Is Time Money When It Comes To Free Agent Contracts?

Last week, Michael Rosen wrote about Jack Flaherty’s delayed free agency market. Michael advanced a number of theories about why Flaherty hadn’t yet signed a deal, and what that might mean about his fastball, teams’ perceptions of his fastball, and the trajectory of his career broadly speaking. I found that piece really interesting – and I also started thinking about what Flaherty not having signed yet means in a larger sense.

You don’t have to look any further than last year to get an idea of what could happen to Flaherty. Blake Snell and Jordan Montgomery both waited a long time before settling for short-term deals. The year before that, Carlos Correa’s multiple failed physicals kept him on the market until the very end. In 2022, Correa, Kenley Jansen, and Trevor Story all found themselves looking for employment well into March.

All of those players came into the offseason expecting a major contract, and all of them ended up getting less than anticipated, bringing to mind some classic FanGraphs articles from Travis Sawchik, back in the halcyon days of 2018. Those articles drew on a study by Max Rieper that separated free agents into pre- and post-New Year’s signings and found a large discount for the latter group.

The general view espoused in those articles was that teams were able to offer less salary for a given caliber of player by waiting until after January 1 to sign them, with the expected future result being that more and more free agents would see their deals get pushed back. At the time, I remember thinking, “Hmmm, I dunno.” If the first free agent doesn’t sign until February, he’d probably still be pretty coveted. Using a date seemed weird — maybe relative signing speed compared to the rest of the class instead of fixed dates would tell us more. Even so, I wondered what was causing the pattern.

Obviously, you didn’t read that article from me in 2018. That’s because my next thought was probably “Wow, that seven-year Italian government bond is wildly mis-priced” or “Get a load of the Canadian interest rate curve.” I wasn’t a writer then, just a baseball fan reading FanGraphs at lunch, and well, I didn’t have the database access or technical savvy to look into it even if I’d wanted to. So I forgot about it within a week and moved on with my life.

Seven years later, it’s time to listen to 2018 Ben. These days, I have an embarrassment of riches when it comes to data – we all do, in fact, thanks to RosterResource’s Free Agent Tracker. I thought I’d take a crack at looking into my theory for real.

To accomplish this, I took crowdsourced contract estimates from the 2021-22, 2022-23, and 2023-24 offseasons, as well as the actual contracts signed in those years. I chose three years for two reasons: first, and selfishly, the data’s kind of messy as you go further back, which makes it harder to collect. Second, and more importantly, the last three years have clear commonalities. There was no COVID-shortened season among them. Even with the rancor of the 2021-22 CBA negotiations, that offseason saw teams commit significant money to free agents, offering similar financial pictures. And they’ve all featured at least a few top-end players — no weird outlier years where no one of note hits the market.

From there, I started crunching the numbers. In each year, I took the 50 players projected by our crowdsourcing data to receive the highest guaranteed paydays. I discounted deals with deferrals based on present value of the deferrals. I used total salary because while it’s a blunt instrument, it’s one that aligns well with the way late-offseason contracts seem to work; a one-year pillow deal is unquestionably a worse outcome than a five-year pact if they have the same average annual value. Finally, I removed players who chose to retire from the data set.

Here’s one way of looking at that data:

There’s no strong correlation between time in the offseason and projected contract, and I think the bump in the 11-20 group makes sense. That’s roughly corresponded, at least over the past two years, with the Winter Meetings, where many of the highest-profile free agents sign. To wit, I even removed Shohei Ohtani from the group to make sure his outlier contract wasn’t driving things, and it was still the highest-projected group by a solid $15 million.

There appear to be two effects here: The last 10 free agents to sign cleared much less than an unbiased prediction of their deals, and free agents with the largest projected deals signed on the earlier side and beat expectations. Sure, there’s a slope between the 21-30 and 31-40 groups, but it’s so small that I’d hardly call it a trend.

If you fit that into the shape of looking at whether the deal happened before or after the New Year, you’d see a similar pattern to what Rieper found back in 2018:

But take a look at the count of free agents in each group. More free agents are signing early in the process than late these days, a stark difference from the pattern that appeared to be forming last decade. Here’s another way of looking at things, bucketed by month:

January isn’t obviously a member of either group — more of its own “roughly on expectations” bucket — but there are clear gaps between signing early and signing late. In the interest of transparency, I should point out that there were a ton of November signings in the 2021-22 offseason thanks to the lockout, with very few December signings (the lockout started December 2), and no January or February signings at all. If you exclude that year and only look at the last two, the pattern changes a bit:

To my eyes, much of this pattern is driven by the big-ticket free agents. They appear to sign most frequently in December (or in the 11-20 group of the top 50), or wait until the season is about to begin. You could think of this as teams waiting some of them out, but to me, a more logical explanation is that there’s a mismatch between public and team valuations.

Imagine two free agents, both of whom hear from their agents (and see from our crowdsourcing) that they’re projected to make roughly $100 million in free agency. Team valuations differ, though; the high team on Player One wants to offer them $120 million, while the high team on Player Two is only willing to offer $80 million. If both of these offers get made in December, Player One will surely sign early, while Player Two will likely wait his market out, hoping that another bidder will emerge, or that the initial team will up its offer.

This hypothetical situation would result in a strong pattern: bigger than expected contracts early and smaller contracts than expected late. In other words, it would look exactly like the data we have. But it’s not so much because of the calendar; instead, it’s all about the mismatch in valuation. Player Two isn’t getting less money because he signed late; he signed late because he’s getting less money.

Here’s another way you could end up with data like this. Imagine two team strategies. One group of teams has strongly differentiated evaluations of free agents. They think that one particular player – and just that one player – is a great fit for them, or a great deal at the projected contract value, or some other variation on “we want this guy specifically.” The other group of teams have less strong opinions about each individual free agent. They’re more interested in the cost to acquire the player relative to projected value.

In this world, the first group of teams comes out of the gate strong, splashing above-estimate contract offers to the players they’ve identified as the best fits. Those players sign early (because the teams aren’t willing to wait, lest they miss out on their target) and above expectations. That leaves the second group of teams looking for the other free agents, and throwing in value-oriented bids rather than springing for a particular target. If you’re willing to sign whichever of 15 free agents is the best deal, you can afford to wait.

Like my first hypothetical, this would result in higher-than-expected deals early and lower-than-expected deals late. Likewise, though, the players who sign late aren’t getting less money because of what month it is; they’re getting less money because they weren’t a target of the teams that act early to find their players. My point isn’t that I know which of these is right, or even that I’m 100% certain that the calendar isn’t the real culprit. What I’m trying to get across is that the data can be interpreted in many ways.

There’s a second group of free agents with a similar but marginally different pattern: the bottom of the top 50. In the past three years, there have been 105 players in the player pool we’re concerned with who had a crowdsourced total salary estimate of $50 million or less. These are the non-stars, for lack of a better way to put it, the good players who fill out rosters but not the ones likely to warrant a bobblehead giveaway.

This group has its own strong pattern of time versus actual contract:

I think this is a slightly different effect, though I’m not particularly confident in my view. Players in this tier have extremely similar projections, and quite frankly, I think that the crowdsourcing on them ends up more uniform as a result. As a contract predictor myself, I know that I struggle to deviate much from a formula when I’m looking at a 1.4 WAR first baseman or a solid middle reliever, and I assume the crowd does as well.

The issue here is that these players are necessarily on the fringes of teams’ plans. What’s more, there are often fewer good landing spots than available players. So while three first basemen might look broadly similar, if there are only two teams looking for a role player first baseman in free agency, you’re going to end up with a pattern where the first two do substantially better than the third. The first 80% or so of mid-tier free agents get deals roughly in line with what you’d expect, and then the last 20% have to settle for less than expected, at least in the last few years of free agency.

I don’t think this has much effect on how I’ll predict free agent contracts in the future. My guess is that most of the pattern comes from a mismatch between team and public-side valuations on the high end of the market, and roster composition on the low end. I think that teams that specifically target individual free agents are part of the equation, too. You can imagine every free agent as having two projected contracts, one for if he’s a team’s number one target and one for if he’s part of a large group of options. That’s where having private knowledge of team preferences would go a long way towards improving predictions – the problem is that that information isn’t available.

There’s absolutely a pattern to when free agents sign and what they get relative to expectations, that much is certain. What causes this pattern, and whether either teams or players can take advantage of it to change the shape of compensation, is far less clear to me. When the 16 free agents left in this year’s top 50 sign, history suggests that they’ll do so for less than the earlier signers received this year. That won’t mean that the market has experienced a sudden downturn or that everything that happened in December was fool’s gold — it’s just how free agency works now.