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17 Again: A Statistical Comparison of Anthony Miller and Alshon Jeffery

Anthony Miller isn’t the first 2nd round, #17-wearing rookie Wide Receiver to make noise in Chicago

NFL: Detroit Lions at Chicago Bears Mike DiNovo-USA TODAY Sports

Anthony Miller is no stranger to comparison. In an open letter he penned to NFL GMs, he noted that he’s spent his entire life watching people compare him to others, calling him “too small” or “too slow”. “I just needed to find a way to get on a practice field and then do what I do.” He said. “Produce. Make plays.“

And make plays he did – Miller’s rookie season ended with him leading the 2018 Bears in touchdown receptions with 7 TD grabs. Donning the number 17, the second-round pick battled through injury to contribute to a campaign that saw the Bears secure a playoff berth for the first time in 8 years. Miller showed his versatility throughout the year as he made contested catches over the middle, ran crisp routes in the end zone, and made defenders miss like he’s done all his life.

Now wait a minute – this is all starting to sound familiar.

Second-round rookie.

Wears #17.

Dealt with injury.

Are we sure we’re not talking about Alshon Jeffery?

Indeed, Anthony Miller and Alshon Jeffery’s rookie seasons are remarkably similar. While the two receivers are of very different size and shape, each started their career playing for a Bears’ team in the midst of changing offenses and contributed solid production throughout the year. They each chose to wear #17 and dealt with personal injury as well as injury to their starting quarterback as the season wore on. Each showed potential as a rookie, and fans left both seasons confident they’d grow as sophomores.

But how do their seasons compare? In the words of today’s youth, “who wore it [#17] better?” This article will attempt to answer these questions and more by diving into their stats in search of answers.

We’ll start off by looking at…

The Raw WR Stats

Player (Year) Games Yards Catches Targets Catch% Longest TD Catches/Game Targets /Game Yards /Game
Player (Year) Games Yards Catches Targets Catch% Longest TD Catches/Game Targets /Game Yards /Game
Anthony Miller* (2018) 15 423 33 54 61.10% 55 7 2.2 3.6 28.2
Alshon Jeffery (2012) 10 367 24 48 50% 55 3 2.4 4.8 36.7
*Does not include postseason play

The first thing that the stats make clear is how similarly these players performed during their respective seasons. Each compiled approximately 400 yards with a longest reception of 55 yards and caught a little more than two receptions per game. That said, the stats also show some key differences; Miller exhibited a significantly better catch percentage than Jeffery and displayed a keener eye for the end zone than the soon-to-be superstar. By contrast, Jeffery received an extra target per game than Miller and managed to produce more yardage per game than the rookie out of Memphis could.

Obviously that’s a lot of information, but does it tell us anything?

Ultimately I don’t think these stats are conclusive enough to draw anything meaningful from. Miller caught more passes, but he also played in more games. Jeffery had more targets per game, but Miller had more catches per target. Jeffery produced more yards per game, but Miller caught more than twice as many touchdowns. What do these comparisons tell us? Do they tell us anything?

I don’t think so, there’s simply too many variables that these stats don’t account for. Raw stats don’t account for things like how their quarterbacks played or how their production factored into the passing offense as a whole. With these factors in mind, I attempted to design a few extra stats to help contextualize the numbers above.

The Contextualized Statistics

Player Raw QB Attempts* Target % Raw QB Yards* Passing Yard % Efficiency Quotient Raw WR Yards Lost* WR Yard % Yards/Target
Player Raw QB Attempts* Target % Raw QB Yards* Passing Yard % Efficiency Quotient Raw WR Yards Lost* WR Yard % Yards/Target
Anthony Miller (2018) 399+76=475 11.37% 3172+515=3687 11.47% 1.01 1535 21.60% 7.83
Alshon Jeffery (2012) 278+50=328 14.63% 2007+171=2178 16.85% 1.15 1232 22.90% 7.64
*Includes only the games the player played in.

Before I get into what these stats tell us, let me explain what they are and what they aim to measure.

  • Raw QB Attempts (QBA): The total number of passes attempted during games the receiver participated in. This number primarily helps determine Target %.
  • Target % (T%): WR Targets divided by QB Attempts converted into a percentage. This number should show the receiver’s overall involvement in the offense and is used in Efficiency Quotient calculation.
  • Raw QB Yards (QBY): Total yards produced by each receiver’s quarterback(s) during games the receiver played in. Mainly used to calculate Passing Yard %, it aims to account for differences in quarterback performance by showing the size of the yardage pool each WR had to draw from.
  • Passing Yard % (PY%): WR Yards divided by QB Yards converted into a percentage. This shows the relative importance of the receiver’s production to their passing offense and is used in Efficiency Quotient calculation.
  • Efficiency Quotient (EQ): Passing Yard % divided by Target %. This simple number should indicate the receiver’s relative performance within their own role – an EQ above 1 indicates overperformance, below 1 shows underperformance, and 1 itself suggests the receiver met expectations.
  • Raw WR Yards Lost (RYL): The sum total of the yardage gained by other WRs during games the receiver participated in. Used to calculate WR Yard %.
  • WR Yard %: Receiver’s Total Yards divided by Total Team WR Yards (Receiver’s Total Yards + RYL) converted into a percentage. This statistic aims to eliminate outside factors (TEs, RBs) and compare each receiver to their team’s total WR production. Effectively an attempt to ensure the PY% and T% aren’t biased due to RB/TE production.
  • Yards/Target (Y/T): WR Yards divided by WR Targets. While obviously not a statistic of my own design, Yards/Target is an elegant ratio that ensures the receiver’s catch rate is factored into their overall efficiency rating.

The beauty of these statistics is that they’re all generated from data calculation rather than subjective injection of my own opinion. These stats don’t assign points based on “how well” the receiver performed their assignment, they simply show the same data from above (catches, yards, targets, etc) in different lights. My hope is that these numbers allow us to successfully compare the two receiver’s abilities to produce rather than to consider their play at all. Spoken another way, these stats should allow us to compare an apple (GUAGI WR) to an orange (shifty WR) by simplifying them into fruit and contrasting from there. But enough about the numbers, let’s discuss what they mean.

If we use T%** to approximate each WR’s role size, it becomes clear that Jeffery and Miller were both given fairly small roles inside their passing offenses. This is to be expected; each had established WRs in front of them taking targets away throughout the year. That said, it’s worth noting that Jeffery’s “small” role was still slightly larger than Miller’s – this was likely due to the 2012 Bears’ lack of a true #2 WR, but that’s neither here nor there.

The next thing we’ll look at is their Efficiency Quotients. As described above, the EQ compares the passing yardage that each receiver generated to the yardage expected of their role and helps us determine over/underperformance. In this case, the stats suggest that Jeffery overproduced his role’s expected yardage while Miller delivered almost exactly what was expected of him. While Jeffery’s overperformance is worth noting, the biggest takeaway from this is that each rookie WR stepped into a real NFL offense and met or exceeded their coaches’ expectations of them. These met expectations assure the coaches that, should the receiver improve in the offseason, they can be trusted with a larger role the following year.

While Jeffery’s high EQ suggests that he had a better season than Miller, Miller’s Yards/Target would say otherwise. Miller produced approximately one more yard than Jeffery every 5 targets; that may not sound like much, but it suggests that Jeffery’s overperformance and Miller’s meeting expectations were roughly the same level of overall production. While this may have been due to the 2018 Bears’ offense being more productive than the 2012 offense, it certainly goes against the notion that Jeffery’s season was outright better than Miller’s.

The final statistic we’ll cover is the WR Yard %. Simply put, this number represents Jeffery and Miller’s respective contributions to the WR corps, thereby excluding RB and TE production. Because both percentages are so close, we can confirm that Miller and Jeffery’s roles inside the WR corps were effectively the same even though Jeffery’s overall passing role was slightly larger.

Now that we’ve compiled all the statistics, what have we learned?

The Conclusion

Anthony Miller and Alshon Jeffery both arrived on the Bears with the expectation that they’d be able to contribute meaningful production in their first season and, based on our analysis, it’s safe to say they each met those expectations. While they operated in similar roles, Jeffery produced more yardage than was expected and Miller led his team in touchdown receptions. Though entirely different in style, these two #17s produced well for the Bears and inspired future excitement for their careers in navy and orange.

Both players had successful seasons and produced at relatively the same rate per-game. While there are hairs to split in favor of each receiver, their statistics across the board are too similar to not declare a direct comparison between the two a tie. Considering we all know how well Jeffery’s sophomore and junior seasons turned out, I think that bodes very well for Anthony Miller.

** My reasoning for using T% as a role size indicator is simple: if the quarterback throws the ball in a receiver’s direction, they deserve credit for being open enough to warrant the throw. And, in cases where the QB is throwing the ball to someone who’s covered, the targeted WR is still being trusted to handle the end of the offensive play. This means that the percentage of times a receiver is targeted is directly related to their overall offensive role and can be used as a role approximation.