Defensive statistics have always been difficult to interpret.
Batting statistics usually tell a fairly direct story. A hitter gets on base, hits for power, strikes out, walks, and produces runs. Pitching statistics are more complicated, but the broad questions are still familiar. Does the pitcher miss bats? Does he limit walks? Does he prevent runs?
Defense is different.
No single defensive statistic is universally accepted as definitive. Defensive Runs Saved, Outs Above Average, Fielding Run Value, FanGraphs Def, fielding percentage, and catcher throwing statistics all attempt to measure defense from slightly different directions. Sometimes they agree. Sometimes they disagree dramatically.
That makes team defense an ideal candidate for principal component analysis. Rather than deciding in advance which defensive statistic is “correct,” PCA lets the statistics reveal the dominant patterns in the data.
For this study, I used 2026 FanGraphs team defensive data through September 7. Six measures were included:
- Defensive Runs Saved
- Outs Above Average
- Fielding Run Value
- FanGraphs Def
- Fielding percentage
- Caught-stealing rate
I standardized each variable before performing the PCA. This is important because the statistics exist on very different numerical scales. Fielding percentage, for example, clusters around .980 to .990, while DRS can range from strongly negative to well over +100. Without standardization, a statistic’s scale could influence the PCA more than the information it contains.
The result was surprisingly clean. The first principal component explains 60.5 percent of all variation among MLB team defenses. Even more importantly, its meaning is easy to interpret.
The loadings on PC1 were:
\mathrm{Def} = 0.507 \mathrm{FRV} = 0.506 \mathrm{OAA} = 0.493 \mathrm{DRS} = 0.393 \mathrm{FP} = 0.297Caught-stealing rate contributed almost nothing to the first component.
In practical terms, PC1 acts as an overall defensive quality axis. That interpretation is reinforced by the extremely strong relationship between PC1 and FanGraphs Def. The correlation is about 0.97. So although PCA was not told what constituted “good defense,” it independently produced a first component that behaves almost exactly like a composite defensive-quality measure.
And one team separates itself immediately.
Chicago is not merely first; the Cubs are in a different neighborhood. Their PC1 score is 5.94, compared with 2.81 for second-place Arizona. No other team approaches Chicago’s position on the primary defensive axis. That distance is important. Rankings can sometimes exaggerate small differences. A team ranked first may be only marginally better than the team ranked second.
That is not what is happening here. The PCA suggests an enormous separation between the Cubs and everyone else.
Chicago entered September 8 with 107 Defensive Runs Saved, 65 Outs Above Average, and 64 Fielding Run Value in the data used here. Those are not merely good numbers. They represent broad agreement among different defensive measurement systems that Chicago has been exceptional.
The top ten teams by PC1 were:
| Rank | Team | PC1 |
|---|---|---|
| 1 | Chicago Cubs | 5.94 |
| 2 | Arizona | 2.81 |
| 3 | St. Louis | 2.01 |
| 4 | Toronto | 1.81 |
| 5 | San Diego | 1.68 |
| 6 | Los Angeles Dodgers | 1.62 |
| 7 | Atlanta | 1.61 |
| 8 | Boston | 1.33 |
| 9 | Kansas City | 1.18 |
| 10 | Cleveland | 0.84 |
Arizona emerges as the clear second-place team. The Diamondbacks’ defensive profile is particularly strong in the advanced range-based measures. They recorded 39 OAA and 31 FRV, producing a PC1 score substantially above most of the league.
St. Louis ranks third, followed by Toronto and San Diego. Cleveland comes in tenth. That is a respectable position, but the graph makes clear how far Chicago is from a good defensive team.
The opposite end of the PCA is equally interesting. Seattle ranks last with a PC1 score of -3.29. The Athletics are close behind at -3.02, followed by the Angels, Colorado, and Minnesota.
| Rank | Team | PC1 |
|---|---|---|
| 30 | Seattle | -3.29 |
| 29 | Athletics | -3.02 |
| 28 | Los Angeles Angels | -2.33 |
| 27 | Colorado | -2.13 |
| 26 | Minnesota | -1.93 |
| 25 | Pittsburgh | -1.82 |
| 24 | Cincinnati | -1.46 |
| 23 | San Francisco | -1.41 |
Seattle’s placement is driven heavily by -51 OAA and -44 FRV. Those numbers suggest a club that has struggled considerably to convert balls in play into outs relative to what would be expected.
But the lower portion of the rankings also reveals why using multiple defensive measurements is useful. Cincinnati, for example, had +1 OAA but -45 DRS. San Francisco showed almost the opposite disagreement, with +22 DRS but -17 OAA.
Which statistic should we trust? That is precisely the wrong question. Different defensive systems use different models, assumptions, opportunities, positioning adjustments, and definitions of responsibility. Disagreement among them is therefore not necessarily evidence that one system has failed.
It can also reveal uncertainty.
PCA helps by asking a different question: across all of these measurements, what common defensive signal appears most consistently? For most teams, that signal is PC1.
The second principal component tells a completely different story. PC2 explains another 17.3 percent of the total variance, bringing the first two principal components to approximately 77.9 percent of all defensive variation in the six original variables.
But PC2 is not another general-defense measure. It reflects the running game almost entirely.
The loading for caught-stealing rate on PC2 is approximately: 0.961. That is extraordinarily large.
The other variables contribute comparatively little. This means that the vertical axis in Figure 1 can essentially be interpreted as running-game control, while the horizontal axis measures broader defensive quality.
That makes several teams particularly interesting. San Diego ranks fifth overall defensively, but the Padres also sit very high on PC2. Kansas City shows a similar pattern. Their location on the graph suggests a defensive identity that differs from teams such as Arizona or Atlanta.
Those clubs may all be good defensively, but not in exactly the same way. And that is one of PCA’s greatest strengths. A simple ranking compresses every team into one number. The PCA retains structure.
Teams can be similar in overall quality while achieving that quality through different defensive profiles. However, there is an important methodological limitation.
DRS, OAA, FRV, and FanGraphs Def are not completely independent measurements. Several are derived from overlapping types of defensive information. OAA and FRV in particular are closely related, while FanGraphs Def incorporates modern defensive valuation into a broader positional framework.
Therefore, PC1 should not be interpreted as a completely new and independent defensive statistic. A better description would be a consensus advanced-defense index. That is still useful.
In fact, for this particular question, the overlap may be an advantage. If several different defensive systems all point in the same direction, PCA extracts that shared signal and gives it substantial weight.
Chicago is the clearest example. The Cubs do not rank first because one unusual statistic loves their defense. They rank first because virtually every major defensive measure agrees that their defense has been outstanding.
Seattle provides the mirror image. Several independent measurements likewise support the Mariners’ placement near the extreme negative end of PC1.
The most interesting cases may actually be the teams in between. Cincinnati and San Francisco show large disagreements among defensive systems. Pittsburgh has a positive DRS despite poor scores elsewhere. Philadelphia has relatively poor overall PC1 positioning while displaying a much stronger running-game score on PC2.
Those teams deserve additional investigation.
A natural next step also emerges.
Instead of using aggregate measures such as DRS, OAA, and Def, we can construct another PCA using the individual Fielding Run Value components: Throwing, Blocking, Framing, Arm, Range, Infield Double Plays, and First-Base Receiving.
That analysis would answer a different question. This PCA tells us who has been good and who has been bad. A component-level PCA could begin telling us why. For now, though, the broad picture is unusually clear.
Chicago has been the best defensive team in baseball through September 7, and not by a small margin. Arizona forms something of a second tier, followed by a cluster containing St. Louis, Toronto, San Diego, the Dodgers, Atlanta, and Boston.
At the other end, Seattle and the Athletics occupy the weakest part of the defensive landscape.
And perhaps most importantly, the analysis demonstrates why PCA is so useful in baseball analytics.
Defense does not have to be reduced to a debate over which statistic is best. Sometimes the better approach is to let the statistics vote.
