Wednesday, January 29, 2014

The value of free agents in the NFL - Part 3

Check out Part 1 and Part 2 of this ongoing and overly long series on the value of free agents in the NFL

It doesn’t matter if your players perform slightly worse if you can get a good deal on them. We’ve been looking at performance but value is the real target here.

Before we get all the way to value we’ll start with a look at cost.

The average player who changes teams is not cashing in on a big payday in the way we might think. While players like Paul Kruger (in 2013 to the Browns) or Matt Cassel (in 2009 to the Chiefs) come to mind when looking at free agency, NFL contracts are basically a series of one year options held by the team. 

For good players, the ability to hold out can give them some leverage for a raise as long as they continue to be good. If a player is merely average they can only hope that the team would have to take a big cap hit to cut them, most won’t see a dollar more from the team if they are let go[1].

Figure 10 - Change in SC% (Prior Starters)

Figure 12 - Changed Team
Figure 11 - Same Team

In the season before changing teams, the average salary cap hit for a player who will change is 2.20% (1.24% for non-starters, 2.87% for starters). Once they change their salary cap hit is 1.56% (0.89% for prior non-starters, 2.03% for prior starters). Most of these players are taking a pay cut to stay in the league.

Tuesday, January 14, 2014

The value of free agents in the NFL - Part 2

The previous post in this series (check out Part 1 here if you missed it earlier) looked at whether teams do well based on how many homegrown players they have or start. There’s some small ability to explain performance but it doesn’t get to the level of detail needed to inform specific allocation decisions. Looking at how well individual players perform when changing teams, however, is a promising way to get enough information for just that kind of decision.

Player Performance

There are a couple trackable and quantifiable factors we might expect to predict a player’s performance in the next season. First and foremost is how they are performing this year. We will be using Approximate Value as created and calculated by the good folks at here as a proxy for performance.

As I qualify any analysis based on this data set, AV is necessarily and admittedly approximate (it’s in the name if you look closely). By using it in a data set as large as this we should avoid having random one-offs impact the results significantly. A great AV typically results from a great player on a good to great unit (offensive or defensive). A good AV means a player played a significant role on a good unit or a huge role on a middling one. A middling AV might be a decent player on a bad team or a rarely-used player on a good team. A low AV is a player who didn’t play or a player who played on a very bad unit.

Average AV by Performance
Pro Bowl (All)
PB (not All-Pro)
Starter (All)
Starter (no AP/PB)

Next up is whether a player changed teams. It’s the point of the analysis and it would be a bit disappointing if we didn’t include it here. Those players who change teams should be expected to suffer a decline in performance.

We could conceivably see differences in the ability to integrate into a new team based on the position played with less complex positions potentially requiring less adjustment and yielding better performance for team-changers. To attempt to address this, we’ll split data set into four sets with one for each combination of offense/defense and skilled/unskilled and run the regression for each[1].

The age of a player and their number of years in the league might also be informative so we’ll add them to the regression[2]. They will interact somewhat but we’ll let the model determine which one reflects the positive bias in the data (see below) and which one reflects the inevitable decline of a player as they age. In place of a constant, these two terms will interact to create a base for the average player/years played combination. For example in this data set the average player in their first season is 23.33 years old, 24.32 in their second, 25.29 in their third and so on.

Wednesday, January 8, 2014

The value of free agents in the NFL - Part 1

The primary goal of an NFL front office, absent any special directives from ownership, is to put together a team that can win games. Different teams go about this in wildly different ways. The Washington Redskins of the early- to mid-Daniel Snyder tenure exemplified a win-now mentality focused on free agency. The Green Bay Packers and Pittsburgh Steelers have focused to a fault on home grown players drafted into the organization. Just leaving it at that would be a nice quip, something you could probably squeeze down to a tweet.

As quickly as you might cite the Redskins, however, you could bring up the New Orleans Saints who put together a Super Bowl-winning team around key free agent signings and had only 12 starters who began their careers with the team. On the other side of things the 2006 Colts had 22 homegrown starters and won the Super Bowl while the 2004 49ers rode 22 homegrown starters all the way to a number one draft pick.

If the NFL didn’t have a salary cap, there might be a clear case for free agency to be a better strategy to assemble a team for teams with the highest budgets. Why go through the effort of evaluating college players and trying to pick out the best ones when you can just wait a couple years and sign them?

Despite anecdotal examples to the contrary, under the collective bargaining structure that is in place in the NFL  with a hard salary cap – it is less effective in general to use players who were signed from another team than those from your own team in both performance and value for money.

Team Performance

To ground the analysis and get started on what is likely to be an indulgently long series of posts, let’s take a few moments to look at the output and make sure this hypothesis even makes sense to review. We will use data from the 2003-09 period (because that's where our other data will come from) to show that teams made up of homegrown players do outperform those with imported players.