are metrics that are out there created by others, but a couple are ones I enhanced or added to (and when I did, I explained why and gave detail on the calculations). My plan is next to take a look at what 2021 could / should look like for the Eagles based on these metrics and what typical improvements and drops we could see. As a summary, following are the offensive metrics that are most important to watch:
| Quarterback | Wide Receiver / Tight End | Running Back | Offensive Line |
| Expected Points Added + Completion Percentage Over Expectation + Rushing Yards / 600 Dropbacks | Avg Depth of Target * Separation + Catch Percentage + Yards After Catch | Rush Yards After Contact + Receiving Yards After Completion | Run Block Win Rate + Pass Block Win Rate |
Some initial credits to several people that have done great work here:
- PFF’s Austin Gayle (@PFF_AustinGayle) was on Fran Duffy’s “Journey to the Draft” podcast which was a great run through the metrics that PFF sees as most stable in projecting players
- Michigan Football Analytics (@mfbanalytics) – which is becoming one of my favorite sites – does great work on metrics and advances the entire community
My approach and what matters
If you read anything else here, you will see me use PFR’s Approximate Value (AV) in a lot of analyses. The reason I like AV is that the basis of the metric – points per drive – makes sense in determining value. What determines wins in the NFL? Obviously, more points than the other team. The best way to measure value is looking at an offense’s ability to score higher than expected and a defense’s ability to allow scoring at a lower rate than expected. This is what AV does. DVOA is another great metric that uses success on a play vs. expected as its basis and there is good correlation between DVOA and AV.
It is easy to get lost down holes looking for correlations among data that “work” on a chart and mathematically but either don’t make sense or are further distanced from what ultimately determines success. A classic example is Leonard Koppett’s “Super Bowl Indicator” which predicted stock market success – it back-tested but then failed moving forward because it fundamentally makes no sense. For the positional metrics, the closer they are to generating points for the offense or preventing points for the defense, the more useful (correlated with player value) and stable (consistent year over year and across players) the metric will be.
The last thing I will say is that football is so incredibly complicated that any single metric has its flaws and will miss things. A lot of people spend time trying to build predictive models to show what a player will do next year and while that can be fun, I don’t think a pure model will ever be good enough at an individual player level to do that. But now, on to the metrics…
Quarterback:
Composite metric of EPA+CPOE and rushing value
PFF will highlight a few metrics that they see as highly stable, including “clean pocket passer rating” and “average depth of target from a clean pocket”. All QBs ability to succeed drops under pressure so the thinking here is you best measure a QB on how successful they are while protected with more value on deeper passes (as it makes the offense more efficient).
FiveThirtyEight did an analysis here showing better correlation of Completion Percentage Over Expected (CPOE) on predicting NFL success. And Michigan Football Analytics has an awesome post here where the look at CPOE plus Expected Points Added (EPA) and the ability to predict QB success.
All of these metrics above make intuitive sense as a quarterback’s ability to complete passes at depth is a close connection to an offense’s ability to score points. But I do think a lot of these miss out on a QB’s running impact on the game which is why I incorporated rushing value. Below are the R2 values for each of these metrics (if you aren’t familiar with R2, most simply it is how well one variable is at explaining changes in another with higher R2 meaning the two metrics are better correlated).
| Metric | R2 with AV |
| Avg depth of target on clean pocket | 0.017 |
| Clean pocket passer rating | 0.319 |
| CPOE | 0.340 |
| EPA | 0.584 |
| CPOE+EPA | 0.553 |
| Composite CPOE+EPA and rushing value | 0.702 |
Below is the chart of quarterback value (normalized to a full season AV) vs. CPOE+EPA+rushing yards per 600 dropbacks (as an aside, that’s Lamar Jackson’s silly 2019 season way out at the top right).
And below is a gallery of the other mentioned metrics compared to AV, each of which has a looser correlation.
QB Value vs Clean Pocket Passer Rating
The above makes sense – CPOE+EPA is the closest metric to successful drives which translates to points but it ignores a QB’s ability to add value rushing. The calculation I use for the composite CPOE+EPA and rushing metric is:
= 6169.56 * CPOE+EPA + Rushing Yards per 600 Dropbacks
Quick explanation on the above:
- A QB’s passing performance is more important to a team’s success than their rushing performance (the model I used showed rushing had 43% the value of passing). This along with EPA+CPOE being a small number generated the 6169.56 coefficient.
- The best measure of rushing value I found was rushing yards per 600 dropbacks (the average number of dropbacks for quarterbacks over a full season). Measuring this by dropbacks normalizes for QBs that didn’t play a full season.
Wide Receiver / Tight End:
Receiver efficiency
I think PFF has this one nailed with Wide Receiver Efficiency (article here) which values receivers based on three things: ability to separate (yards of separation at the time the ball arrived), ability to catch (catch percentage), and ability to generate yards after the catch (YAC). This is also weighted by average depth of target (aDOT) as there is more value generated from deeper receptions. Again, this one makes intuitive sense – a wide receiver needs to get open hopefully deeper down the field, catch the ball, and then add yards to the completion.
I wrote more on this in the Eagles WR draft philosophy post here and showed a similar set of data using Next Gen Stats’ data as inputs:
Running Back:
Yards After Contact + Receiving Yards After Catch
PFF and Next Gen Stats both have good research on running back value, focusing on a running back’s ability to create more yards than expected for a given rush. Next Gen Stats uses a metric called Rushing Yards Over Expected (RYOE) to identify what a running back created on each rushing play vs. the average (expected) based on where the defenders were, speed, and relative location. Offensive line, scheme, and a back’s own ability to create all factor into their value but you have to separate what a RB controls to get their value.
Yards before contact (YBC) has traditionally been viewed as more of an offensive line stat, and while partly true, a running back obviously can avoid contact with vision and speed. When looking at how both yards after contact (YAC) and YBC vs. Rushing Yards Over Expected (RYOE), both have not-super-high R2 values. YAC does have an R2 almost double YBC’s (0.38 vs 0.21), showing YAC has a relatively larger contribution to a back’s ultimate performance than YBC.
YAC vs RYOE 
YBC vs RYOE
Adding a running back’s pass-catching impact by using Yards After Catch to their ability to create on the ground, you get an R2 of 0.79, meaning 79% of a back’s value is explained by yards after contact and after catch.
This is an improvement over other metrics which have much lower correlations to total running back value:
| Metric | R2 with AV |
| % Rushes with Gains Over Expected (ROE%) | 0.069 |
| Rushing Yards Over Expected (RYOE) | 0.167 |
| Broken Tackles | 0.366 |
| Yards Before Contact (YBC) | 0.501 |
Again, this makes intuitive sense – what separates running backs is, all things equal, their ability to create yards in excess of the rest of the league. On average, contact is made at just under 2 yards per carry and what a back does after that has a relatively larger impact on their ultimate value. The above model sightly improves to an R2 of 0.83 if you do factor in YBC with a coefficient of 0.63 (roughly meaning 63% of yards before contact contribute to a back’s success) but I left that out to not over-complicate it for a small improvement.
Offensive Line:
Pass Block Win Rate + Run Block Win Rate
ESPN’s Run Block Win Rate (RBWR) and Pass Block Win Rate (PBWR) described here are the metrics most focus on to judge offensive line success. Offensive line metrics are much harder to isolate and link the line with AV, particularly for pass blocking because so much depends on the quarterback. In the below showing 2020 PBWR vs. passing net yards per attempt, you see negative outliers on the left (PHI, WFT, CHI) where pass blocking was better than the passing offense – each of these teams had awful quarterback play. And on the right side, teams with marginal pass blocking (TEN, HOU, TAM, MIN) outperformed because of good QB play last year.
RBWR has a bit better link to outcomes but is still impacted by the quality of the running back (and further skewed by mobile quarterbacks) as described above.
This is one area where I think the win rate metrics are better than PFF’s blocking ratings and I wrote more about that here – a couple of teams stick out when looking at PFF’s grades. Pittsburgh, for example, had a really poor line in 2020 and scored that way in PBWR but scored higher in PFF grades because Ben released the ball quicker than any quarterback by a large margin. Here, PBWR tells the fuller picture.
Again, blocking win rates are metrics that make intuitive sense. Pass Block Win Rate measures how often a lineman holds their block for at least 2.5 seconds. Football Outsiders has good data that shows across the league, a quarterback’s DVOA is 108 points lower when under pressure. Run Block Win Rate measures how often the lineman prevents the defense from forcing the runner to adjust a running lane or making a tackle within 3 yards of the line of scrimmage.
Next I will use these metrics to look at the Eagles 2021 season and what we can expect…
Source data and code
All source files and the Python scripts to visualize the data are in my GitHub repo here: https://github.com/greghartpa/position-analyses
Additionally, the source data is included below. All source data files are XLSX with the calculations used to create the QB composite score or WR efficiency in the Excel files – the calculations are not done in the Python scripts, which are only used at this point for visualization.