Six Batters, One Billion Dollars, and an Era-Adjusted Reality Check
February 2026 Newsletter
Six Bats, One Billion Dollars, and the Illusion of Separation
This offseason, the big 6 free agent batters walked into free agency and walked out with contracts totaling almost $1 billion. Not theoretical dollars. Not incentive dollars. Real, fully-guaranteed, franchise-shaping dollars.
Front offices went out and PAID for bats:
Kyle Tucker: 4 years, $240M (29 years old to start the 2026 season)
Bo Bichette: 3 years, $126M (28 years old)
Alex Bregman: 5 years, $175M (31 years old)
Cody Bellinger: 5 years, $162.5M (30 years old)
Pete Alonso: 5 years, $155M (31 years old)
Kyle Schwarber: 5 years, $150M (33 years old)
The public justification was simple: elite hitters cost elite money.
The analytical question is more complex:
How different are they really, once you strip away single-season mythology and assess from an era-adjusted lens?
Using three-year rolling performance, era-adjusted stats (ebWAR, adjusted BA, adjusted HR rates, and adjusted BB rates), similarity clustering, and PCA skill mapping, we can look at what teams actually paid for. This is beyond a comparison to historical mega contracts and dollar per bWAR, which we looked at last offseason in response to the Juan Soto megadeal.
Spoiler: this market did not buy six different species of superstar.
It bought variations of the same animal.
With different fur patterns.
And one of them got paid like a dragon.
Satirical Aside: Seeing that Kyle Tucker deal, you’d think he was hitting 60 home runs a year. In reality, he’s the modern version of Larry Walker.
The Three-Year Lens: Memory is not a Metric
Contracts are forward-looking, but projections lean heavily on the recent past. Teams usually are wary of paying guys who have had a single breakout season in a contract year. Therefore, we assessed the free agents’ performance over a three-year window.
When you compare:
Raw bWAR vs era-adjusted bWAR
Raw BA vs era-adjusted BA
HR and BB rates normalized to the talent pool
Something interesting happens:
The spread compresses. Era adjustment is the great equalizer.
Satirical aside: Era adjustment is the statistical version of removing Instagram filters.
We can tier the players and their deals based on their recent play.
Tier 1: Kyle Tucker
Small Gap
Tier 2: Alex Bregman, Cody Bellinger
Big Gap
Tier 3: Kyle Schwarber, Pete Alonso
Small Gap
Tier 4: Bo Bichette
So why does Tucker get so much more than everyone else when his average bWAR and ebWAR isn’t significantly higher? And why does Bichette make equal almost to Bregman and Bellinger when he seems to average the least bWAR?
Kyle Tucker — Priced Like a Tier Above
Kyle Tucker received the largest annual value of the group, doubling the value of the lowest AAV.
From a three-year era-adjusted lens:
Highest ebWAR and bWAR average at 5.13 and 4.9, even with an injury-riddled 2024.
Strong rate balance; PCA analysis shows his high bWAR comes from contributions across BA, HR, BB, rather than just HR like Schwarber/Alonso
Low volatility; standard deviation of 0.529 for bWAR, second lowest among the group, but significantly more bWAR per season than Alonso, who has the lowest
Excellent consistency; Bellinger and Bichette have had down seasons in one of the last three years, and Bregman’s highest ebWAR season would rank third for the last 3 years, compared to Tucker
But here’s the twist: when we ran multivariate similarity distance on era-adjusted stats, Tucker did not sit alone.
His nearest statistical neighbor: Cody Bellinger.
Not MVP Bellinger. Not Rookie-of-the-Year Bellinger. Not Dodgers World Series champion Bellinger. Just present-form Bellinger, who played pretty well for the Cubs and Yankees. Both also had a semi-down season in between, leading them to change squads.
Satirical aside:
The Tucker contract suggests a unicorn like Henry Aaron. The math suggests a very expensive horse with excellent posture, akin to Joey Votto.
To be clear: Tucker grades extremely well. But the analysis shows cluster leadership, not statistical monarchy, for both era-adjusted and raw stats. As teams do pay for raw stats rather than era-adjusted stats, this justifies the AAV a little more, but not significantly. But the era adjustment gives him the least benefit for the doubt.
Cody Bellinger — The Shape-Shifter Profile
Cody Bellinger is the weirdest similarity case in the dataset.
Across three-year era-adjusted metrics:
Solid average bWAR (4) and ebWAR (4.82)
In the era-adjusted PCA similarity space, Bellinger is Tucker’s closest statistical neighbor (distance = 1.82), closer than Bregman, Schwarber, or Alonso.
Bellinger’s three-year bWAR standard deviation (1.56) sits in the middle of the group — more volatile than Tucker or Bregman, but far steadier than Bichette or Schwarber.
Bellinger’s standardized three-year profile shows positive batting average (+0.91), with only moderate negatives in HR rate (−0.33) and walk rate (−0.72), indicating multi-channel value rather than a single-stat dependency (the WAR dependency indicates good fielding), and a balanced skill mix that is focused on contact.
In PCA skill mapping (reducing bWAR, ebWAR, HR rate, BB rate, BA into two dominant dimensions), Bellinger sits near the “balanced production” ridge.
Satirical aside:
He’s statistically positionless — like modern basketball, but with more pine tar.
The Cubs and Yankees paid him like a stable middle-of-cluster star, which stability metrics say is fair.
Alex Bregman — Quietly Near the Top of Everything
Alex Bregman is the analytical favorite, not because he leads every category, but because he avoids weakness everywhere.
Three-year era-adjusted profile:
bWAR (4.17) and ebWAR (4.98) averages in the high cluster
None of his rate stats show extreme spikes, indicating value built from across-category contribution rather than one carrying tool.
His walk rate sits near the group mean (z = −0.17), meaning his value is supported by plate discipline without being dependent on extreme walk-rate inflation.
His three-year bWAR standard deviation (0.72) ranks among the lowest in the group, indicating a tight year-to-year value spread.
PCA loadings show bWAR and ebWAR dominate the primary value axis — and Bregman scores well there without extreme rate dependencies.
Satirical aside:
If spreadsheets voted for MVP, Bregman would win in a landslide and give a very organized acceptance speech.
He is not flashy, but rather analytically sound.
Pete Alonso — The Loud Tool Specialist
Pete Alonso lives on the power-heavy end of the skill spectrum.
Clustering analysis pairs him early with Schwarber because:
Lower bWAR (3.1) and ebWAR (3.89) averages
HR-rate drives value; Power-heavy profile confirmed by PCA scaled input of HR rate +0.834
Walk rate supports OBP, while BA contributes less relative share, with PCA scaled input of -0.709
Unlike Schwarber, consistent, low bWAR standard deviation (0.436)
Era adjustment trims, but does not erase, his separation. Power stats are more environment-sensitive than plate discipline, so normalization compresses pure slugging gaps slightly.
Satirical aside:
If exit velocity had a stock ticker, Alonso would check it between pitches.
The style difference between him and Tucker leads to a lower overall value via bWAR.
Kyle Schwarber — Three True Outcomes, Fully Weaponized
Kyle Schwarber is Alonso’s statistical cousin in multivariate clustering. A true three-outcome hitter: home run, walk, or strikeout. A home run machine in the most literal statistical sense.
If most hitters build value through a mix of contact, power, and discipline, Schwarber builds it through two tools turned up to maximum volume.
Profile shape:
Average bWAR is low (3), as is ebWAR (3.89)
HR rate heavy, +1.31, the highest of the group in PCA.
Walk rate strong, +1.41, also the highest of the group.
BA suppressed, -1.61, lowest of the group, just hitting all the extremes.
In other words, Schwarber’s statistical fingerprint is almost cartoonishly polarized. He lives at the extremes of the rate distribution: elite power, elite patience, minimal batting average support.
That profile also explains his volatility. With a bWAR standard deviation of 2.05, Schwarber shows the second-highest year-to-year swing in the group. Players whose value comes from concentrated offensive events tend to oscillate more than balanced hitters. When the home runs land, the line looks explosive. When they don’t, the box score can feel empty.
PCA makes that shape very clear. While WAR and eWAR dominate the primary value axis, HR_rate and BB_rate define the secondary “style” dimension. Schwarber sits at the far pole of that dimension — essentially the statistical endpoint of the power–discipline archetype.
Satirical aside:
He does not believe in singles. Singles are rumors.
Era-adjusted metrics keep him in the same overall value neighborhood — just via a very different road.
Bo Bichette — The Wide Error Bar Star
Bo Bichette shows the highest three-year volatility in both bWAR and ebWAR in calculations.
That matters. High variance means larger performance swings, wider projection cone, and higher risk pricing, partially due to an injury-riddled 2024.
Averave bWAR of 2.33, and ebWAR of 3.26 (era-adjusted bWAR very kind to him)
Cluster placement puts him near balanced hitters, but the standard deviation flags uncertainty, at a bWAR SD of 2.64, easily the highest.
Bichette's power and patience is in the negatives in scaling, but his BA is second highest, after Bellinger. He is a contact hitter.
Bichette being younger and playing shortstop likely is part of why he got such a bag even with lower bWAR numbers
His cluster placement situates him near the “balanced hitters” ridge — his positive batting average tempers negative contributions from HR and walk rate. Standardized metrics show a mixed profile: negative power (-1.42 SD) and patience (-1.22 SD), but strong contact (+0.82 SD), giving him multi-channel upside rather than a single-stat dependency.
In short, Bichette is statistically positionless but production-rich, a hitter whose value is distributed across multiple dimensions but prone to volatility. Teams did not pay him for separation; they paid for upside distribution.
Satirical aside:
He’s not a blue-chip stock — he’s a growth stock with caffeine.
The Tucker Deal — Elite, Yes. Twice-As-Good? Not Even Close.
Kyle Tucker’s 4/$240M contract is not a mistake. It’s not reckless. It’s not fantasy baseball budgeting.
It is elite money for an elite player.
But when you run him through the same three-year, era-adjusted framework as the rest of this group, the gap shrinks in ways the contract gap does not.
Here are ebWAR numbers, normalized per 650 plate appearances:
Tucker: 6.20
Bregman: 5.20
Bellinger: 5.10
Alonso: 3.69
Schwarber: 3.62
Bichette: 3.81
That’s a one-WAR separation between Tucker and the next tier.
One.
Not double.
Not generational.
Not “unlockable character.”
Yes, going from 5 to 6 WAR is inherently harder than going from 0 to 1, so naturally teams will pay exponentially more. But this is beyond even that.
And here’s the twist: Tucker benefits the least from era adjustment.
Three-year ebWAR boost per 650 PA:
Schwarber: +0.87
Bellinger: +0.87
Bregman: +0.85
Alonso: +0.75
Bichette: +0.53
Tucker: +0.29
Under the hood, this matters.
Era adjustment works by evaluating performance relative to the distribution of talent in a given season, effectively asking: how rare is this performance shape in this competitive environment?
Tucker’s raw production already sits cleanly in that distribution. His component shifts are modest:
BA boost: +0.0199
HR rate compression: −0.0064
BB boost: +0.0087
The model doesn’t need to “fix” him.
The others move more because their shapes are either:
More balanced (Bregman, Bellinger), which gains credit under normalization
More concentrated (Schwarber, Alonso), which gains value once evaluated relative to how rare that damage profile is
Era adjustment compresses the cluster.
It doesn’t carve a canyon.
Satirical aside:
The contract says “final boss.”
The math says “top row of selectable characters.”
Stability, Projection, and the Certainty Tax
Where Tucker does separate meaningfully is variance.
Three-year bWAR standard deviations:
Tucker: 0.53
Bregman: 0.70
Alonso: 0.44
Bellinger: 1.56
Schwarber: 2.05
Bichette: 2.64
He combines:
Highest production level
Low volatility
Age advantage
Defensive value in the outfield
A clean projection curve
That cocktail is rare.
But rare does not mean twice as productive.
It means safer.
And markets pay for safety.
Front offices say they pay for the ceiling (Bichette). Based on Tucker’s contract, some of them pay for the floor.
Satirical aside:
This is less “buying the only spaceship” and more “paying extra for the spaceship with the best warranty.”
Why the Others Look Cheaper Under the Same Lens
Using the same era-adjusted, per-650 framework:
Bregman sits one WAR behind Tucker per 650 PA and gains nearly three times as much from normalization.
Bellinger is Tucker’s closest neighbor in multivariate distance space.
Alonso and Schwarber cluster differently stylistically but move significantly upward once adjusted.
Bichette is volatile, but not value-distant.
The spread in dollars is larger than the spread in adjusted production.
That’s not anti-Tucker.
That’s math.
One important note on what the era adjustment is actually doing here. The Full House Model doesn’t just rescale WAR arbitrarily; it evaluates performance relative to the competitive distribution of a given season and the estimated size of the underlying talent pool. In other words, value is not just “how many wins above replacement,” but how rare that performance is within that environment. A 4-WAR season in a compressed talent distribution doesn’t mean the same thing as a 4-WAR season in a deeper, broader pool. What’s striking in this group is that when you account for talent-pool context and distribution shape, Tucker’s separation narrows rather than widens. The model doesn’t discover a hidden tier above everyone else. It discovers a crowded top ridge.
The Dodgers Factor
And then there’s the team context.
The Dodgers operate with a ~$400M tax payroll and roughly a $530M total outlay, including penalties. They hold eight of the fifty highest AAV contracts in baseball. They employ the two highest AAV players in the league. Other big market teams, like the New York Yankees and Mets, and the Chicago Cubs, will spend, but not to this level.
This was not a farmer’s market negotiation.
It was an airport bidding war, and the Dodgers were the only team with lounge access.
When there’s no salary cap, eventually one franchise decides the marginal win is worth the marginal dollar.
And when you have that kind of leverage, paying a certainty tax is a luxury, not a mistake.
The Big Picture
Era adjustment compresses offensive separation.
PCA shows a crowded top ridge.
Similarity distance shows clustering, not hierarchy.
Stability explains premiums, but not doubling.
The market did not buy a king.
It bought the safest projection at the top of a tightly packed hill and paid extra to remove risk.
And if any of these hitters separate historically, it won’t be because the last three years demanded it.
It’ll be because baseball, like free agency, occasionally rewards the boldest spender in the room.
Or, more simply:
The numbers say “cluster.”
The contract says “confidence.”
And in Los Angeles, confidence has a luxury tax exemption.