With the NFL Draft fast approaching, I am taking a deep dive into the numbers behind the selection process to highlight tendencies that could optimize the Bears’ selection efficiency for the 2022 draft.
Last week, I covered the best high-level approach. Briefly, I detailed my web scraping algorithm. So, this week, I’ll analyze the 2021 Draft class to determine trends among positional groups regarding immediate impact.
Once again, I will be doing so through PPV (Pure Point Value), a new stat I have created that acts as a derivative to Pro Football Reference’s Adjusted Value but factors in historical metrics to develop a more bottom-line approach to players' value.
I’ll acknowledge that this study only analyzes the 2021 Draft class’s performance, focusing on immediate impact. Still, looking at the Bears’ 2022 schedule, I believe this team could be far better than people expect, particularly if they get players that can add an immediate impact.
The meat of this project is similar to that of my previous project.
I enhanced my Python data extraction algorithm to widen the breadth of my search, linked it to Microsoft Excel, and calculated each player’s PPV for the 2021 NFL season. I then charted this data according to each player’s chart value — simply the value of their draft pick according to the NFL Draft Trade Value Chart.
The graph came out as follows:
As you can see, the expected PPV is about 0.11 percent of a player’s chart value. Yes, this trendline exhibits a squared correlation coefficient of about 0.40 percent, which qualifies as mediocre. Still, the two-tailed p-value for the data is nearly 0 percent, demonstrating that the likelihood of such a trend occurring by chance is extremely low.
I then took things one step further. For each position group and round, I determined each player’s t-score to determine where they would lie on a traditional normal distribution and averaged this t-score to determine holistic performance by round and position.
As an aside, one can use this methodology to determine whether or not a given player is a “bust.” If their PPV falls below the trendline, they will qualify as one, and if their PPV falls above the trendline, they will not.
With all that said, let’s break down the results.
We begin by analyzing the early draft picks, meaning those in rounds 1 and 2. The graphs turned out as follows.
As you can see, quarterbacks have the lowest t-score value by far, with an average of -1.06. That makes sense seeing as that position is usually the hardest to transition to between college and the NFL. After all, most college quarterbacks come from programs where they are always the most talented team by a mile. It is challenging to go to an NFL team where they will inevitably be a part of the least talented team on the field most nights.
However, the best performers were those in the front seven with an average t-score of about 1.13, demonstrating that it is best to take a defensive lineman or a linebacker for immediate impact.
Next, we shift to mid-round picks by analyzing the data from Rounds 3 and 4.
Interestingly, Round 3 showed more volatility on both sides of the ball. But one thing that remained consistent from early draft picks was offensive line performance. Offensive linemen had an average t-score of 0.92 in Round 1 and 0.96 in Round 2, and in round 3, they suffered minimal drop-off, as they averaged a t-score of about 0.86.
From a value perspective, this suggests that in a class as deep as this one, selecting a position of greater need (i.e., wide receiver) and taking an offensive lineman with a mid-round pick could serve best for 2022. In Round 4, the biggest takeaway is the emergence of the running back position. Running backs had an average t-score of 1.31, which rivals the 1.43 of Round 2.
Again, from a value perspective, it doesn’t make sense to use an early draft pick on a running back (or pay one, for that matter), especially when you factor in their limited lifespan and dependence on the offensive line.
Another noteworthy trend is that defensive back performance in Rounds 3 and 4 was far greater than in the early rounds. Surprisingly, this will continue as we progress into the later rounds.
We conclude this analysis by breaking down “late” rounds (i.e., rounds 5 through 7).
From a pure performance perspective, things take a dip in later rounds with all positions except running backs, defensive backs, and — to an extent — defensive linemen.
Such is a microcosm of this analysis. For “can’t miss” performers, look to members of the front seven, running backs, and (in mid to early rounds) offensive linemen. For high-risk, high-reward picks, look for wide receivers and quarterbacks.
The Bears drafted who they hope is their long-term answer at quarterback last year, but wide receiver is a clear position of need this spring. According to the data, the optimal position for this selection would be in Rounds 3 or 4. This approach would coincide with my previous analysis, from which I concluded that you achieve consistent draft success by trading down.
However, this process must also be balanced with the qualitative analysis of each player.
For example, if a player will become a superstar X receiver (i.e., Christian Watson), the Bears should snatch that guy up at pick No. 39 regardless of any metrics. Depending on how the board plays out, though, I wouldn’t mind the Bears taking an offensive lineman at pick No. 39 while trading back out of pick No. 48 to stockpile two late second-rounders and taking either a receiver or defensive back.
After all, the numbers indeed suggest that such an approach serves best, especially in a deep class.
Here’s to hoping that whatever the Bears do, it has a strong impact — ideally sooner rather than later.