The mission of this column is to find what parts of an NFL prospect’s profile that actually matter. Does a wide receiver’s college receiving yards per game matter to overall success? What about just for his NFL YPT? Has the 40-yard dash been correlated to NFL PPR fantasy points? The list goes on and on, but this column will answer a lot of those questions.
Everything you’ll see in this wide receivers column will be replicated for all positions, including offensive tackle, interior offensive line, and all of the defensive positions. If you haven’t already, make sure to take a look at the NFL Draft Analytics for QBs and NFL Draft Analytics for RBs columns.
Correlation Coefficients:
The chart below will look complicated, but it’s really not. The left side of the chart is the college stats, and the top of the chart is the NFL stats. The darker the color (either green or red) the more correlated the two stats are. If there isn’t a lot of color in the cell, then the two stats have little to zero relationship. Here’s a short video if you’re confused. Lastly, the NFL data is for NFL seasons two through four (not the rookie season) because that’s when an NFL team is seeing the biggest returns on the draft pick, and it helps us avoid games where players were barely used. … The numbers shown in the chart are correlation coefficients. If you want R-squared, just square the number. I chose correlation coefficients to show positive and negative correlations with color.
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Statistics that have historically NOT been correlated to early NFL success (AV/G) for WRs:
1. NFL Combine Vertical Jump (R-squared = 0.00)
2. College YPR vs. Teammates (0.00)
3. NFL Combine 40-Yard Dash (0.00)
4. NFL Combine Broad Jump (0.00)
5. College YPR (0.00)
6. NFL Combine Shuttle (0.00)
7. NFL Combine Cone Drill (0.01)
8. NFL Combine Agility Score (0.01)
9. TD% (Rec) vs. Teammates (0.01)
10. TD% (Rec) (0.01)
11. NFL Combine Weight (0.01)
12. NFL Combine Speed Score (0.01)
13. NFL Combine Height (0.01)
Statistics that have historically been correlated to early NFL success (AV/G) for WRs:
1. College Market Share of Receptions (R-squared = 0.06)
2. College Dominance Rating (0.06)
3. College Market Share of Receiving Yards (0.06)
4. College Market Share of Receiving TDs (0.05)
5. College PPR Fantasy Points (0.05)
6. College Receiving TD/G (0.05)
Yikes. These stats just aren’t very correlated to early NFL success, well at least for approximate value per game. However, they are a little better at predicting PPR fantasy points, but the metrics still only explain about 5-10% of variations in NFL PPR fantasy points if you’re using the final college season, but there are other ways to get better results (age-adjusted seasons, career, etc.) that I’ll get to in a future column.
However, if you still want to take advantage of the small correlations here, you definitely want to focus on the market share data. In general, the market share data outperformed the total production and per game production. In fact, the four top metrics in this sample were the market share metrics -- dominance rating adds up market share of receiving yards and market share of receiving touchdowns.
As has been the case with quarterbacks and running backs, college fantasy points are high on the list. I hope this is enough evidence for you to start reading my weekly CFB DFS articles. In fact, all of this research was just a big ploy for you to play college football DFS.
Conclusion:
The NFL Draft has not been great at evaluating wide receivers relative to other positions. The r-squared for the position is only 0.14, meaning that only 14% of the variation in early NFL success for wide receivers can be explained by the overall draft pick. That’s not very good, but I’m expecting this number to improve with college football analytics starting to improve.
I theorize that the newest college football metrics will be better than what we currently have. For example, some of the stats in my 2019 NFL Draft WR Stats Report don’t have historical data, so we can’t measure correlation yet. But marginal efficiency/explosion, YPT vs. teammates, and other per-play metrics should outperform the current metrics that have historical data. So I’m using those data points as a part of my evaluation even if I don’t have proof that they are meaningful.
But if you want to build models that have historical data, I’ve found that market share -- specifically per game market share -- and college PPR fantasy points have been the most correlated to early NFL success for receivers. On the other hand, the NFL Combine has been little to zero correlation, meaning that it’s smart to buy-low on receivers who did mediocre (not terrible) at the combine.
On the next page, we will get away from overall early NFL success and look at what’s correlated to individual statistics like fantasy points, receiving yards, receiving touchdowns, catch rate, and YPT. There are some interesting findings when we begin to plot these data points on graphs. Join me!
Data notes: The wide receivers sampled (n=325) met the following requirements: Had a total Approximate Value of at least 2.0 during NFL seasons two through four and were drafted between the year 2000-2017. Some of these wide receivers that qualified didn’t play in the FBS, so their college statistics were omitted but I kept them in the sample while researching the NFL Combine. It’s up to you if you want to extrapolate these college production/efficiency findings to the FCS level. Also, not every wide receiver participated in the NFL Combine, but the sample (n=281 for the 40-yard dash) is large enough to take these NFL Combine results seriously. The reason I choose the year 2000 was to keep the sample large enough to have reasonable results, but not too far away from “Today’s NFL” that is vastly different than the 1980s. Lastly, since this sample is looking at NFL seasons two through four, it’s up to you to extrapolate these findings to projected rookie seasons and seasons coming after the rookie contract. I’d guess that the data would change marginally, but the major takeaways would more-or-less stay the same. … In order to establish a baseline for what “NFL success” is, I’ve decided to use Pro Football Reference’s Approximate Value statistic. While not perfect, it’s a good enough metric for the column (and it will help me analyze all positions, which will ultimately let me produce an Analytics Top-300 Big Board). If you aren’t familiar with approximate value per game (AV/G), here is how the top 2018 NFL WRs finished in AV. In general, the NFL-caliber starters have an AV/G of 0.50, and the studs are above 0.75 AV/G. Knowing this will help you as you scan the charts on Page 2 and 3.
