This was the first project I oversaw for the Sports Analytics Group at Berkeley and was completed by members in just about four weeks.
During my first semester after founding the club, this project was one of two that was completed. However, this was our first attempt at putting a team of five members on a large-scale research project. While initally, we had eleven teams working on research projects in that first semester, that was obviously not successful. This was one of the teams that I oversaw and assisted, but as this was our first project of this scope, there are certainly some key issues. As time has gone on, we have remedied some of those problems. Below is a description of and from the project, which is available in full here.
Basics
This research creates and offers a new metric called Free Agency Rating (FAR) that evaluates and compares franchises in the National Football League (NFL). FAR is a measure of how good a franchise is at signing unrestricted free agents relative to their talent level in the offseason.
FAR allows us to properly assess how a franchise performs in free agency, specifically how good they are at creating properly valued contracts. Free agency is not the only factor that determines a franchise’s success, but it is significant in allowing the franchise to not only win in the regular season, but go far in the playoffs and contend for championships.
Our hope is that the FAR rankings offer fans and analysts a more quantitative approach to determining where free agents in the future might land. If, for example, three teams are looking to land a particular free agent, it’s reasonable to think the team with highest FAR will have the highest chance of landing that free agent.
This is not always guaranteed to be the case, as there might be other factors weighing into the decision, but it is a reliable tool to use. Perhaps more significant is the impact that this research can have on teams’ front offices. Understanding what factors cause individual free agents to get underpaid and overpaid including talent, age, position can help teams better set minimums and maximums for certain free agents they’re interested in and avoid overpaying and unnecessarily using more cap space.
Methods
This is done by collecting and combining free agent and salary information from Spotrac and player overall ratings from Madden over the last six years. This research allowed us to validate our assumptions about which franchises are better in free agency, as we could compare them side to side. It also helped us understand whether certain factors that we assumed drove free agent success actually do.
We explored this concept through examining:
- Team Winning Percentage
- Team Market Size
- Weather
- Team Region
- Free Agency and Positional Differences
among other things.
Results
The results of this research show that the single most significant factor of free agency success is a franchise’s winning culture. Meanwhile, factors like market size and weather, do not correlate to a significant degree with FAR, like we might anticipate.
We showed that there is a strong correlation between winning and FAR, although it is hard to tell which causes which. For example, it’s shown that bad teams tend to have to overpay, while good teams tend to be able to sign free agents at discounted values. On the flipside, overpaying for free agents can lead to an unbalanced and less complete team, as we saw with the Indianapolis Colts, who stopped winning after having an offseason of overpaying.
However, the theory that free agents highly value things like market size, weather, and region has been debunked. As shown above, there is little to no correlation between these factors and FAR, as one might expect, even though in some few cases, having good weather or having a large population might help. It is clear that building a winning culture is the single most important factor to succeed in free agency.
Future Work
In the future, this research will help teams develop better strategies and help fans and analysts better project where free agents might go.
There are many ways to apply this research. First off, when future free agency seasons come around and each unrestricted free agent is linked to a few teams, the findings in this research can help to assess approximate probabilities of the free agent signing with those teams and thus, better predict where they’ll go. Additionally, this research could be used to accurately predict whether free agents will get underpaid or overpaid, and by how much they will get underpaid or overpaid by using machine learning techniques. Factors to be considered would include current team, current salary, position, age and overall rating.
One limitation for this research was the time range, as we were unable to find reliable salary and free agent information from before 2011. Expanding the time frame of this analysis would give us a much larger sample size and help us better recognize how teams stack up against each other in terms of free agency over time, understand how winning affects FAR in different time periods, as well as identify trends and patterns in free agency over time. Perhaps the biggest impact of our research is that this FAR metric can be created in any sports league that has free agency, such as the NBA, MLB, and NHL.
In fact, free agency is arguably a bigger part of a team’s success in these sports than in the NFL.
Closing Thoughts
As this was one of first projects working with sports data at this scale, I am certainly proud of our effort. The practice of identifying where a player will sign resonates in every sport and with every fan. Certainly, future work would try to actually model this information and develop some sort of accuracy for such a model. As with most of our projects, this type of method allowed our undergraduate members and opportunity to work with this type of data. There is much to be done with this concept and I’d love to see further research on the subject.