Size Does Matter: How an NFL Player's Height and Weight Affect Performance

Every year, it seems like a few talented college players are being snubbed from the NFL because their measurables, in NFL draft speak—height and weight, for example—are poor. To wit:

• Missouri quarterback Chase Daniel finished fourth in the Heisman Trophy voting in 2007—the second-highest ever for a Tiger—and finished his college career with the most offensive yardage in Missouri history. He had over 13,000 total yards in his three full years as a Tiger, including over 4,300 passing yards in both his junior and senior seasons.

His completion percentage was 72.9 percent in his final season, in which he also had 39 touchdowns and 18 interceptions.

But scouting reports said that Daniel, measured at 6'0" at the Scouting Combine, "lacks ideal height for an NFL quarterback." Daniel went undrafted in the 2009 NFL Draft and is currently fighting for the No. 3 quarterback spot on the Washington Redskins.

• Northern Illinois running back Garrett Wolfe’s worst college season was his junior season. He gained a measly 1,580 yards on the ground with a dismal 16 touchdowns on 243 carries, pathetic numbers for a man who also missed three games due to injury.

Sarcasm aside, Wolfe gained over 1,600 and 1,900 yards in his other two seasons (the latter of which led the NCAA), but at 5'7" and 186 pounds, Wolfe was drafted at the end of the third round to the Chicago Bears.

He hasn’t been given much opportunity to shine at all, with 46 career carries in two seasons in the NFL.

• The prototypical small guy, at 5'9", Wes Welker failed to garner much attention after high school. A week after signing day, Welker received a scholarship from Texas Tech when one of the Red Raiders’ projected signees chose a different school.

In his four-year career at Texas Tech, Welker had 3,475 total yards from scrimmage (rushing and receiving) with 23 touchdowns. His eight career punt return touchdowns set an NCAA record (since tied).

Welker went undrafted in the 2004 NFL draft; he signed with the San Diego Chargers but was ultimately cut after Week One.

Nevertheless, Welker showed that small players can succeed in the NFL. After breaking onto the scene in 2006, with 687 yards on 67 receptions for Miami, he was traded to the Patriots. Welker ended up with a league-high 112 catches in 2007, followed up by 111 in 2008; he had over 1,100 yards as well each of those two years.

Others, such as 6'0" Drew Brees—who, among many other accomplishments, threw for 5,000 yards in 2008—have proved that, when given the chance, size doesn’t matter.

In this article, I’ll be testing the effects of height and weight on seasonal and career production, split up by position.

Do taller quarterbacks have a higher completion percentage, on the notion that they can see over the line of scrimmage? Do taller wide receivers have an advantage over smaller receivers when it comes to yards and touchdowns?

As always, my data comes from Pro-Football-Reference.com. Unfortunately, I could only find height and weight data in the college section of P-F-R, so I went through the 212 colleges with 20 or more guys who played in the NFL and collected height and weight data from there. This gave me height and weight data for all but 898 of the more than 10,000 player seasons since 1980 (and career data for all but 227 of the more than 2,300 player careers).

Now, I don’t know the source of P-F-R’s height and weight data; I presume it is the numbers given out by each team. This may have some drawbacks (smaller players will sometimes get a boost to their height or weight, for example), but in general it works.

The first measure of correlation between measurables and production is, well, correlation.

Correlation shows the relationship between two variables in a number between negative-one and one.

The more related two variables are, and the more the graph between the two looks like a perfect line, the closer to one or negative-one the correlation is; a positive number represents a positive relationship—that is, as one stat goes up, so does the other—and a negative number represents a negative relationship—when one variable goes up, the other goes down.

Since that probably made no sense, take a look at this graph, which shows the correlation of several sets of data. If, say, weight and completion percentage form a straight line rising to the right, the correlation will be one. If they form a straight line rising to the left, the correlation is negative-one. And if the