2017 NFL Draft Prospect: Chris Godwin
As we barrel toward the combine and eventually the NFL draft, RotoViz is here to give you a unique look at draft prospects. Today my focus is on Chris Godwin.
I don’t consider myself a prospect expert, so when I dive into players I like to cheat off the great work done in years past here at RotoViz identifying the traits that we should be looking at in wide receivers. RotoDoc and Josh Hermsmeyer are working to build on that this offseason by creating a new model to project WR performance, and I’m excited to see where Godwin fits into that model. Shawn Siegele summed the key factors to look for in a recent post looking at true junior WRs in this year’s class:
Based on the wealth of previous research, we’re confident that early college dominance, final market share yardage, whether a receiver declares early, and draft position are the most important metrics for projecting NFL performance. In addition, TD production, yards per reception, rushing usage, size, and athleticism are all likely candidates to end up in the machine-learning model, with post-NFL Draft updates likely to include opportunity and QB quality.
Because Godwin is a true junior himself, Shawn gave a brief profile of him in the same post. Here is what he had to say:
CHRIS GODWIN, PENN STATE, 6-1, 205, 20.8
Godwin got a big boost by finishing his career with 9-187-2 line in a Rose Bowl loss to Southern California. The No. 25 receiver recruit from 2014 blew up as a sophomore with almost 40 percent of the yards thrown by the much-maligned Christian Hackenberg.
Year G Rec Yds Avg TD msYD msTD 2014 13 26 338 13 2 0.11 0.15 2015 13 69 1101 16 5 0.39 0.25 2016 13 59 982 16.6 11 0.27 0.38 Career 154 2421 15.7 18 0.26 0.29
Godwin’s career numbers are just outside the elite prospect range, but his 2015 yardage and 2016 scoring paint the picture of a player with plenty of upside in the right NFL environment.
First of all, be sure to check out Shawn’s piece that includes similar write-ups on the other true juniors in this class, and keep an eye out for his pieces focusing on redshirt juniors, true seniors, and redshirt seniors coming soon. They are invaluable resources.
With respect to Godwin, though his 0.39 msYD and 0.38 msTD came in separate seasons, they speak to a production profile that might be slightly underwhelming for his career but doesn’t have any glaring flaws. Last offseason, Kevin Cole looked at what metrics matter most for projecting WRs with a decision tree analysis that argued production trumps athleticism at the position.1 Cole’s decision tree looked like this:
The decimal in each “node” is the success rate, n is the number of prospects who fall into that node, and the percentage is how many players in the overall sample n represents. Godwin’s career market share of receiving yards of 0.26 falls short of the first decision, which puts him on the left side of the tree. He clears the final year receiving-yards marker, although it’s fair to note by only about 50 yards. The next node is receptions per game, where fewer actually indicates greater success once you’ve accounted for raw receiving yards. In other words, can a receiver hit that receiving-yardage benchmark without simply accumulating a ton of catches?
Godwin’s 4.5 receptions per game is below that mark, and it puts him in a final node with a historical success rate of 50 percent, albeit on a small sample of just 14 players.
There are some other positives here though. For instance, on the other side of the tree,2 a final year yards-per-reception rate of 16 comes into play, a number Godwin surpassed (16.6). We also see age come into play on the left side, which I’ll get into below, but Godwin is well below the cutoff there.
Those decisions don’t come into play for Godwin, but they are important traits for projecting WR performance. My point is simply that, more often than not, Godwin’s profile is one that would move him into more successful nodes than less, based on this tree.
He’s Just a Baby
It shouldn’t be ignored that the two benchmarks Godwin misses are the most important — career msYD and final year msYD — as they are how the most successful node is identified.3 But what does work in Godwin’s favor is he’s one of the youngest prospects in this year’s class. That Godwin falls into a node with a historical success rate of 50 percent without his age coming into play is a great sign, because his best collegiate season came at just 19 years old and he played his final game at 20.
CBS Sports projects Godwin as a third-round pick, and how well he tests at the combine will almost certainly have an impact on whether he rises or falls from there. But what we have here is a receiver whose market share numbers a bit underwhelming but not really terrible. And then we consider the age at which he put up those numbers, and he feels a bit undervalued at present.
Godwin’s numbers don’t jump off the page, but he checks a lot of boxes. Circling back to the traits Siegele identified, Godwin has early college dominance, declaring early, TD production, yards per reception, and size covered. His athleticism and draft position remain to be seen, but he really only falls short in final-year market share yardage and rushing production, the latter of which isn’t a serious red flag.
I’m very interested to see where he falls in the aforementioned machine-learning model, as well as the draft. If he winds up in a good NFL situation, I’ll be making him a priority pick in the second round of rookie drafts.