This is Matt Swartz’ fifth piece as part of his July residency at FanGraphs. A former contributor to FanGraphs and the Hardball Times — and current contributor to MLB Trade Rumors — Swartz also works as consultant to a Major League team. You can find him on Twitter here. Read the work of all our residents here.
When I tell people about my side career as a baseball analyst, they frequently ask me of what research I’m most proud. The answer? The work I did establishing that teams receive fewer WAR per Dollar when signing free agents away from other teams than when re-signing their own players.
My clearest and most thorough analysis of this topic came in the 2012 Hardball Times Annual. The results were initially met with strong skepticism when I published a post on the topic at Baseball Prospectus back in 2010. It took a couple years of evidence before I was able to persuade the sabermetric community that it was true — and, more importantly, that the reason for this phenomenon was that teams re-signing their own players had better information on them.
My 2012 Hardball Times Annual article tested and confirmed that this held true for a variety of players. Traded MLB players and traded minor-league prospects both tended to underperform their projections when compared to untraded players.
What’s the significance of this discovery? Generally speaking, it means that an “average” player who reaches free agency is overvalued by his projections relative to another “average” player who doesn’t reach free agency. So much of sabermetric analysis involves looking at free agents. Suddenly, I had research indicating that such analysis was based on a biased sample. The results immediately colored every potential free-agent signing. With every free agent I encountered afterward, I began asking myself: “is there some reason this player’s original team let him go?”
When most sabermetric discoveries are made, they remain true even if the world knows about them. The physics behind batted-ball distance doesn’t change simply because we can more accurately measure launch angle or exit velocity. That pitchers exert more control over fielding-independent events (such as strikeouts and walks) than other outcomes remains true regardless of how commonly accepted that principle is.
That said, my “Other People’s Players” (OPP) finding is different (and shares something in common with catcher pitch-framing, too): the more widely understood the phenomenon, the less likely it is to remain true. Were teams to believe that free agents signed away from other clubs might be “lemons,” they would likely shy away from signing them.
This possibility is what I explore in the present article — and I’m actually pleased and surprised to find that the “OPP Premium” is eroding. Teams are becoming more careful when signing players from other teams and putting a premium back onto their own players when re-signing them.
Whether my analysis precipitated this development, I have no idea. I think it’s fair to suppose, however, that a general awareness of this phenomenon probably did drive teams to sign more of their own players — especially their own pitchers (on whom the OPP Premium was largest). But since many teams probably tested such things internally, I can’t verify that my own work was responsible for any of this. I will say that team officials with whom I’ve met with over the years have been aware of the concept, whether due to my research, word of mouth, or their own club’s confirmation of the same facts by way of internal research.
My initial analysis focused on multi-year deals because of the complications involved with analyzing one-year deals for free agents. Such players (i.e. those on one-year deals) were frequently close enough to replacement level that small changes in the approximation of replacement level would lead to very large changes in $/WAR (as the denominator changed drastically in percentage terms). One-year deals are frequently laden with incentives, as well, amplifying the effects of even small inaccuracies in the contract data I was able to procure. Throwing out such deals can sometimes lead to a crisper answers, while also removing one-year “make good” deals that could be signed with other benefits besides just money in mind.
It’s not that I was cherry-picking data. The OPP Premium still obviously existed in aggregate when looking at all players collectively, but it was best to ignore those one-year deals (or just small deals in general) in order to really identify the existence the OPP Premium effect. In fact, the decline of the OPP Premium that I’m reporting in this article would disappear if we didn’t limit our sample to the relevant subset of free-agent deals.
Another important aspect of the OPP Premium that I identified as the idea was being scrutinized by the sabermetric community is that “hometown discounts” given to players who sign contracts well in advance of free agency (in exchange for financial security) are a huge part of the OPP Premium. Since their cases don’t feature the same cases of information asymmetry on which I’ve focused, I think that excluding deals signed more than a year before free agency is the best approach, as well. This lowers the OPP Premium of course, but it better reflects the truth.
When we compare the OPP Premium in the “pre-THT Annual” and “post-THT Annual” time periods (loosely, 2006-11 versus 2012-16), we see that the more precise the subset of contracts reviewed, the more pronounced the decline in the premium. The last column shows that on the most relevant subset of contracts — multi-year deals over $2 million above the league minimum salary, signed less than one year before a player reaches free agency — the OPP Premium declined from 20% to 7%. Besides the peskiest deals (those under $2 million, for which I frequently have poor information on incentives), the OPP Premium declines for every other subset included in the sample.
|Year Range||All Players||Net AAV >= $2MM||Net AAV >= $2MM,
|Net AAV >= $2MM,
Signed w/in 1 year of FA
The table below breaks down the information contained in the last column. Note that teams spent significantly more on their own players over time, and that was the driver closing this gap. This lends some important credibility to my claim in the first article in this series that the effect of teams learning more information leads them to bid up undervalued players as much as it causes them to lower their spending on overvalued players.
Indeed, we see that OPP cost per WAR did actually go up from the pre-period to the post-period, albeit slower than the rapid increase for re-signed players.
|Year Range||Player-Seasons Re-signed||Player-Seasons OPP||$/WAR Resign||$/WAR OPP||OPP Premium|
When I drilled down into the details of this finding, I contrasted the OPP Premium for hitters and pitchers and found that it was quite different. Pitchers are frequently injured and frequently decline in skill level suddenly, so private information about a team’s own pitchers made teams uniquely able to eschew re-signing their riskiest pitchers. The OPP Premium was more than twice as large for pitchers as for hitters. When I wrote about this finding earlier, I frequently noted the importance of this distinction.
Fascinatingly, the OPP Premium for pitchers has completely disappeared and reversed itself, while the OPP Premium for hitters has increased. I suspect that this is just a function of limited sample sizes, but it could also be a case of the market overcorrecting for the risk of signing other team’s pitchers.
|Player-Seasons OPP||$/WAR Resign||$/WAR OPP||OPP Premium|
|Player-Seasons OPP||$/WAR Resign||$/WAR OPP||OPP Premium|
I think the best evidence is that teams have gotten smarter about signing free agents from other teams, and the market has become more efficient as a result. While it is difficult to say for sure due to sample sizes, the marked decline in OPP Premium for pitchers in particular suggests teams now consider the information asymmetry when targeting free agents.
While, as I say, the OPP Premium is probably the most significant outcome of my work on free-agent market inefficiencies, there are several other sources of variation in cost per WAR that I have found, as well. The subsequent articles in this series will analyze changes over time in these discrepancies.
Matt writes for FanGraphs and The Hardball Times, and models arbitration salaries for MLB Trade Rumors. Follow him on Twitter @Matt_Swa.