BASEBALL – Page 2 – Random Thoughts from a Nonlinear Mind

The Geography of Power: Home Runs by Primary Position

Introduction

Home runs are not evenly distributed across the diamond.

That is obvious at one level. First basemen and corner outfielders have historically been expected to hit for power. Middle infielders and catchers have often been evaluated based on a broader mix of defense, contact, arm strength, range, game-calling, and positional scarcity. Designated hitters exist almost entirely because of the bat.

But the obvious pattern is still worth measuring.

This chapter asks a simple question:

How have home runs historically been distributed by primary position?

To answer that, I built a player-season dataset from the Lahman-style batting and appearance files already used in this project. Each player-season was assigned to the position where the player appeared most often. Then I compared home run totals across primary positions.

The main version of the study uses regular player-seasons:

PA \geq 300

That cutoff matters because all player seasons include bench players, call-ups, pitchers, defensive replacements, and partial seasons. Those observations are part of baseball history, but they compress the distribution heavily toward zero. The 300-plate-appearance version gives a clearer view of regular players.

The result is consistent with baseball intuition, but the details are interesting.

Among regular player seasons, the highest median home run totals come from:

The lower median positions are:

Third base sits where we might expect it to sit: not quite a pure slugger position like first base, right field, left field, or designated hitter, but clearly more power-oriented than second base or shortstop.

That makes third base a bridge position. It carries defensive responsibility, but it has also historically demanded more power than the middle infield.

Data and Method

The unit of analysis is the player-season.

For each player-season, I summed the player’s batting record across all stints. Home runs came from the batting file:

HR_{i,y} = \sum_{t} HR_{i,y,t}

Where:

i = \text{player}

y = \text{season}

t = \text{team or stint}

Plate appearances were estimated from the available batting columns as:

PA = AB + BB + HBP + SF + SH

This is the same practical plate-appearance construction used elsewhere in the project.

Each player-season was then assigned a primary position using the appearances file. The primary position was the position at which the player appeared in the most games:

\mathrm{PrimaryPosition}_{i,y} = \operatorname*{arg\,max}_{p} \left( G_{i,y,p} \right)

Where:

G_{i,y,p} = \text{games played by player } i \text{ at position } p \text{ in season } y

The positions included were:

For the main batting-position figures, I excluded pitchers because pitcher seasons have a very different distribution and can compress the plot.

The main regular-player sample uses:

PA \geq 300

This produced the following number of regular player-seasons by batting position:

C: 2,650

1B: 3,273

2B: 3,273

3B: 3,213

SS: 3,149

LF: 3,262

CF: 3,331

RF: 3,259

DH: 646

The designated hitter sample is smaller because the position did not exist across the full historical record.

Why Use Box Plots?

Home run totals are skewed.

Most players do not hit 40 home runs. Many regulars hit fewer than 10. A smaller number of players produce the spectacular seasons that shape memory and record books.

A box plot is useful because it shows several parts of the distribution at once:

median

middle 50 percent

upper and lower spread

outlier seasons

The median is the central line in the box.

The box itself shows the interquartile range:

IQR = Q_3 - Q_1

Where:

Q_1 = \text{25th percentile}

Q_3 = \text{75th percentile}

The outliers show the extreme home run seasons. Those outliers matter because home run history is partly a history of extremes.

The box plot, therefore, helps separate typical power from exceptional power.

Figure 1: Regular Player-Seasons by Primary Position

Figure 1. Home runs by primary position, regular player-seasons.

The first figure is the main result.

It includes batting positions only and uses the regular-player cutoff:

PA \geq 300

The median home run totals are:

C: 7

1B: 12

2B: 5

3B: 9

SS: 4

LF: 10

CF: 7

RF: 11

DH: 18

The pattern is clear.

Designated hitters have the highest median:

\widetilde{HR}_{DH} = 18

First basemen follow:

\widetilde{HR}_{1B} = 12

Right fielders and left fielders are next:

\widetilde{HR}_{RF} = 11

\widetilde{HR}_{LF} = 10

Third basemen sit just behind the corner outfielders:

\widetilde{HR}_{3B} = 9

The middle infield positions are lower:

\widetilde{HR}_{2B} = 5

\widetilde{HR}_{SS} = 4

This is the historical power spectrum in compact form.

The right side of the defensive spectrum contains more home runs. The middle of the diamond contains fewer.

The Third-Base Position in Context

The third-base result is especially relevant to the larger project.

Third base has a median of 9 home runs among regular player-seasons:

\widetilde{HR}_{3B} = 9

Its mean is:

\overline{HR}_{3B} = 11.24

Its 90th percentile is:

P_{90,3B} = 26

Its 95th percentile is:

P_{95,3B} = 32

That means a 26-home-run season by a regular third baseman sits around the 90th percentile historically, while a 32-home-run season sits around the 95th percentile.

This helps frame the third-base offensive studies. A third baseman does not need to hit like a first baseman or designated hitter to be power-relevant at the position. But third base has historically demanded more power than second base or shortstop.

In that sense, third base is not a pure defensive position and not a pure slugger position. It sits between worlds.

Summary Table: Regular Player-Seasons

The main regular-player summary is:

Position Mean HR Median HR 90th Percentile Max
C 9.20 7 20 60
1B 14.68 12 31 70
2B 6.89 5 16 45
3B 11.24 9 26 54
SS 6.66 4 17 57
LF 12.47 10 27 73
CF 10.07 7 24 62
RF 13.19 11 28 66
DH 19.48 18 33 56

The mean is higher than the median at every position. That reflects the right-skewed nature of home run totals:

\overline{HR}_p > \widetilde{HR}_p

Where:

\overline{HR}_p = \text{mean home runs at position } p

\widetilde{HR}_p = \text{median home runs at position } p

This skew is exactly why box plots are useful. The median shows the typical regular. The outliers show the historical power peaks.

Figure 2: All Player-Seasons

Figure 2. Home runs by primary position, all player-seasons.

The second figure includes all player-seasons, regardless of plate appearances.

This plot answers a different question.

Instead of asking what regular players do, it asks what the full population of player-seasons looks like.

The result is much more compressed toward zero.

That is expected.

All player-seasons include players who appeared briefly, bench players, injury-shortened seasons, late-season call-ups, defensive specialists, and players with very few batting opportunities. Many of these seasons have few or no home runs.

This is why the regular-player cutoff is important.

The all-player plot is useful because it shows the structure of the full record. But the regular-player plot is better for comparing positional expectations.

The difference between Figure 1 and Figure 2 is a methodological lesson:

Home run distributions depend heavily on playing-time filters.

That is not a flaw. It is part of the phenomenon.

Figure 3: Including Pitchers

Figure 3. Home runs by primary position, regular player-seasons including pitchers.

The third figure adds pitchers to the regular-player sample.

There are only 49 pitcher seasons with at least 300 estimated plate appearances in this dataset. That is a tiny sample compared with the defensive positions.

Pitchers have a very low home run distribution:

P_n = 49

The pitcher box is compressed near the bottom of the plot.

This is why pitchers are excluded from the main batting-position comparison. Pitchers are not just another defensive position in this context. Historically, they had a very different offensive role.

Including them is still useful as a reminder of how specialized baseball’s offensive expectations have become.

The pitcher comparison also shows why the designated hitter changed the structure of the game. The DH role separated the hitting function from the pitching function.

Figure 4: Before 1994

Figure 4. Home runs by primary position, regular player-seasons before 1994.

The fourth figure examines regular player seasons before 1994.

This view helps separate the long historical baseline from the more recent high-power period.

Before 1994, the positional pattern is still visible:

1B, LF, RF, and DH are higher-power positions.

2B and SS are lower-power positions.

3B sits in the middle-to-upper range.

However, the distributions are generally lower than in the modern period.

That is especially visible in the medians and upper tails. The tops of the boxes and whiskers are lower for most positions.

This matters because the modern home run environment can distort our intuition. If we only look at recent baseball, we may overestimate what a typical power season historically looked like.

The pre-1994 plot keeps the longer history visible.

Figure 5: 1994–2025

Figure 5. Home runs by primary position, regular player-seasons, 1994–2025.

The fifth figure focuses on the modern high-power period from 1994 through 2025.

Here the distributions shift upward.

The median home run totals are visibly higher at most positions than in the pre-1994 plot.

This is particularly clear at positions that historically carried lower power expectations. Second base, shortstop, catcher, and center field all show more power in the modern period than in the older baseline.

The modern plot also shows how the distinction between positions has narrowed in some ways. Shortstops and second basemen now have more home run upside than they did historically, though they still remain below first base and designated hitter in the central distribution.

Third base remains a power-relevant position. In the 1994–2025 period, the third-base distribution shifts upward, reflecting the broader increase in home run production across baseball.

Maximum Home Run Seasons by Primary Position

The maximum regular-player home run seasons by primary position are:

C: Cal Raleigh, 2025, 60 HR

1B: Mark McGwire, 1998, 70 HR

2B: Marcus Semien, 2021, 45 HR

3B: Alex Rodriguez, 2007, 54 HR

SS: Alex Rodriguez, 2002, 57 HR

LF: Barry Bonds, 2001, 73 HR

CF: Aaron Judge, 2022, 62 HR

RF: Sammy Sosa, 1998, 66 HR

DH: Kyle Schwarber, 2025, 56 HR

The maximum values show how different the upper tail can be from the median.

For third base:

\max(HR_{3B}) = 54

The median regular third-base season is:

\widetilde{HR}_{3B} = 9

So the highest third-base season is six times the median:

\frac{ \max(\mathrm{HR}_{\mathrm{3B}}) }{ \widetilde{\mathrm{HR}}_{\mathrm{3B}} } = \frac{54}{9} = 6

This is the shape of home run history. The typical distribution matters, but the record book is made in the tails.

Positional Power Spectrum

The regular-player medians produce a rough power spectrum:

DH > 1B > RF > LF > 3B > C/CF > 2B > SS

This is not a law. It is an empirical summary of the historical data.

It shows that home run expectations follow the defensive spectrum, but imperfectly.

First base and corner outfield positions carry major power expectations. Middle infield positions carry lower typical power expectations. Third base sits between them.

That makes sense. Third base is a reaction position, an arm-strength position, and historically a position where teams have often accepted more offensive responsibility than at shortstop or second base.

Why Third Base Is Interesting

This chapter began as a positional home run study, but it also helps explain why third base is such a useful position for the larger project.

Third base is not first base. It is not a position where the bat alone defines most of the historical expectation.

But it is also not shortstop or second base. The position has carried real offensive expectations for a long time.

This makes third base analytically rich.

A third baseman can be great through power.
A third baseman can be great through defense.
A third baseman can be great through a two-way profile.
A third baseman can be average in one dimension and exceptional in another.

That is why the earlier z-score, WAR, and wRC+ studies were useful. Third base demands a multidimensional approach.

The home run box plots confirm the same idea from a different angle.

Third base lives in the middle of the power spectrum.

Limitations

This study uses primary position by games played. That is practical and transparent, but it simplifies players who split time across multiple positions.

A player-season is assigned to only one position, even if the player spent substantial time elsewhere.

The plate appearance formula is estimated from available batting columns:

PA = AB + BB + HBP + SF + SH

That is a reasonable construction, but it may differ slightly from official plate appearance totals in some historical contexts.

The 300-PA cutoff is also a choice. A different cutoff, such as 250 or 400 PA, would change the sample slightly. The broad pattern should remain, but the exact medians and percentiles would move.

Finally, the designated hitter is not available across the full historical period. It should be interpreted separately from long-running defensive positions.

Conclusion

Home runs have never been distributed evenly across the diamond.

The regular-player box plots show a clear positional structure. Designated hitters, first basemen, right fielders, left fielders, and third basemen occupy the higher-power part of the spectrum. Second basemen and shortstops occupy the lower-power part. Catchers and center fielders sit in between, with major outlier seasons but lower typical medians.

For regular player-seasons:

DH: 18 median HR

1B: 12 median HR

RF: 11 median HR

LF: 10 median HR

3B: 9 median HR

C: 7 median HR

CF: 7 median HR

2B: 5 median HR

SS: 4 median HR

Third base sits in a revealing place.

It is not the highest-power position. It is not the lowest. It is a position where power matters, but not alone.

That is why the position works so well for this larger study.

Third base is a bridge between offensive and defensive expectations.

The home run distributions show that clearly.

Postscript: Where the Home Runs Came From

The box plots in this chapter show the distribution of home run seasons by position. They answer a question about typical and exceptional seasons:

What does a home run season usually look like at each position?

But there is another question worth asking:

Where did the historical home run volume come from?

To answer that, I summed all home runs by primary position.

\mathrm{TotalHR}_{p} = \sum_{i,y} HR_{i,y,p}

Where:

p = \text{primary position}

The first postscript figure uses all player-seasons from 1871 through 2025.

Postscript Figure 1. Total home runs by primary position, all player-seasons, 1871–2025.

The largest total comes from first base:

\mathrm{TotalHR}_{1B} = 56{,}082

Right field and left field follow closely:

\mathrm{TotalHR}_{RF} = 50{,}858

\mathrm{TotalHR}_{LF} = 49{,}898

Third base ranks fourth:

\mathrm{TotalHR}_{3B} = 42{,}140

This reinforces the larger pattern. The historical home run supply has come disproportionately from the corners: first base, corner outfield, and third base.

The second postscript figure uses only regular player-seasons:

PA \geq 300

Postscript Figure 2. Total home runs by primary position, regular player-seasons, PA ≥ 300, 1871–2025.

The regular-player version tells the same basic story. First base remains first:

\mathrm{TotalHR}_{1B} = 48{,}064

Right field remains second:

\mathrm{TotalHR}_{RF} = 42{,}977

Left field remains third:

\mathrm{TotalHR}_{LF} = 40{,}663

Third base remains fourth:

\mathrm{TotalHR}_{3B} = 36{,}127

The designated hitter is the important caution. DH has the highest median home run total among regular player-seasons, but it does not rank near the top in total historical home runs because it is not present across the full 1871–2025 record.

Keep that distinction in mind.

The box plots show rate and distribution.
The bar charts show accumulated historical volume.

They are related, but they are not the same thing.

A position can have a high typical power profile but a smaller historical total if it has fewer seasons in the record. That is exactly what happens with designated hitters.

Third base, by contrast, has both a long historical presence and a meaningful power profile. It does not lead the home run record, but it sits clearly among the power-producing positions.

That is the key postscript result:

First base produced the most historical home run volume.

Corner outfield followed.

Third base remained a major source of home runs.

Designated hitter had the strongest power profile but a shorter historical runway.

Three Measures of Third-Base Greatness: Z-Scores, WAR, and wRC+

Introduction

At this point in the third-base study, we have three different ways of measuring greatness.

The first is our own z-score framework. It asks how far a player separated from other third basemen in the same season and across a career.

The second is WAR. It asks how much total value a player produced, including offense, defense, baserunning, position, replacement level, and playing time.

The third is wRC+. It asks how strong a hitter was after adjusting for league and park context, with 100 set as league average.

Each measure is useful.

Each measure answers a different question.

That is why this comparison matters.

A player can dominate by z-score because he separates from his third-base peers. A player can dominate by WAR because he accumulates value across many seasons. A player can dominate by wRC+ because his offensive rate quality is extraordinary.

The goal of this chapter is not to declare that one metric is correct and the others are wrong.

The goal is to compare the stories they tell.

For Study 1, I focused on regular third basemen with at least five qualified third-base seasons and matched values for career WAR, career wRC+, and our career z-score measures. That produced a working sample of 239 third basemen.

The central question is:

Which third basemen remain elite when judged by z-scores, WAR, and wRC+ together?

The answer begins with Mike Schmidt.

Across the three-metric composite, Schmidt is the clear anchor of the study. He ranks first in combined z-score, first in WAR, and second in wRC+. He is the player who survives every test.

But the rest of the list is more interesting than a simple ranking.

Eddie Mathews, Chipper Jones, Wade Boggs, George Brett, Home Run Baker, Alex Rodriguez, Ron Santo, Scott Rolen, and Jose Ramirez also emerge as strong cross-metric performers.

At the same time, several players reveal the tension between the metrics.

Brooks Robinson ranks extremely high by combined z-score and WAR, but much lower by wRC+. Dick Allen ranks first by wRC+, but much lower by combined z-score and WAR. Adrian Beltre ranks third by WAR but much lower by wRC+.

Those differences are not problems.

They are the point of the chapter.

The Three Metrics

This study compares three broad dimensions: Combined career z-score, career WAR, and career wRC+.

The combined z-score is internal to this project. WAR and wRC+ are external validation measures.

The three metrics are not interchangeable.

They measure different things.

The Combined Z-Score

The combined z-score is based on two career components: Model C offensive career score and Traditional defensive career score.

Each career score is standardized across the third-base regular sample.

The standardized offensive score is:

z_{\mathrm{Offense},i} = \frac{ \mathrm{Offense}_{i} - \overline{\mathrm{Offense}} }{ s_{\mathrm{Offense}} }

The standardized defensive score is:

z_{\mathrm{Defense},i} = \frac{ \mathrm{Defense}_{i} - \overline{\mathrm{Defense}} }{ s_{\mathrm{Defense}} }

The combined z-score is:

\mathrm{Combined\ Z}_{i} = z_{\mathrm{Offense},i} + z_{\mathrm{Defense},i}

This score rewards players who separate from other third basemen in both offensive and traditional defensive dimensions.

It is not the same thing as WAR.

It does not directly assign run values. It does not use replacement level. It does not use park factors in the way WAR or wRC+ does. It is a peer-separation measure.

That is its strength.

It asks:

How far did this third baseman stand from the position?

WAR

WAR is a broader value metric.

In this chapter, WAR is used as a career value measure for the player’s qualified third-base seasons in our merged dataset. WAR includes offense, defense, baserunning, positional value, replacement value, and playing time.

For the purpose of this chapter, we can think of WAR abstractly as:

\begin{aligned} \mathrm{WAR}_i &= \mathrm{Offense}_i + \mathrm{Defense}_i + \mathrm{Baserunning}_i \\ &\quad+ \mathrm{Position}_i + \mathrm{Replacement}_i \end{aligned}

That is not intended as a full WAR formula. It is a conceptual summary.

WAR asks a different question from the z-score model.

It asks:

How much total value did this player produce?

That is why Adrian Beltre, Wade Boggs, Brooks Robinson, Scott Rolen, Graig Nettles, and Buddy Bell can look stronger by WAR than they do by wRC+ alone.

WAR values more than hitting.

wRC+

wRC+ is an offensive rate measure.

It is scaled so that 100 is league average:

wRC^+ = 100

A hitter with a 120 wRC+ is roughly 20 percent better than league average offensively:

wRC^+ = 120

A hitter with an 80 wRC+ is roughly 20 percent below league average offensively:

wRC^+ = 80

For this study, wRC+ answers a narrower question:

How good was the hitter?

It does not measure third-base defense. It does not measure total career value. It does not reward playing third base well. It isolates offensive rate quality.

That is why Dick Allen can rank first in wRC+ without ranking first in the other systems.

Why a Composite Score Is Useful

Because the three measures use different scales, we cannot simply add raw z-score, WAR, and wRC+.

Instead, I converted each metric into a percentile rank.

For each player:

P_{\mathrm{Combined\ Z},i} = \mathrm{PercentileRank}(\mathrm{Combined\ Z}_i)

P_{\mathrm{WAR},i} = \mathrm{PercentileRank}(\mathrm{WAR}_i)

P_{wRC^+,i} = \mathrm{PercentileRank}(wRC^+_i)

Then I calculated a three-metric composite percentile:

\mathrm{Composite}_{i} = \frac{ P_{\mathrm{Combined\ Z},i} + P_{\mathrm{WAR},i} + P_{wRC^+,i} }{3}

Higher values indicate players who rank well across all three systems.

This composite is not meant to replace the individual metrics. It is a summary tool.

It rewards broad agreement.

A player who ranks high in all three metrics will rise. A player who is exceptional in one metric but weaker in the others will still be visible, but not necessarily at the top of the composite.

That is why this study is useful.

It separates all-around consensus from metric-specific greatness.

Figure 1: The Top 25 Composite Performers

Figure 1. Top third basemen across combined z-score, WAR, and wRC+.

The top 25 composite chart gives the broadest view of the results.

The top ten are:1. Mike Schmidt 2. Eddie Mathews 3. Chipper Jones 4. Wade Boggs 5. George Brett 6. Home Run Baker 7. Alex Rodriguez 8. Ron Santo 9. Scott Rolen 10. Jose Ramirez.

It is not simply an offensive list. Brooks Robinson does not reach the top ten because wRC+ pulls him down, but the list still includes two-way and value-based players such as Wade Boggs, Ron Santo, and Scott Rolen.

It is not simply a WAR list either. Adrian Beltre ranks third by WAR, but thirteenth by the composite because his wRC+ rank is lower than his WAR and z-score ranks.

It is not simply a wRC+ list. Dick Allen ranks first by wRC+, but he does not land near the top of the composite because his combined z-score and WAR ranks are lower.

The top composite list rewards players who remain strong across the different definitions of greatness.

That is why Schmidt is first.

He is not merely great by one method. He is great by all three.

The Top Players by Each Metric

The top players change depending on the question.

By combined z-score, the top five are: 1. Mike Schmidt 2. Brooks Robinson 3. Nolan Arenado 4. Scott Rolen 5. Wade Boggs.

This list rewards two-dimensional separation. Robinson and Arenado rise because traditional defense is included.

By WAR, the top five are: 1. Mike Schmidt 2. Eddie Mathews 3. Adrian Beltre 4. Wade Boggs 5. Brooks Robinson.

This list rewards total value and career accumulation.

By wRC+, the top five are: 1. Dick Allen 2. Mike Schmidt 3. Eddie Mathews 4. Harmon Killebrew 5. John McGraw.

This list rewards offensive rate quality.

These are three different lists because they are answering three different questions.

The question is not which list is correct.

The question is what each list reveals.

Figure 2: Rank Movement Across the Three Systems

Figure 2. How the top composite third basemen rank by combined z-score, WAR, and wRC+.

The rank-comparison figure shows how players move across the three measures.

Mike Schmidt barely moves. That is the signature of a consensus number one. His profile is not dependent on one definition of value.

Eddie Mathews is similarly strong. He ranks high in WAR and wRC+, and still remains strong in the combined z-score system.

Chipper Jones is also stable. His defensive score is not strong, but his offensive value is so high that he remains near the top.

The movement becomes more interesting with players like Adrian Beltre, Nolan Arenado, and Scott Rolen.

Beltre ranks extremely high by WAR but much lower by wRC+. That makes sense. His case is not purely about offensive rate. It is about durability, defense, and total value.

Arenado ranks very high by combined z-score but much lower by wRC+. Again, that makes sense. His profile is two-dimensional and defense-forward.

Rolen is a balanced case. He ranks very high by combined z-score and WAR but lower by wRC+. That reflects his two-way value.

This figure shows that the metrics are not redundant.

They overlap, but they do not tell the same story.

Figure 3: Combined Z-Score Versus WAR

Figure 3. Combined career z-score versus career WAR among third-base regulars.

The combined z-score and WAR relationship is strong.

The fitted line is:

\mathrm{WAR} = 15.98 + 6.69(\mathrm{Combined\ Z})

The model fit is:

R^2 = 0.782

That means the combined z-score explains a large share of the variation in career WAR among third-base regulars.

This is important.

It tells us that our z-score framework is not just an internal ranking system. It aligns strongly with a major external value metric.

But the scatterplot also shows meaningful differences.

Mike Schmidt sits at the upper-right extreme. His combined z-score and WAR both identify him as historically exceptional.

Brooks Robinson sits high in combined z-score and WAR, but his shape is different. His combined z-score is powered by traditional defense rather than offensive dominance.

Adrian Beltre sits higher in WAR than his combined z-score alone would predict. That suggests his total value, longevity, and broader WAR components are stronger than the simplified z-score model fully captures.

Nolan Arenado sits high in combined z-score but lower in WAR relative to the line. That may reflect career length, active-career status, or differences between traditional defensive separation and WAR’s defensive valuation.

The relationship is strong, but the residuals still matter.

They show where the systems disagree.

Figure 4: Offensive Z-Score Rate Versus wRC+

Figure 4. Average offensive z-score per qualified third-base season versus career wRC+.

The relationship between average offensive z-score and wRC+ is also strong.

The fitted line is:

wRC^+ = 100.89 + 5.41(\mathrm{Average\ Offensive\ Z})

The fit is:

R^2 = 0.740

This confirms the earlier wRC+ validation result.

Average offensive z-score is a strong predictor of wRC+ because both are measuring offensive quality, though in different ways.

The equation says that each additional point of average offensive z-score corresponds to about 5.41 additional points of career wRC+:

\beta_1 = 5.41

This is why the offensive names rise in this figure.

Dick Allen, Mike Schmidt, Eddie Mathews, Chipper Jones, Alex Rodriguez, George Brett, Home Run Baker, Wade Boggs, Al Rosen, and David Wright all appear as strong offensive profiles.

Brooks Robinson, by contrast, is much closer to the middle of the wRC+ distribution. That is not a criticism. It simply reflects that Robinson’s greatness is not primarily a wRC+ case.

That is exactly why this comparison matters.

Figure 5: Cross-Metric Rank Disagreements

Figure 5. Largest cross-metric rank disagreements among notable third basemen.

The disagreement chart is one of the most useful figures in the study.

It identifies players whose rankings differ sharply across combined z-score, WAR, and wRC+.

The rank spread is:

\begin{aligned} \mathrm{RankSpread}_{i} &= \max\left( r_{\mathrm{CombinedZ},i}, r_{\mathrm{WAR},i}, r_{\mathrm{wRC}^{+},i} \right) \\ &\quad- \min\left( r_{\mathrm{CombinedZ},i}, r_{\mathrm{WAR},i}, r_{\mathrm{wRC}^{+},i} \right) \end{aligned}

A large spread means the player looks very different depending on the metric.

Some of the most interesting disagreement cases are: Brooks Robinson, Dick Allen, Adrian Beltre, Nolan Arenado, Willie Kamm, Gary Gaetti, Harmon Killebrew, Edwin Encarnacion, Jim Ray Hart, and Deacon White.

These players are not mistakes in the data.

They are interpretive opportunities.

Brooks Robinson is a defensive and WAR giant, but not a wRC+ giant.

Dick Allen is an offensive-rate giant, but not a top combined z-score or WAR third-base regular in this framework.

Adrian Beltre is a WAR giant, but wRC+ does not fully capture his case.

Willie Kamm is extremely strong by the combined z-score framework because of traditional defense, but he is not similarly high by wRC+.

Edwin Encarnacion is much stronger by wRC+ than by third-base z-score or WAR within the third-base framework, partly because his career offensive identity extends beyond a long regular third-base profile.

The disagreement chart shows why a single number is not enough.

The Schmidt Result

Mike Schmidt is the central result of Study 1.

He ranks: Combined z-score rank: 1 WAR rank: 1 wRC+ rank: 2 Composite rank: 1

This is almost the perfect cross-metric profile.

Schmidt is not merely the best by our internal model. He is also the best by WAR and nearly the best by wRC+.

That matters because it means his result is robust.

He is not a product of one method.

He is the player who remains elite when the question changes.

If the question is peer separation, Schmidt wins.

If the question is total value, Schmidt wins.

If the question is offensive rate quality, Schmidt is still almost at the top.

That is the strongest possible case.

Eddie Mathews, Chipper Jones, and the Offensive Greatness Group

Eddie Mathews ranks second by the composite.

He ranks: Combined z-score rank: 6 WAR rank: 2 wRC+ rank: 3 Composite rank: 2.

That is a very strong cross-metric profile. Mathews does not have Schmidt’s complete separation, but he remains elite everywhere.

Chipper Jones ranks third by the composite: Combined z-score rank: 8 WAR rank: 6 wRC+ rank: 6 Composite rank: 3.

Chipper’s case is offense-forward. His traditional defensive component is not strong, but his offensive quality is so high that he remains elite across the systems.

George Brett and Home Run Baker also belong in this broad offensive greatness group. They are strong by wRC+, strong by WAR, and strong enough by combined z-score to remain near the top.

This group shows that offensive greatness can carry a third-base profile a long way.

Wade Boggs and the On-Base Profile

Wade Boggs ranks fourth by the composite: Combined z-score rank: 5 WAR rank: 4 wRC+ rank: 14 Composite rank: 4.

Boggs is a fascinating case because he is not a home-run power archetype. His greatness is built around contact, on-base skill, batting average, plate discipline, and sustained offensive quality.

The fact that he ranks so highly in the composite confirms that the model is not simply rewarding slugging power.

Boggs was a different kind of offensive star, and the metrics recognize it.

Rolen, Beltre, Arenado, and Two-Way Value

Scott Rolen, Adrian Beltre, and Nolan Arenado show why WAR and combined z-score are necessary companions to wRC+.

Rolen ranks: Combined z-score rank: 4 WAR rank: 10 wRC+ rank: 34 Composite rank: 9.

Beltre ranks: Combined z-score rank: 9 WAR rank: 3 wRC+ rank: 58 Composite rank: 13.

Arenado ranks: Combined z-score rank: 3 WAR rank: 15 wRC+ rank: 57 Composite rank: 15.

These are not weak wRC+ players. But their all-time third-base cases are not primarily wRC+ cases.

They are two-way cases.

Rolen is balanced. Beltre is a total-value and longevity case. Arenado is a defense-forward combined z-score case.

If this study used only wRC+, these players would be underrated.

If it used only WAR, their offensive shape would be less visible.

If it used only z-score, the relationship to broader value would be less clear.

The three-metric comparison gives the fuller picture.

Brooks Robinson and the Limits of wRC+

Brooks Robinson is the clearest example of a player whose greatness is not offensive-rate greatness.

He ranks: Combined z-score rank: 2 WAR rank: 5 wRC+ rank: 114.

That is a huge split.

It makes perfect sense.

Robinson’s historical case is not based on being one of the greatest offensive third basemen. It is based on defense, durability, and total value.

The combined z-score model sees him because traditional defense is included. WAR sees him because total value includes defense. wRC+ does not see him in the same way because wRC+ is an offensive metric.

That is not a flaw in wRC+. It is a reminder that wRC+ answers a narrower question.

Dick Allen and the Limits of Third-Base Accumulation

Dick Allen is the opposite case.

He ranks: wRC+ rank: 1 Combined z-score rank: 87 WAR rank: 51.

Allen’s offensive rate quality is extraordinary. But within this third-base regular framework, he does not accumulate the same kind of third-base-specific z-score or WAR profile as Schmidt, Mathews, Chipper, Boggs, or Brett.

This shows the difference between a great hitter who played third base and a great third baseman across the entire profile. That is an important distinction.

Allen is not diminished by this result. The study simply clarifies what kind of greatness he represents.

He is a wRC+ giant. He is not the top all-around third-base regular by the three-metric composite.

Why This Study Is Interesting

The value of Study 1 is that it prevents the project from becoming metric-dependent.

If Schmidt ranked first only by our z-score model, the conclusion would be interesting but narrower.

But Schmidt also ranks first by WAR and second by wRC+. That makes the conclusion much stronger.

At the same time, the disagreements prevent the chapter from becoming too simple.

Brooks Robinson, Dick Allen, Adrian Beltre, Nolan Arenado, Rolen, Boggs, and others show that greatness has different forms.

The study therefore supports two conclusions at once: 1. Mike Schmidt is the clearest cross-metric third-base anchor. 2. Different metrics reveal different kinds of third-base greatness.

Both points are important.

Limitations

This chapter uses regular third basemen with at least five qualified third-base seasons and matched values across the three systems. That makes the comparison cleaner, but it also means the study is focused on third-base regulars, not every player who ever appeared at third base.

The combined z-score uses this project’s offensive Model C and traditional defensive model. It does not include modern defensive metrics, park factors, or full run-value modeling.

WAR includes many components that the z-score model does not.

wRC+ is a rate statistic and should not be treated as an accumulated career value measure. That is why the study includes average offensive z-score per qualified season when comparing to wRC+.

The composite percentile score is a summary tool. It is not a new definitive metric. It is best used to identify players who remain strong across multiple systems.

Conclusion

Study 1 compares three ways of measuring third-base greatness: Combined z-score, WAR, and wRC+

The main result is clear.

Mike Schmidt is the strongest cross-metric third baseman in the study.

He ranks first by combined z-score, first by WAR, second by wRC+, and first by the three-metric composite.

Eddie Mathews, Chipper Jones, Wade Boggs, George Brett, Home Run Baker, Alex Rodriguez, Ron Santo, Scott Rolen, and Jose Ramirez also emerge as strong cross-metric performers.

But the disagreements are just as important.

Brooks Robinson shows that wRC+ cannot capture defensive greatness.

Dick Allen shows that offensive rate greatness is not the same as all-around third-base accumulation.

Adrian Beltre, Nolan Arenado, and Scott Rolen show the importance of two-way value.

The larger conclusion is this:

Third-base greatness is not one-dimensional.

Z-scores show peer separation.
WAR shows total value.
wRC+ shows offensive quality.

The best third basemen are the ones who remain visible when the lens changes.

By that standard, Mike Schmidt stands at the center of the argument.

The Shape of a Pitching Staff: A Dendrogram of MLB Team Pitching, 2001- 2025

The earlier chapter asked a ranking question: which MLB organizations built the best pitching staffs from 2001 to 2025?

This post asks a slightly different question.

Which teams pitched alike?

That is not the same thing. Two teams can both be good without being similar. One team might dominate through strikeouts and fielding-independent indicators. Another might prevent runs through contact control, ground balls, park fit, or bullpen management. A third might have a mixed profile, with decent run prevention but weaker underlying indicators. Ranking tells us who was best. Clustering tells us who had the same shape.

That is why a dendrogram is useful. It does not begin with a leaderboard. It begins with resemblance.

The question becomes: if we describe each franchise by its long-term pitching profile, which franchises naturally group together?

The method

Each franchise was described using season-normalized pitching variables from 2001 through 2025. That step is important because pitching environments changed dramatically during this period. A 4.00 ERA in 2001 does not mean exactly the same thing as a 4.00 ERA in 2025.

So each team-season was first compared to its own season.

z_{i,y,m} = \frac{ X_{i,y,m} - \overline{X}_{y,m} }{ s_{y,m} }

Here, $(X_{i,y,m})$ is team (i)’s value for metric (m) in season (y), $(\bar{X}_{y,m})$ is the league average for that metric in that season, and $(s_{ym})$ is the season standard deviation.

For lower-is-better metrics, such as ERA-, FIP-, xFIP-, SIERA, BB%, HR/9, HR/FB, and Hard%, I reversed the sign so that higher values always mean a more favorable pitching profile.

The clustering used these long-term franchise traits:

Category	Variables
Value	WAR
Dominance	K-BB%
Command	BB% prevention
Run prevention	ERA-
Fielding-independent skill	FIP-, xFIP-, SIERA
Home-run control	HR/9, HR/FB
Contact profile	GB%, Hard% prevention
Starter usage	Quality-start rate

After calculating each franchise’s average profile, I standardized the franchise-level variables and used Ward hierarchical clustering. The distance between teams is based on how far apart their standardized pitching profiles are.

d(i,j) = \sqrt{ \sum_{m=1}^{p} \left( z_{i,m} - z_{j,m} \right)^2 }

The dendrogram then links the most similar teams first and gradually joins them into larger groups.

Figure 1. MLB team pitching identity dendrogram, 2001-2025

The first thing to notice is that the dendrogram is not only a quality ranking. It does separate many of the best pitching organizations, but it also captures style.

The Dodgers, Yankees, Astros, Guardians, Phillies, Cubs, and Red Sox form a major cluster. That makes sense. These organizations score well across the strongest modern pitching indicators: WAR, K-BB%, FIP-, xFIP-, and SIERA. This is the elite skill-and-value cluster.

But the Braves, Giants, and Cardinals form a different group. They are not grouped with the Dodgers and Yankees, even though they include strong pitching organizations. Their similarities lie more in run prevention, home-run suppression, ground-ball tendency, and contact management. In other words, their profile is not merely “good pitching.” It is a particular kind of good pitching.

The Rays, Brewers, Padres, Blue Jays, and Diamondbacks form another interesting group. This cluster is more modern and peripheral-driven. These teams tend to show some strength in strikeout-minus-walk skill and xFIP/SIERA-style indicators, but they are not as dominant in long-term value or run prevention as the elite group.

At the other end, the Rockies, Royals, Orioles, Reds, and Rangers group together as long-term struggling pitching profiles. That does not mean each franchise was bad every year. It means that across the full 2001-2025 window, their average profile shares several weaknesses: lower WAR, weaker K-BB%, weaker fielding-independent indicators, and poorer home-run prevention.

The six main clusters

The dendrogram produced six useful interpretive groups:

Cluster	Teams	Interpretation
Elite skill and value staffs	BOS, CHC, CLE, HOU, LAD, NYY, PHI	Strongest overall skill profile, especially WAR, K-BB%, FIP-, xFIP-, and SIERA
Contact-control run preventers	ATL, SFG, STL	Strong run prevention, home-run control, ground-ball tendency, and starter length
Modern peripheral builders	ARI, MIL, SDP, TBR, TOR	Better in K-BB%, xFIP, and SIERA than in long-term WAR or ERA dominance
Mixed middle profiles	ATH, CHW, DET, LAA, MIN, NYM, SEA, WSN	No single shared identity as strong as the other groups, generally middle-range profiles
Low-dominance HR suppressors	MIA, PIT	Weak dominance indicators but relatively better home-run suppression
Long-term struggling profiles	BAL, CIN, COL, KCR, TEX	Broadly weak long-term pitching profile across value, dominance, and fielding-independent metrics

Why the clusters formed

Figure 2 explains the dendrogram. It shows the average profile of each cluster.

The elite skill-and-value group is strong almost everywhere that modern pitching analysis would expect. The group is especially strong in K-BB%, FIP-, xFIP-, SIERA, and WAR. This is the clearest “modern excellence” cluster.

The contact-control group is different. Atlanta, San Francisco, and St. Louis are not grouped primarily because of overwhelming strikeout dominance. They cluster because of run prevention, home-run prevention, ground-ball tendency, and starter length. This is a more traditional-looking run-prevention cluster.

The modern peripheral group is subtle. Arizona, Milwaukee, San Diego, Tampa Bay, and Toronto do not dominate the long-term value category. Still, they show enough similarity in strikeout-minus-walk skill and advanced indicators to cluster together. This feels like a group of organizations that, at different times, leaned into modern pitching design without producing the same full-period dominance as the Dodgers or Yankees.

The struggling group is also clear. Baltimore, Cincinnati, Colorado, Kansas City, and Texas sit below average in most of the key categories. Colorado’s presence is not surprising because pitching in Denver creates a unique environmental problem. But the dendrogram is not only about park effects. The broader cluster reflects weaker long-term skill indicators too.

Why the Dodgers separate

The Dodgers are inside the elite skill-and-value cluster, but they remain visually distinctive. They join the group later than several other teams inside that cluster. That is important.

It suggests that the Dodgers are similar to other elite pitching organizations but are still somewhat their own thing. Their long-term profile is so strong across so many categories that they are not simply interchangeable with the Yankees, Astros, Guardians, Phillies, Cubs, or Red Sox.

That matches the earlier chapter. The Dodgers were the top pitching organization by average normalized score from 2001 to 2025. The dendrogram confirms the same story from a different angle. They are not only highly ranked. They have a recognizable organizational profile.

Houston and Cleveland as development stories

Houston and Cleveland are also revealing.

The Astros cluster with the elite organizations, but their full-period story contains more transformation than the Dodgers’ story. Houston’s early-2010s pitching collapse and late-2010s pitching rise are both part of the same 25-year average. Even with those bad seasons included, Houston still clusters with the strongest pitching organizations. That says something about how powerful the later Houston pitching model became.

Cleveland’s placement also makes sense. The Guardians are not always discussed like the Dodgers or Yankees because they operate with a different market profile, but the data places them in the same broad pitching family. Their identity is built around development, strike-zone control, and repeatable pitching skill.

Tampa Bay, Milwaukee, and the modern middle

The Rays and Brewers are especially interesting because they appear in the modern peripheral group rather than the elite skill-and-value group.

That is not an insult. It may actually be the more interesting result.

Tampa Bay and Milwaukee often represent modern pitching creativity: bullpen flexibility, role adaptation, pitcher development, and tactical staff construction. But over the full 25-year period, the dendrogram places them closer to San Diego, Toronto, and Arizona than to the Dodgers or Yankees.

That suggests a distinction between modernity and dominance. A team can be tactically modern without producing the same long-term value profile as the very top organizations.

What the dendrogram adds

The dendrogram adds something that a leaderboard cannot.

A leaderboard says:

The Dodgers were first.
The Yankees were second.
The Astros, Guardians, Cubs, Red Sox, Phillies, and Braves followed.

The dendrogram says something different:

The Dodgers, Yankees, Astros, Guardians, Cubs, Red Sox, and Phillies belong to a shared skill-and-value family.

The Braves, Giants, and Cardinals form a separate contact-control and run-prevention family.

The Rays, Brewers, Padres, Blue Jays, and Diamondbacks form a modern peripheral family.

The Rockies, Royals, Orioles, Reds, and Rangers share a long-term struggling profile.

That is the value of clustering. It changes the question from “who was better?” to “who was built alike?”

Conclusion

The central lesson is that pitching identity has shape.

Some organizations built strong staffs across nearly every modern indicator. Some built staffs that prevented runs through contact management and home-run control. Some had modern peripheral strengths without the same long-term value profile. Some struggled across nearly every measure.

The dendrogram does not replace the chapter’s rankings. It deepens them.

From 2001 to 2025, the best pitching organizations were not merely collecting arms. They were building systems. The Dodgers built the most consistent system. The Yankees, Astros, Guardians, Phillies, Cubs, and Red Sox clustered near them because they also produced strong long-term skill profiles. The Braves, Giants, and Cardinals showed a different path, one built more around run prevention and contact control.

The larger point is simple: team pitching is not just performance. It is identity.

And the dendrogram shows us how identity takes shape.

Pitching in the Age of the Strikeout: MLB Team Pitching, 2001-2025

Pitching is one of the most difficult parts of baseball to measure because it sits at the intersection of many things. A pitcher controls the ball, but not everything that happens after contact. A defense turns balls in play into outs, or fails to. A park changes the meaning of a fly ball. A league environment changes the meaning of a 4.00 ERA. A bullpen changes the way we understand a starter. A front office changes the way we understand a pitching staff.

That is why a long-term team pitching study has to be deliberate and careful. If we simply rank every team from 2001 to 2025 by ERA, we are mixing together very different run environments. A 3.70 team ERA in one season does not mean exactly the same thing as a 3.70 team ERA in another. The offensive environment changes. The baseball changes. Strikeout rates change. Bullpen usage changes. Even the definition of a normal starting pitcher changes.

So the goal of this chapter is not merely to ask which team had the lowest ERA. The better question is this: which organizations consistently produced strong pitching staffs relative to their own era?

That is a critical distinction. A team does not pitch in the abstract. It pitches in a particular season, against a particular league, with a particular baseball, inside a particular tactical environment. The 2001 Diamondbacks, the 2011 Phillies, the 2017 Guardians, the 2018 Astros, and the 2024 Braves all belong to the same broad story, but they do not belong to the same pitching world.

The data in this study covers MLB team pitching from 2001 through 2025. The core variables include ERA, ERA-, FIP, FIP-, xFIP, xFIP-, SIERA, WAR, K%, BB%, K-BB%, HR/9, HR/FB, complete games, quality starts, and several contact-profile measures. For 2001, xFIP, SIERA, and detailed contact data are incomplete, so the 2001 season is included in the main study but handled carefully where those variables are missing.

The central finding is straightforward: from 2001 to 2025, team pitching moved decisively toward strikeout-based run prevention. The best organizations were not simply the ones that prevented runs in a given season. They were the ones who repeatedly built staffs with strong strikeout-minus-walk rates, strong fielding-independent indicators, and enough depth to remain competitive across changing offensive environments.

The Dodgers stand out most clearly. Over the full 25-year period, they were the strongest pitching organization by average normalized pitching score. The Yankees, Astros, Guardians, Cubs, Red Sox, Phillies, and Braves also appear near the top. But the Dodgers are the outlier, not because of one spectacular season, but because of repeated organizational excellence.

Method: comparing teams within seasons

The first methodological problem is that pitching statistics are unstable over time. A league-average pitching staff in 2001 did not look like a league-average pitching staff in 2025. Strikeouts increased. Complete games declined. Velocity rose. Bullpen usage expanded. Home-run rates surged and retreated. If we compare raw numbers across all years, we risk confusing historical context with team quality.

To solve this, each team-season was compared only to the other teams from the same season. In other words, the 2011 Phillies were compared to the league as a whole in 2011. The 2018 Astros were compared with the league in 2018. The 2024 Braves were compared to the league as a whole. This allows us to ask which staffs were exceptional relative to the environment in which they actually pitched.

The basic within-season z-score is:

z_y(X_{i,y}) = \frac{ X_{i,y} - \overline{X}_y }{ s_y(X) }

Here, ( $X_{i,y}$ ) is a statistic for team (i) in season (y), ( $\overline{X}_y)$ is the league average for that statistic in that season, and ( $s_y(X)$ ) is the standard deviation across teams in that season.

For statistics where higher is better, such as WAR and K-BB%, the z-score is used directly. For statistics where lower is better, such as ERA-, FIP-, xFIP-, and SIERA, the sign is reversed. This keeps the interpretation consistent. A higher score always means better pitching.

q_{i,y,m} = \begin{cases} z_y(m_{i,y}), & \text{if higher values are better} \\ -z_y(m_{i,y}), & \text{if lower values are better} \end{cases}

The overall pitching score is then the average of the available component scores:

\text{Pitching Score}_{i,y} = \frac{1}{|M_{i,y}|} \sum_{m \in M_{i,y}} q_{i,y,m}

The metric set is:

M = \left\{ \text{WAR}, \text{K-BB\%}, \text{ERA-}, \text{FIP-}, \text{xFIP-}, \text{SIERA} \right\}

This score is not meant to be the only possible definition of pitching quality. It is a deliberately balanced measure. It includes actual run prevention through ERA-, fielding-independent performance through FIP- and xFIP-, skill-based dominance through K-BB%, and overall value through WAR.

K-BB% is especially important because it captures the two plate appearance outcomes most directly controlled by the pitcher: strikeouts and walks.

\text{K-BB\%} = \text{K\%} - \text{BB\%}

FIP also deserves special attention because it attempts to isolate the events most directly connected to the pitcher: home runs, walks, hit batters, and strikeouts.

\text{FIP} = \frac{ 13 \cdot \text{HR} + 3 \cdot (\text{BB} + \text{HBP}) - 2 \cdot \text{K} }{ \text{IP} } + c_{\text{FIP}}

The constant ( $c_{\text{FIP}}$ ) places FIP on an ERA-like scale. Because that run environment changes by season, FIP- and other indexed measures help compare teams more fairly.

The league changes: strikeouts become the center of pitching

The first figure shows the most important league-wide transformation: the rise of strikeouts.

Figure 1. K%, BB%, and K-BB% trends, 2001-2025

In 2001, the league strikeout rate was about 17.3%. By 2025, it was about 22.2%. That is not a small tactical adjustment. It is a structural change in how pitching works. The modern pitching staff is built around missing bats in a way that the early 2000s staff was not.

Walk rate did not change nearly as dramatically. In 2001, the league walk rate was about 8.4%. In 2025, it was about 8.4% again. There were fluctuations in between, but the broad pattern is clear: strikeouts rose much more than walks did.

That means K-BB% increased substantially. In 2001, league K-BB% was about 8.9%. In 2025, it was about 13.8%. That is the heart of the modern pitching revolution. The best staffs are not just striking out more hitters. They are increasing the gap between strikeouts and walks.

This is why K-BB% belongs near the center of the composite score. It is simple, but powerful. It strips pitching down to a basic contest: can the staff create strikeouts without giving back too many free baserunners?

The answer, increasingly, is yes. But not all teams answered equally well.

Run prevention, FIP, and the changing meaning of ERA

The second figure compares ERA, FIP, xFIP, and SIERA over time. Note that some of the data overlaps on the same line.

Figure 2. ERA, FIP, xFIP, and SIERA trends, 2001-2025

One of the striking features of the long-term data is that ERA does not move in one simple direction. The league ERA was about 4.42 in 2001. It fell to about 3.74 in 2014, rose to about 4.51 in 2019, and then settled around 4.16 in 2025.

That pattern matters because it reminds us that pitching quality cannot be evaluated solely by raw ERA. A team can have a lower ERA because it is genuinely better, but it can also have a lower ERA because the entire league is scoring less. Likewise, a higher ERA in a high-offense environment may not be as bad as it looks.

The 2014 season is a useful example. League run prevention was strong. ERA, FIP, xFIP, and SIERA all sat at relatively low levels. A good pitching staff in 2014 had to be judged against that lower-scoring context. The opposite problem appears in 2019, when the home-run environment pushed run scoring upward. A team that survived 2019 with strong FIP-based indicators deserves credit, given the more difficult environment.

That is why indexed statistics such as ERA- and FIP- are valuable. They tell us how a staff performed relative to league average, where lower is better.

\text{ERA-} = 100 \cdot \frac{ \text{Team ERA} }{ \text{League ERA} } \quad \text{adjusted for context}

A team with an ERA- of 90 was roughly 10% better than league average by that measure. A team with an ERA- of 110 was roughly 10% worse. The same logic applies to FIP-, except the foundation is fielding-independent pitching rather than actual runs allowed.

This distinction becomes crucial when comparing staffs across 25 seasons. The 2011 Phillies and the 2018 Astros both appear as historically great staffs, but they do not look great in exactly the same way. The Phillies represent a more traditional elite rotation model. The Astros represent the modern strikeout, command, and run-prevention model.

The home-run environment

The third figure shows the home-run environment.

Figure 3. HR/9 and HR/FB trends, 2001-2025

Home runs are one of the most important pressure points in modern pitching analysis because they connect individual pitcher skill, batted-ball profile, park context, and league environment. In 2001, league HR/9 was about 1.14. It dipped below 1.00 in several seasons, including 2010 and 2014, then spiked dramatically in 2019, reaching about 1.41 HR/9.

The 2019 season stands out immediately. It was not merely a season with more scoring. It was a season in which the relationship between contact and damage changed. HR/FB also rose sharply, reaching about 15.3% in 2019. That created a very different environment for pitchers.

This is one reason xFIP can be useful. FIP uses actual home runs allowed. xFIP estimates performance by normalizing home-run rate relative to fly balls. Neither statistic is perfect. FIP gives pitchers the actual cost of the home runs they allowed. xFIP asks whether that home-run rate was likely to persist.

For a team-level study, the difference between FIP and xFIP can be revealing. A team with a much better FIP than xFIP may have suppressed home runs unusually well, perhaps through park effects, pitcher skill, batted-ball management, or some combination of these. A team with a much worse FIP than xFIP may have been punished by an elevated home-run rate.

The home-run environment also helps explain why season-normalization is necessary. A team pitching in 2019 faced a different kind of run-prevention problem than a team pitching in 2014. The raw numbers alone cannot tell us whether a staff was good. They have to be interpreted against the league context.

The disappearance of the complete game

The fourth figure captures one of the clearest tactical changes in baseball.

Figure 4. Complete games and quality starts, 2001-2025

In 2001, MLB teams combined for 199 complete games. In 2002, that number was 214. By 2025, it had fallen to 29.

This is not a gradual stylistic preference. It is a transformation in pitcher usage. The complete game went from a normal, if still special, part of pitching to a rare event. The starting pitcher’s job changed. The bullpen’s job changed. The manager’s job changed. The entire architecture of run prevention changed.

Quality starts also declined. In 2001, teams combined for 2,342 quality starts. In 2025, that number was 1,676. Unlike complete games, quality starts did not nearly vanish; they simply became less central to how team pitching is organized.

This matters because a traditional pitching staff was often understood through the front of the rotation. The ace mattered. The number two starter mattered. The innings-eater mattered. In the modern game, those categories still matter, but they are less complete descriptions of team pitching quality. A staff can be excellent because it has dominant starters, but it can also be excellent because it has a deep bullpen, matchup flexibility, velocity, strikeout depth, and player-development infrastructure.

This is one reason team-level pitching analysis is valuable. It captures the staff as an organization, not merely as a list of starting pitchers.

The organizational scoreboard

The franchise-level results show which organizations repeatedly built strong pitching staffs across the full period.

Figure 5. Franchise average pitching score, 2001-2025

The top organizations by average normalized pitching score were:

Rank	Franchise	Avg Score	Avg Rank	Top-5 Seasons
1	LAD	1.043	5.80	16
2	NYY	0.785	8.12	10
3	HOU	0.470	11.20	8
4	CLE	0.447	12.16	9
5	CHC	0.363	12.80	6
6	BOS	0.361	12.44	6
7	PHI	0.356	11.88	6
8	ATL	0.344	12.60	7

The Dodgers are the clear leader. Their average rank was 5.80 across 25 seasons, and they finished in the top five 16 times. That is a remarkable level of consistency.

The Yankees also stand out. They were not as dominant as the Dodgers by average score, but they were consistently strong. Their average rank was 8.12, and they had 10 top-five seasons.

Houston’s position is interesting because the Astros’ 25-year period includes both very bad years and elite years. Their full-period average ranks third, but that average hides a sharp organizational transformation. The 2013 Astros appear among the worst pitching seasons in the dataset. The 2018 and 2019 Astros appear among the strongest. That makes Houston one of the most dramatic before-and-after stories in the study.

Cleveland also deserves attention. The Guardians were not merely good in one season. They produced nine top-five seasons across the full period, including the remarkable 2017 staff and the shortened-season 2020 staff. Cleveland’s results point toward a consistent ability to develop or acquire pitching skill, especially strikeout and command skill.

The Phillies are different. Their full-period average is strong, but their story is anchored by the 2011 staff, the top single-season result in the study. The Phillies’ score is not just about consistency. It is about peak excellence.

The best team pitching seasons

The best individual team pitching seasons in the study were:

Figure 6. Best team pitching seasons, 2001-2025

Rank	Season	Team	Score	WAR	ERA-	FIP-	K-BB%
1	2011	PHI	2.472	29.45	78.58	82.88	14.75%
2	2017	CLE	2.464	30.35	72.49	75.44	20.59%
3	2018	HOU	2.442	28.63	75.89	78.23	21.17%
4	2013	DET	2.089	26.26	89.31	82.13	15.79%
5	2024	ATL	2.059	23.62	84.13	86.61	18.47%

The top three are especially revealing because they show three different versions of elite pitching.

The 2011 Phillies represent the great traditional staff. Their rotation was the center of the story. Their ERA- was 78.58, meaning they were far better than league average at preventing runs. Their FIP- was also excellent at 82.88. They were not merely outperforming their peripherals. They were genuinely strong across the major indicators.

The 2017 Guardians look like a bridge between traditional excellence and modern dominance. Their ERA- was 72.49, the best among the top five listed here, and their FIP- was 75.44. Their K-BB% was 20.59%, which is extraordinary. This is a staff that combined run prevention, fielding-independent strength, and strikeout-minus-walk dominance.

The 2018 Astros are the modern model. Their K-BB% was 21.17%, the highest among these top five. Their ERA- and FIP- were both outstanding. They did not merely prevent runs. They controlled the plate appearance.

That phrase may be the key to the whole chapter: the modern elite staff controls the plate appearance. It wins by turning fewer balls into uncertain events. More strikeouts. Fewer walks. Better home-run control. Better matchup deployment. Better depth.

The 2013 Tigers are also fascinating. Their FIP- was much stronger than their ERA-, which suggests a staff whose fielding-independent indicators were better than its actual run prevention. That kind of gap is analytically useful because it may point toward defense, sequencing, bullpen leakage, park effects, or simple variation.

The 2024 Braves round out the top five, showing that the modern model remains alive. Strong WAR, strong ERA-, strong FIP-, and excellent K-BB% place them among the best team pitching seasons of the last 25 years.

The heat map view: organizational memory

The heat map shows how pitching strength is distributed over time.

Figure 7. Team pitching score heat map, 2001-2025

A heat map is useful because it shows continuity. A table gives us leaders. A heat map gives us memory.

The Dodgers’ consistency becomes visible immediately. They do not merely spike and disappear. They remain strong across many different league environments. This suggests that their pitching success is not just the product of one rotation or one era. It is organizational.

The Yankees also show long-term strength, although with a different shape. They remain regularly above average, but the Dodgers’ top-end consistency is stronger.

Houston’s pattern is more dramatic. The Astros transitioned from poor pitching during the rebuilding years to elite pitching in the late 2010s and beyond. This makes them one of the best examples of organizational reinvention in the dataset.

Cleveland’s pattern is also compelling. The Guardians do not always (understatement) have the resources of the largest-market teams, but the pitching results are consistently strong enough to suggest a real developmental identity. Cleveland’s peak seasons are not accidents.

Tampa Bay deserves separate attention as well. The Rays do not rank at the very top over the full period, but their modern pitching identity is clear. They are one of the organizations most associated with bullpen creativity, opener usage, and flexible staff construction. A team-level study captures some of that, although a starter-reliever split would make the story even sharper.

While the heat map shows the full league, a smaller set of franchise trajectories makes the organizational story easier to see. Figure 8 follows several teams that help define the period: the Dodgers, Yankees, Astros, Guardians, Rays, Phillies, and Braves. The Dodgers show sustained excellence. Houston shows dramatic organizational reinvention. Cleveland and Tampa Bay show the value of pitching development and tactical adaptation. Philadelphia shows the difference between peak rotation dominance and long-term consistency.

Figure 8. Selected Franchises, 2001 – 2025

Three eras of team pitching

Breaking the study into periods helps clarify the historical movement.

From 2001 to 2009, the leading organizations were:

Period	Rank	Franchise	Avg Score	Avg Rank	Top-5 Seasons
2001-2009	1	CHC	0.980	6.89	4
2001-2009	2	LAD	0.840	7.33	5
2001-2009	3	ARI	0.805	8.78	5
2001-2009	4	BOS	0.791	8.22	4
2001-2009	5	NYY	0.733	9.00	4

The early period is more rotation-centered. The Cubs, Dodgers, Diamondbacks, Red Sox, and Yankees all had strong stretches. This era still belongs partly to the older model of staff construction. Starting pitching carries more of the symbolic weight. Complete games are declining, but they have not yet collapsed to modern levels.

From 2010 to 2019, the leaders were:

Period	Rank	Franchise	Avg Score	Avg Rank	Top-5 Seasons
2010-2019	1	LAD	1.202	4.40	7
2010-2019	2	NYY	0.925	6.70	4
2010-2019	3	WSN	0.733	9.20	3
2010-2019	4	CLE	0.709	10.20	6
2010-2019	5	TBR	0.687	8.60	2

This is the period when the modern pitching environment becomes much clearer. Strikeouts rise. Velocity rises. Bullpen roles become more specialized. Cleveland, Tampa Bay, Washington, and Los Angeles all become central parts of the story.

From 2020 to 2025, the leaders were:

Period	Rank	Franchise	Avg Score	Avg Rank	Top-5 Seasons
2020-2025	1	LAD	1.082	5.83	4
2020-2025	2	PHI	0.941	5.67	3
2020-2025	3	MIL	0.808	8.00	2
2020-2025	4	TBR	0.808	6.83	2
2020-2025	5	ATL	0.658	11.33	2

The modern period is especially interesting because it includes the shortened 2020 season, the post-2020 workload reset, and the continuing dominance of strikeout-based staff construction. The Dodgers remain first. The Phillies rise. The Brewers and Rays become central examples of modern pitching development and staff management. The Braves also emerge strongly, especially with the 2024 season.

The worst seasons and the cost of weak pitching infrastructure

The worst team pitching seasons are just as revealing as the best ones.

At the bottom of the dataset are seasons such as the 2025 Rockies, 2006 Royals, 2023 Athletics, 2024 Rockies, and 2013 Astros. These seasons combine weak WAR, poor run prevention, poor FIP-based indicators, and low K-BB%.

The 2025 Rockies had a pitching score of -2.757, with a 125.19 ERA-, 119.75 FIP-, and only an 8.47% K-BB%. The 2006 Royals were similarly poor, with a 124.43 ERA-, 118.20 FIP-, and a 4.15% K-BB%. The 2023 Athletics had a 132.94 ERA-, 122.15 FIP-, and 9.49% K-BB%.

These are not merely bad ERAs. They are broad staff failures. When a team is poor in both run prevention and fielding-independent indicators, the problem is deeper than sequencing or defense. It suggests that the staff is not controlling the strike zone, not limiting damaging contact enough, and not producing enough value.

The 2013 Astros are especially important because they later became one of the strongest pitching organizations in the study. That contrast gives us a natural case study in organizational transformation. Bad pitching staffs do not have to remain bad forever. But the transformation requires more than one good pitcher. It requires a system.

What the study suggests

This first pass suggests several conclusions.

First, team pitching from 2001 to 2025 became increasingly strikeout-centered. The rise in K% and K-BB% is the central statistical movement of the period. It changed what good pitching looks like.

Second, raw ERA is not enough for a long-term study. ERA remains important because runs allowed are real. But ERA must be placed next to FIP, xFIP, SIERA, K-BB%, and indexed measures such as ERA- and FIP-. Otherwise, we risk mistaking league environment for team quality.

Third, complete games and traditional starter workload declined dramatically. This changes how we should think about team pitching. A great staff is no longer just a great rotation. It is a complete run-prevention system.

Fourth, the Dodgers are the strongest pitching organization of the 2001-2025 period. Their dominance is not just peak dominance. It is consistency. They averaged a top-six pitching rank across 25 seasons and finished in the top five 16 times.

Fifth, several organizations deserve deeper case studies. The Astros show organizational reinvention. The Guardians show player-development strength. The Rays show tactical creativity. The Phillies show the power of peak rotation excellence. The Braves show modern staff strength. The Yankees show long-term high-level stability.

Conclusion: pitching as organizational identity

The most important lesson from this study is that pitching is no longer best understood as a collection of individual arms. At the team level, pitching has become an organizational identity.

The best teams do not merely find pitchers. They shape pitching environments. They develop velocity. They manage workloads. They build bullpens. They optimize matchups. They control the strike zone. They use data to turn raw stuff into repeatable advantage.

That is why the Dodgers’ long-term record matters. It is not just that they had good pitchers. Many teams have good pitchers for a year or two. The Dodgers repeatedly built strong pitching staffs across different run environments, tactical eras, and roster cycles.

The same broader lesson applies to Houston, Cleveland, Tampa Bay, Milwaukee, Atlanta, Philadelphia, and New York. The details differ, but the underlying pattern is the same. Modern pitching excellence is systemic.

From 2001 to 2025, baseball shifted toward a game in which the best staffs increasingly controlled plate appearances. Strikeouts rose. Walks became more costly. Home runs reshaped risk. Complete games disappeared. Bullpens expanded. The old image of pitching as one starter carrying a game into the ninth inning gave way to something more distributed, more specialized, and more organizational.

The great pitching staffs of this period are therefore not just statistical outliers. They are historical markers. They show how the game changed, and how the smartest organizations changed with it.

The Shape of Defense: What MLB Fielding Metrics Tell Us So Far This Season

The Shape of Defense: What MLB Fielding Metrics Tell Us So Far This Season

Defense is the hardest part of baseball to measure cleanly. I thought it might be interesting to study all MLB teams as of the end of June, 2026.

Hitting leaves a visible trail. A batter walks, strikes out, singles, doubles, homers, or makes an out. Pitching is more complicated, but it still has a fairly direct statistical language. Strikeouts, walks, home runs, velocity, chase rate, and contact quality all point in recognizable directions. Defense is different. Good defense often appears as absence. The ball that does not fall. The extra base that is not taken. The throw that does not need to be dramatic because the fielder got to the ball early enough.

That makes defensive analysis both frustrating and interesting. One number rarely tells the whole story. Fielding percentage tells us whether a team usually completes the plays it reaches, but it says little about how many plays it reaches in the first place. Errors measure visible mistakes, but not invisible range. Modern metrics try to correct for that. Defensive Runs Saved, Outs Above Average, Fielding Run Value, FanGraphs Def, framing value, arm value, and range value each capture a different part of the defensive picture.

For this study, I used three FanGraphs team defensive leaderboards. The goal was not simply to rank teams. The better question is how the different defensive systems agree, where they disagree, and what kind of defense each team is actually playing.

The main conclusion is clear: so far this season, the Cubs are the strongest defensive team in baseball by a wide margin. But the deeper conclusion is more interesting. OAA, FRV, and FanGraphs Def are telling very similar stories. DRS is related to those measures, but it is not identical. Traditional fielding percentage has some relationship to defensive value, but not nearly enough to stand on its own.

Defense, in other words, is not one thing.

Building a Composite Defensive Score

To compare teams across multiple defensive systems, I created a composite z-score using four broad measures:

Defensive Runs Saved, or DRS
Outs Above Average, or OAA
Fielding Run Value, or FRV
FanGraphs Def

Each metric was standardized across the 30 MLB teams. The z-score for team (i) on metric (m) is:

z_{i,m} = \frac{x_{i,m} - \mu_m}{\sigma_m}

where $(x_{i,m})$ is team (i)’s value on metric (m),

$(\mu_m)$ is the league average for that metric,

and $(\sigma_m)$ is the standard deviation across teams.

The composite defensive score is then:

D_i = \frac{ z_{i,\mathrm{DRS}} + z_{i,\mathrm{OAA}} + z_{i,\mathrm{FRV}} + z_{i,\mathrm{Def}} }{4}

This score does not claim that all defensive metrics are perfect or equally philosophical. It is simply a way to ask a practical question: which teams look good across several major defensive metrics simultaneously?

The top of the list is not subtle.

Rank	Team	Composite Z	DRS	OAA	FRV	Def
1	CHC	2.41	57	38	34	34.06
2	LAD	1.73	61	23	20	21.77
3	BOS	1.32	42	18	17	19.20
4	ARI	1.30	25	23	23	19.08
5	SDP	1.08	17	16	22	19.61
6	STL	0.89	18	16	20	10.75

The Cubs are not merely first. They are first by a lot. Their composite z-score is 2.41, meaning they are far above league average across the combined defensive measures. The Dodgers are also excellent, but they are closer to the next group than they are to Chicago.

At the other end of the chart, the weakest defensive teams are also fairly clear.

Team	Composite Z	DRS	OAA	FRV	Def
MIN	-1.50	-32	-19	-19	-17.35
SEA	-1.36	-2	-27	-22	-18.83
LAA	-1.24	-3	-18	-23	-19.03
PHI	-1.08	-29	-18	-9	-7.46
ATH	-1.08	-4	-19	-16	-15.51

Minnesota rates last by the composite score. Seattle and the Angels are also deep in negative territory, though they arrive there in slightly different ways. Minnesota is hurt badly by DRS and modern range-based measures. Seattle is particularly poor by OAA and FRV standards. The Angels are near the bottom in FRV and FanGraphs Def.

The first lesson is that the defensive standings have a clear shape. Chicago is alone at the top. Los Angeles leads the next tier. Boston, Arizona, San Diego, and St. Louis form a strong second group. At the bottom, Minnesota, Seattle, Los Angeles Angels, Philadelphia, and the Athletics are the weakest group.

But rankings are only the beginning.

OAA and FRV Mostly Agree

The tightest relationship in the study is between Outs Above Average and Fielding Run Value.

The regression equation is:

\mathrm{FRV}_i = 0.83 \cdot \mathrm{OAA}_i + 0.77

with:

R^2 = 0.819

That is a strong relationship. This means that about 82% of the variation in team FRV is explained by team OAA in this dataset.

That makes intuitive sense. OAA and FRV are closely related modern defensive concepts. Both are trying to move beyond errors and fielding percentage. Both are interested in actual plays made relative to expected plays. Both reward teams that turn difficult batted balls into outs.

The Cubs sit in the upper-right corner of the chart. They are not just good by one metric. They are extreme by both. Chicago has 38 OAA and 34 FRV. Arizona, Los Angeles, San Diego, Boston, and St. Louis also occupy the positive area of the chart. At the bottom, Seattle, the Angels, Minnesota, and the Athletics cluster in negative territory.

This is useful because it gives confidence. If two modern metrics with related but not identical constructions point in the same direction, the result is more persuasive. The Cubs are not a leaderboard accident. Their defensive advantage appears in multiple systems.

DRS Tells a Related but Different Story

DRS also matters, but it does not align with FRV as tightly as OAA does.

The regression equation is:

\mathrm{FRV}_i = 0.43 \cdot \mathrm{DRS}_i - 3.98

with:

R^2 = 0.418

That is still a meaningful relationship, but it is much weaker than the OAA-FRV relationship. DRS and FRV are clearly not measuring the same thing in the same way.

This is where the study becomes more interesting. The Dodgers have the highest DRS total in the dataset, with 61, but they trail the Cubs in FRV, OAA, and Def. The Cubs have slightly lower DRS than Los Angeles, but they dominate in OAA and FRV. Tampa Bay is another example of disagreement. The Rays have a positive DRS total, but their FRV is negative. Philadelphia is negative in both, but much worse in DRS than FRV.

The correlation table reinforces the point. The correlations among the major modern measures are:

Pair	Correlation
OAA and FRV	0.90
FRV and Def	0.97
OAA and Def	0.94
DRS and OAA	0.65
DRS and FRV	0.65
DRS and Def	0.66

The formula for correlation is:

r_{XY} = \frac{ \sum_i (x_i - \bar{x})(y_i - \bar{y}) }{ \sqrt{\sum_i (x_i - \bar{x})^2} \sqrt{\sum_i (y_i - \bar{y})^2} }

This tells us that DRS belongs in the conversation, but it should not be treated as interchangeable with OAA or FRV. When DRS disagrees with the Statcast-style measures, that disagreement is not a nuisance. It is evidence that defensive measurement still depends on the assumptions built into each system.

The Cubs Are Winning with Range

The component data explains why Chicago is separated from the rest of the league.

The Cubs have:

Component	Value
Range	31
Arm	7
Framing	-3
Blocking	0
Throwing	-2
FRV	34

That is the key to the whole study. Chicago is not leading because of framing. It is not leading because of blocking. It is leading because of range.

Range is the largest component in the dataset. The Cubs have a Range value of 31. The next highest teams are Arizona at 19, the Dodgers at 18, Boston at 16, and San Diego at 13. That gap is enormous. It suggests that Chicago is turning a large number of balls in play into outs that an average defense might not convert.

A simple component model is:

\mathrm{FRV}_i = \mathrm{Range}_i + \mathrm{Arm}_i + \mathrm{Framing}_i + \mathrm{Blocking}_i + \mathrm{Throwing}_i + \epsilon_i

The additional term at the end of the equation: $(\epsilon_i)$ is included because published component totals may not always sum perfectly to the displayed total due to rounding, classification, or metric construction. But the practical interpretation is still clear. In this dataset, Range is the dominant separating variable.

Arizona is similar to Chicago in shape, though not in magnitude. The Diamondbacks have 19 Range and 23 FRV. The Dodgers combine 18 Range with 6 Arm. Boston combines 16 Range, 3 Arm, and 2 Blocking. San Diego has a more balanced profile, with 13 Range, 4 Arm, and positive throwing value.

Toronto is the most interesting contrast. The Blue Jays have a strong FRV total of 12, but they do not get it from range. Their Range value is -2. Their Framing value is 13. Toronto is a reminder that not all good defenses are built the same way. Some teams prevent runs by reaching more balls. Others gain value through catcher receiving. A single defensive ranking can hide those differences.

Traditional Fielding Stats Still Miss the Heart of Defense

Fielding percentage remains familiar, but it is not enough.

The regression between fielding percentage and FanGraphs Def is:

\mathrm{Def}_i = 1958.5 \cdot \mathrm{FP}_i - 1930.6

with:

R^2 = 0.227

That means that fielding percentage explains only about 23% of the variation in FanGraphs Def in this dataset.

This should not be surprising. Fielding percentage is calculated as:

\mathrm{FP} = \frac{\mathrm{PO} + \mathrm{A}} {\mathrm{PO} + \mathrm{A} + \mathrm{E}}

Putouts and assists are important, as are errors. But this formula has a blind spot. It only evaluates plays that become official chances. It does not ask how many difficult balls were reached. It does not ask whether a fielder’s first step turned a hit into an out. It does not measure the value of positioning. It does not capture the difference between a clean single and a ball that a better defense would have converted into an out.

This is why a team can have a decent fielding percentage and still grade poorly by modern defensive metrics. Avoiding errors is not the same thing as preventing hits. Completing routine plays is not the same thing as creating outs.

The traditional fielding formula is useful, but incomplete. It measures reliability on contacted chances. Modern defensive metrics try to measure territory, difficulty, and run value.

What the Rankings Mean

The strongest defensive teams so far fall into three groups.

The first group is Chicago alone. The Cubs are the best team in the study because they combine elite OAA, elite FRV, elite FanGraphs Def, and excellent DRS. Their defense is driven by range, and the size of that range advantage is the most important finding in the data.

The second group includes the Dodgers, Red Sox, Diamondbacks, Padres, and Cardinals. These teams are all meaningfully above average. The Dodgers have the strongest DRS figure and excellent marks across the board. Arizona and San Diego are especially strong in FRV. Boston is balanced and rates highly in all major measures. St. Louis is not quite as strong by FanGraphs Def, but its OAA and FRV remain impressive.

The third group is more complicated. Atlanta, Cleveland, Toronto, Texas, Kansas City, and the Yankees all have positive composite scores, but each has a different profile. Toronto is especially noteworthy because its positive value comes primarily from framing rather than range. Texas has positive OAA but neutral FRV, which keeps its composite score closer to the middle.

At the bottom, Minnesota is the weakest team overall. Seattle and the Angels are also poor by modern defensive value. Philadelphia is unusual because DRS dislikes it more than FRV does, while the Athletics are consistently weak across several measures.

Why This is Important

Defense affects the interpretation of everything else.

A pitcher on a great defensive team is not working in the same environment as a pitcher on a poor defensive team. A staff backed by Chicago’s range advantage may see more balls converted into outs. A staff backed by a weak range defense may see more balls fall in, even when contact quality is similar. That matters when evaluating ERA, run prevention, pitcher luck, and even team overperformance.

The same is true for team analysis. A club with strong pitching numbers may be getting help from its defense. A club with disappointing run prevention may have a fielding problem hidden underneath the pitching line. Defense is not just a separate category. It is part of the context in which pitching statistics are produced.

This is especially important because modern baseball produces so many batted-ball measurements. We can now ask not just whether a ball was caught, but whether it should have been caught. We can compare actual outs to expected outs. We can separate the routine from the exceptional. That changes the language of defense.

Errors used to dominate the conversation because they were visible. Range is harder to see, but it is often more important.

Conclusion

So far this season, the defensive story is clear at the top. The Cubs have been the best defensive team in baseball by the combined evidence of DRS, OAA, FRV, and FanGraphs Def. Their advantage is not cosmetic. It is large, broad, and especially driven by range.

The Dodgers are also excellent, and they lead in DRS. Boston, Arizona, San Diego, and St. Louis form a strong second tier. At the bottom, Minnesota, Seattle, the Angels, Philadelphia, and the Athletics have been the weakest defensive teams by the composite measure.

But the broader lesson is methodological. Defense should not be reduced to one statistic. OAA, FRV, and FanGraphs Def are closely aligned. DRS is related but more independent. Fielding percentage captures a small part of the picture, but it misses much of what makes modern defense valuable.

The old defensive question was: did the fielder make an error?

The better question is: how many outs did the defense create that an average defense would not have created?

That is where the Cubs separate themselves. Not merely by being clean. By getting to the baseball.

Season-Level Validation: Do Third-Base Offensive Z-Scores Predict wRC+?

Introduction

The first wRC+ validation study used a career-level FanGraphs export.

That study was useful. It showed that, among regular third basemen, average Model C offensive score per qualified season strongly predicted career wRC+. It also showed that traditional defense did not predict wRC+, which was exactly what we wanted from a negative-control test.

But the career-level study had one limitation.

wRC+ is fundamentally a season-level offensive rate statistic. Our offensive z-score system is also built season by season. So the cleanest validation test is not career score against career wRC+.

The cleanest test is:

Does a third baseman’s season-level offensive z-score predict his season-level wRC+?

This chapter answers that question.

The answer is yes.

Using the season-level FanGraphs export, the Model C offensive season score explains about 69 percent of the variation in season wRC+ among qualified third-base seasons.

R^2 = 0.692

The fitted model is:

wRC^+ = 101.47 + 5.86(\text{Model C Offensive Season Score})

That is a strong result.

Just as important, the traditional defensive score does not predict wRC+:

R^2 = 0.002

This is exactly the pattern the project needed.

Offensive z-scores predict offense.

Traditional defensive z-scores do not.

That means the Model C offensive score is not merely identifying generally good players. It is measuring offensive quality.

Data Used in the Season-Level Study

The FanGraphs season-level export included:

9,152 player-season rows through 2025
Season
Name
Team
PA
wRC+
PlayerId
MLBAMID

The broader third-base season dataset included:

3,188 qualified third-base seasons
Season range: 1880–2025

The merge was very strong:

Matched seasons: 3,163
Unmatched seasons: 25
Match rate: 99.2%

The remaining unmatched seasons were mostly older Negro Leagues or historical ID cases. The modern and post-integration major-league seasons matched very well.

This makes the season-level validation much cleaner than the first career-level wRC+ test.

Why Season-Level Validation Matters

The career-level wRC+ test asked whether accumulated third-base offensive separation was related to career offensive quality.

The season-level test is more direct.

It asks:

In a given season, does the offensive z-score model identify the same kind of offensive performance that wRC+ identifies?

This is a better test because both measures are season-specific.

The z-score model compares a third baseman to other third basemen in the same season. wRC+ compares a hitter’s offensive production to the league and park context of that season.

They are not the same statistic.

But they should be related.

If Model C is measuring offensive quality, high Model C scores should correspond to high wRC+ values.

That is what the data show.

The Model C Offensive Score

The Model C offensive score uses seven components:

OBP
ISO
BB/PA
SO/PA, inverted
Net SB/PA
R/PA
RBI/PA

Each component is converted into a same-position, same-season z-score.

The basic z-score formula is:

z = \frac{x - \mu}{\sigma}

Where:

x = \text{the player's value}

\mu = \text{the same-position, same-season peer-group mean}

\sigma = \text{the same-position, same-season peer-group standard deviation}

This is the central idea of the study.

Raw numbers ask how large a number is. Z-scores ask how far a player separated from his peer group.

Offensive Component Equations

On-base percentage is:

OBP = \frac{H + BB + HBP}{AB + BB + HBP + SF}

Slugging percentage is:

SLG = \frac{TB}{AB}

Isolated power is:

ISO = SLG - AVG

Walk rate is:

BB/PA = \frac{BB}{PA}

Strikeout rate is:

SO/PA = \frac{SO}{PA}

Net stolen bases are:

NetSB = SB - CS

Net stolen-base rate is:

NetSB/PA = \frac{SB - CS}{PA}

Run rate is:

R/PA = \frac{R}{PA}

RBI rate is:

RBI/PA = \frac{RBI}{PA}

The strikeout component is inverted because lower strikeout rates are better:

z_{\text{Low SO/PA}} = -\left( \frac{ (SO/PA)_i - \overline{(SO/PA)}_{\text{peer}} }{ s_{SO/PA,\text{peer}} } \right)

The full Model C offensive season score is:

\begin{aligned} \text{Season Score} &= z_{\text{OBP}} + z_{\text{ISO}} + z_{\text{BB/PA}} + z_{\text{Low SO/PA}} \\ &\quad + z_{\text{NetSB/PA}} + z_{\text{R/PA}} + z_{\text{RBI/PA}} \end{aligned}

This score measures offensive separation from same-season third-base peers.

Regression Framework

The main validation model is:

wRC^+_s = \alpha + \beta_1(\text{Model C Offensive Season Score}_s) + \varepsilon_s

Where:

wRC^+_s = \text{FanGraphs wRC+ for season } s

\alpha = \text{intercept}

\beta_1 = \text{slope for the offensive z-score}

\varepsilon_s = \text{residual error}

The coefficient of determination is:

R^2 = 1 - \frac{ \sum_s \left( wRC^+_s - \widehat{wRC^+}_s \right)^2 }{ \sum_s \left( wRC^+_s - \overline{wRC^+} \right)^2 }

A higher value of $R^2$ means the model explains more of the variation in wRC+.

Main Season-Level Result

The fitted offense-only model is:

wRC^+ = 101.47 + 5.86(\text{Model C Offensive Season Score})

The result is:

R^2 = 0.692

This means the Model C offensive season score explains about 69.2 percent of the variation in season-level wRC+ among matched qualified third-base seasons.

That is a strong validation result.

The slope is also meaningful:

\beta_1 = 5.86

Each additional point of Model C offensive season score corresponds to about 5.86 additional points of wRC+.

For example, a player with an offensive score of 0 projects as:

wRC^+ = 101.47 + 5.86(0)

wRC^+ = 101.47

A player with an offensive score of 5 projects as:

wRC^+ = 101.47 + 5.86(5)

wRC^+ = 130.77

A player with an offensive score of 10 projects as:

wRC^+ = 101.47 + 5.86(10)

wRC^+ = 160.11

This is exactly the pattern expected if Model C is capturing offensive dominance.

Figure 1: Model Comparison

Figure 1. How well season-level third-base metrics predict wRC+.

The first figure compares several models.

The offensive z-score model performs well:

R^2_{\text{Offensive z-score}} = 0.692

The traditional defensive score performs almost not at all:

R^2_{\text{Traditional Defense}} = 0.002

Adding traditional defense to offense does not meaningfully improve the result:

R^2_{\text{Offense + Defense}} = 0.692

Adding plate appearances produces only a tiny improvement:

R^2_{\text{Offense + PA}} = 0.695

The WAR_off benchmark is higher:

R^2_{\mathrm{WAR}_{\mathrm{off}}} = 0.846

That is expected. WAR_off is already a sophisticated offensive value measure. It is included only as a benchmark, not as a competing z-score model.

The important comparison is offense versus defense.

The offensive z-score score predicts wRC+ strongly. The defensive score does not.

Figure 2: Offensive Z-Score Versus wRC+

Figure 2. Season wRC+ versus Model C offensive season score among third basemen.

This figure shows the main relationship directly.

The x-axis is:

\text{Model C Offensive Season Score}

The y-axis is:

wRC^+

The fitted line is:

wRC^+ = 101.47 + 5.86x

R^2 = 0.692

The pattern is clear.

High offensive z-score seasons generally produce high wRC+ seasons. Miguel Cabrera’s 2013 season, Chipper Jones’s 1999 season, Mike Schmidt’s 1980 and 1981 seasons, George Brett’s 1985 season, and Alex Rodriguez’s 2007 season all sit in the upper-right region.

That is exactly where they should be.

The plot also shows interesting residual cases. Some seasons have high wRC+ relative to their Model C score. Others have lower wRC+ than the z-score model predicts.

Those differences are not necessarily errors. They show that Model C and wRC+ measure offense from different angles.

Figure 3: Actual Versus Predicted wRC+

Figure 3. Actual versus predicted season wRC+ using the offensive z-score model.

The prediction equation is:

\widehat{wRC^+}_s = 101.47 + 5.86(\text{Model C Offensive Season Score}_s)

The residual is:

\text{Residual}_s = wRC^+_s - \widehat{wRC^+}_s

Players near the diagonal are well predicted. Players above the diagonal have higher wRC+ than the z-score model predicts. Players below the diagonal have lower wRC+ than the z-score model predicts.

The figure shows that most seasons fall around the diagonal, which is why the model produces a strong $R^2$ .

It also shows the value of residual analysis. The most interesting seasons are often the ones that do not land exactly where the model expects.

Figure 4: The Defensive Negative Control

Figure 4. Traditional defensive score does not predict season wRC+.

The negative-control model is:

wRC^+ = \alpha + \beta_1(\text{Traditional Defensive Season Score}) + \varepsilon

The fitted result is:

wRC^+ = 102.46 + 0.51(\text{Traditional Defensive Season Score})

R^2 = 0.002

This is one of the most important results in the chapter.

The traditional defensive score explains almost none of the variation in wRC+.

That is exactly what should happen.

wRC+ is an offensive metric. A traditional defensive score should not meaningfully predict it. The fact that it does not strengthens the validation.

It shows that the Model C offensive score is measuring offense specifically, not simply general player quality.

Figure 5: Residuals

Figure 5. Largest season-level wRC+ residuals from the offensive z-score model.

The residual equation is:

\text{Residual}_s = wRC^+_s - \widehat{wRC^+}_s

Positive residuals mean the season had a higher wRC+ than predicted by the z-score model.

Negative residuals mean the season had a lower wRC+ than predicted.

The largest positive residuals include:

Matt Williams 1995
Jim Finigan 1954
Jack Gleason 1884
Sean Berry 1995
Ron Cey 1981
George Scott 1970
Mike Schmidt 1981
Bill Joyce 1894

The largest negative residuals include:

Art Devlin 1905
Chone Figgins 2011
Jerry Royster 1977
Pie Traynor 1922
Chuck Harmon 1954
Bubba Phillips 1960
Charlie Hayes 1999
Maikel Garcia 2024

These residuals are worth studying because they show where the z-score model and wRC+ disagree most.

Interpreting Positive Residuals

A positive residual means wRC+ sees more offensive value than the z-score model predicts.

There are several possible reasons.

First, wRC+ is built from run values and is park- and league-adjusted. Model C is built from peer separation in selected categories. The two systems overlap strongly, but they are not identical.

Second, Model C includes runs and RBI rates. Those are useful for describing offensive dominance, but they can also be influenced by lineup context. wRC+ is more directly centered on offensive production independent of team context.

Third, partial seasons can create interesting differences. Matt Williams 1995, for example, had a very high wRC+ in fewer plate appearances than a full season. The z-score model includes playing-time weighting, so a shorter season can be pulled downward relative to a rate statistic.

That does not mean either measure is wrong.

It means they are answering slightly different questions.

Model C asks:

How much offensive separation did this third baseman produce in this season?

wRC+ asks:

How strong was this hitter's offensive production after league and park adjustment?

Those are related questions, not identical questions.

Interpreting Negative Residuals

A negative residual means the z-score model predicted a higher wRC+ than the player actually had.

This can happen when a player scores well in the Model C components but not as well in wRC+.

For example, a player may separate from third-base peers in runs, RBI, baserunning, or contact profile without producing the same level of park- and league-adjusted offensive value.

Art Devlin 1905 is the largest negative residual in this run. Pie Traynor 1922, Ossie Vitt 1915, and several other early-era or context-sensitive seasons also appear in the negative tail.

This is not surprising.

The farther back the data go, the more differences we expect between a transparent peer-z-score model and a modern run-value metric such as wRC+.

The residuals are not a failure of the model. They are a useful diagnostic tool.

Why This Season-Level Result Matters

This season-level validation is probably the cleanest offensive test in the project.

The WAR validation showed that the combined offense-defense model predicts total value.

The career wRC+ validation showed that average offensive z-score predicts career offensive quality.

But this season-level wRC+ validation is even more direct.

It compares:

\text{Season Offensive Z-Score}

to:

\text{Season } wRC^+

The result is strong:

R^2 = 0.692

That means Model C captures a substantial share of the same offensive signal captured by wRC+.

The defensive negative control confirms the interpretation:

R^2_{\text{Defense Only}} = 0.002

That is almost zero.

Offensive z-scores predict offense. Traditional defensive z-scores do not.

That is exactly the validation pattern we wanted.

How This Fits With the Earlier Validation Studies

The validation sequence now has three layers.

First, the WAR study showed that offense and traditional defense together predict total value:

R^2_{\text{Career WAR, Offense + Defense}} = 0.814

Second, the career-level wRC+ study showed that average offensive z-score predicts career offensive quality:

R^2_{\text{Career wRC+}} = 0.740

Third, this chapter shows that season-level offensive z-score predicts season-level wRC+:

R^2_{\text{Season wRC+}} = 0.692

Together, these results give the project a strong methodological foundation.

The z-score model is not WAR.

It is not wRC+.

It is a simpler and more transparent peer-separation model.

But it clearly captures real value-related information.

Limitations

This chapter should still be read carefully.

The FanGraphs season-level file matched almost all qualified third-base seasons, but not every season. The unmatched cases were mostly older Negro Leagues or historical ID records.

The Model C offensive score is not park-adjusted in the same way as wRC+. It is same-position and same-season adjusted through z-scores, but that is not identical to league and park adjustment.

Model C also includes runs and RBI rates, which are not purely individual batter skill measures. They can reflect lineup and team context.

Finally, wRC+ is itself a model. It is extremely useful, but it is not a perfect measure of all offensive contribution. It does not treat baserunning the same way Model C does, and it does not ask the same positional-peer question.

So the correct conclusion is not:

Model C is the same as wRC+.

The correct conclusion is:

Model C strongly predicts wRC+, while preserving a different interpretive question.

That is exactly what we want from a validation study.

Conclusion

The season-level wRC+ validation gives the clearest offensive support for the third-base z-score project.

The main model is:

wRC^+ = 101.47 + 5.86(\text{Model C Offensive Season Score})

The result is:

R^2 = 0.692

That means the offensive z-score model explains about 69 percent of the variation in FanGraphs season-level wRC+ among matched qualified third-base seasons.

The traditional defensive score explains almost none:

R^2 = 0.002

That negative-control result is crucial.

The offensive model predicts offense.

The defensive model does not.

The broader implication is clear.

The z-score system is not just an internal ranking device. It aligns strongly with established external value metrics.

WAR validates the two-dimensional model.

wRC+ validates the offensive model.

And the season-level wRC+ study confirms that Model C captures a real offensive signal year by year.

Do Third-Base Offensive Z-Scores Predict wRC+?

Introduction

The WAR validation chapter tested the full two-dimensional model.

It asked whether our third-base z-score framework could predict total player value. The answer was yes. Offensive z-scores predicted WAR. Traditional defensive z-scores added substantial explanatory power. The combined model performed especially well at the career level.

But WAR is broad.

WAR includes offense, defense, baserunning, positional adjustment, replacement level, and playing time. That makes it useful, but it also makes it complex. If the question is whether our offensive z-score model really measures offensive quality, WAR is not the cleanest validation target.

For that, we need an offense-only benchmark.

That is where wRC+ becomes useful.

FanGraphs wRC+ is designed to measure offensive production relative to league and park context, with 100 as league average. A 120 wRC+ means a hitter was about 20 percent better than league average. An 80 wRC+ means about 20 percent below league average.

So the validation question becomes simple:

Does our Model C offensive z-score predict FanGraphs wRC+?

The answer is yes.

Among third-base regulars with at least five qualified third-base seasons, the average Model C offensive score per qualified season explains a large share of career wRC+ variation:

wRC^+ = 100.89 + 5.41(\text{Model C Offensive Score per Qualified Season})

R^2 = 0.740

That is a strong relationship.

Just as important, the traditional defensive score does not meaningfully predict wRC+:

R^2 = 0.022

That negative-control result matters. It tells us that the offensive z-score model is not simply measuring general player quality. It is measuring offense.

Why wRC+ Is the Right Validation Target

The earlier WAR validation was a broad test.

It asked:

Do our offense-defense scores predict total value?

This chapter asks something narrower:

Does our offensive z-score predict an established offensive metric?

That is a cleaner test of Model C.

The offensive z-score model was built from same-position, same-season peer comparisons. It was not designed to reproduce wRC+. It does not directly use the same run-value formula. It does not include park adjustments in the same way. It includes runs and RBI, which wRC+ does not treat as independent batter skills in the same way. It includes baserunning through net stolen bases, while wRC+ is focused on hitting.

Even so, the relationship is strong.

That is useful validation.

It means Model C is not just producing interesting internal rankings. It is also aligned with an external offensive measure.

Data Used in the Study

The FanGraphs file used for this chapter was a career batting leaderboard export. Because the file was career-level rather than season-level, this first wRC+ validation is a career-level study.

The merge was very successful.

The third-base career dataset included: 897 third-base players

The FanGraphs wRC+ merge matched: 786 of 897 players

Among regular third basemen, defined as players with at least five qualified third-base seasons, the merge matched: 239 of 240 players

That gives us a strong sample for the validation test.

The main analysis focuses on the regulars because wRC+ is a rate statistic, and very short careers can create noisy results. A five-qualified-season cutoff helps identify players with enough third-base playing time to make the comparison meaningful.

The Model C Offensive Score

The offensive score used in this validation is the same Model C score used throughout the third-base study.

Model C uses seven offensive components:

OBP ISO BB/PA SO/PA, inverted Net SB/PA R/PA RBI/PA

The basic z-score formula is:

z = \frac{x - \mu}{\sigma}

Where:

x = \text{the player's value}

\mu = \text{the same-position, same-season peer-group mean}

\sigma = \text{the same-position, same-season peer-group standard deviation}

This equation asks a simple question:

How far above or below the third-base peer group was this player?

That is the core of the whole study.

Offensive Component Equations

On-base percentage is:

OBP = \frac{H + BB + HBP}{AB + BB + HBP + SF}

Slugging percentage is:

SLG = \frac{TB}{AB}

Isolated power is:

ISO = SLG - AVG

Walk rate is:

BB/PA = \frac{BB}{PA}

Strikeout rate is:

SO/PA = \frac{SO}{PA}

Net stolen bases are:

NetSB = SB - CS

Net stolen-base rate is:

NetSB/PA = \frac{SB - CS}{PA}

Run rate is:

R/PA = \frac{R}{PA}

RBI rate is:

RBI/PA = \frac{RBI}{PA}

The strikeout component is inverted because fewer strikeouts are better:

z_{\text{Low SO/PA}} = -\left( \frac{ (SO/PA)_i - \overline{(SO/PA)}_{\text{peer}} }{ s_{SO/PA,\text{peer}} } \right)

The full Model C offensive season score is:

\begin{aligned} \text{Season Score} &= z_{\text{OBP}} + z_{\text{ISO}} + z_{\text{BB/PA}} + z_{\text{Low SO/PA}} \\ &\quad + z_{\text{NetSB/PA}} + z_{\text{R/PA}} + z_{\text{RBI/PA}} \end{aligned}

This produces one offensive score for each qualified third-base season.

Playing-Time Weighting

The broader study uses a playing-time weight so that a partial season does not count the same as a full season.

The weight is:

w = \min\left(1, \frac{PA}{600}\right)

The weighted season score is:

\text{Weighted Offensive Season Score} = \text{Model C Offensive Season Score} \times w

The career offensive score is:

\text{Career Offensive Score} = \sum_{s=1}^{n} \text{Weighted Offensive Season Score}_s

This career score is cumulative. It rewards repeated separation from third-base peers.

But wRC+ is not cumulative. It is a rate-style offensive measure. That creates an important methodological issue.

Why We Use Average Offensive Score per Qualified Season

Because wRC+ is rate-based, the best predictor is not simply total career offensive score.

A player with many seasons can accumulate a large career score even if his average season was not historically great. Another player with fewer seasons can have a higher offensive level but a lower accumulated score.

So for this validation, the primary predictor is:

\text{Average Offensive Score} = \frac{ \text{Career Offensive Score} }{ \text{Qualified Third-Base Seasons} }

Or:

\text{Average Offensive Score} = \frac{ \sum_{s=1}^{n} \text{Weighted Offensive Season Score}_s }{ n }

Where:

n = \text{number of qualified third-base seasons}

This gives us an offensive quality measure rather than a pure accumulation measure.

That distinction matters.

The cumulative career offensive score still predicts wRC+, but not as well as the average score.

For third-base regulars:

Average offensive score per qualified season:
R² = 0.740

Cumulative career offensive score:
R² = 0.661

The average score is a better validation measure because it matches the rate-like nature of wRC+.

Regression Framework

The basic validation model is:

wRC^+_i = \alpha + \beta_1(\text{Average Offensive Score}_i) + \varepsilon_i

Where:

wRC^+_i = \text{FanGraphs career wRC+ for player } i

\alpha = \text{intercept}

\beta_1 = \text{effect of one additional average offensive z-score point}

\varepsilon_i = \text{residual error}

The fitted model for third-base regulars is:

wRC^+ = 100.89 + 5.41(\text{Average Offensive Score})

R^2 = 0.740

This means that each additional point of average Model C offensive score is associated with about 5.41 points of career wRC+.

A player with an average offensive score of 0 projects near league average:

wRC^+ = 100.89 + 5.41(0)

wRC^+ = 100.89

A player with an average offensive score of 3 projects as:

wRC^+ = 100.89 + 5.41(3)

wRC^+ = 117.12

A player with an average offensive score of 6 projects as:

wRC^+ = 100.89 + 5.41(6)

wRC^+ = 133.35

This is exactly the kind of relationship we hoped to see.

Figure 1: Model Comparison

Figure 1. How well third-base z-scores predict FanGraphs wRC+.

The first figure compares the validation models.

The most important result is:

R^2 = 0.740

for the average offensive score model among regular third basemen.

The cumulative offensive score also performs well:

R^2 = 0.661

But the average score is better because wRC+ is a rate metric.

The traditional defensive score performs very poorly as a wRC+ predictor:

R^2 = 0.022

That is not a problem. It is exactly what we want.

Defense should not predict wRC+ very well. If it did, that would suggest either a hidden confounding problem or a model that was mixing offensive and defensive signals.

The offense-plus-defense model is nearly identical to the offense-only model:

R^2 = 0.741

That small difference tells us that traditional defense adds almost nothing to the prediction of wRC+. Again, this strengthens the interpretation.

The offensive model predicts offense. The defensive model does not.

Figure 2: Average Offensive Z-Score Versus wRC+

Figure 2. Career wRC+ versus average offensive z-score among third-base regulars.

The second figure shows the main relationship directly.

The x-axis is:

\text{Model C Offensive Score per Qualified Third-Base Season}

The y-axis is:

wRC^+

The fitted line is:

wRC^+ = 100.89 + 5.41x

R^2 = 0.740

The upward trend is clear.

Players with high average offensive z-scores tend to have high career wRC+ values. Mike Schmidt, Chipper Jones, Eddie Mathews, George Brett, Wade Boggs, Dick Allen, and Al Rosen all sit in the upper-right region. Players with lower offensive z-score averages tend to have lower wRC+ values.

This is a strong validation of Model C.

The z-score model is not simply rewarding raw counting totals. It is recovering a meaningful offensive signal that corresponds closely to an established offensive metric.

Figure 3: Actual Versus Predicted wRC+

Figure 3. Actual versus predicted career wRC+ using the offense-only model.

The actual-versus-predicted plot shows how well the model estimates wRC+.

The prediction equation is:

\widehat{wRC^+} = 100.89 + 5.41(\text{Average Offensive Score})

The residual is:

\text{Residual}_i = wRC^+_i - \widehat{wRC^+}_i

Players near the diagonal are well predicted. Players above the diagonal have higher wRC+ than the model predicts. Players below the diagonal have lower wRC+ than the model predicts.

This figure shows that the model captures the broad structure very well, but it also shows useful outliers.

That is important.

The purpose of validation is not only to confirm that the model works. It is also to identify where it differs from an established metric.

Figure 4: The Defensive Negative Control

Figure 4. The traditional defensive score does not meaningfully predict wRC+.

The negative-control model is:

wRC^+ = \alpha + \beta_1(\text{Traditional Defensive Score per Qualified Season}) + \varepsilon

The fitted equation is:

wRC^+ = 105.34 - 1.49(\text{Traditional Defensive Score per Qualified Season})

R^2 = 0.022

This means traditional defense explains only about 2.2 percent of the variation in career wRC+ among regular third basemen.

That is a very small relationship.

This is one of the most important findings in the chapter. It shows that the validation is specific. Offensive z-scores predict offensive value. Traditional defensive z-scores do not.

The negative-control test strengthens the model.

It tells us that Model C is not simply identifying famous players or good players in general. It is identifying an offensive quality.

Figure 5: Residuals

Figure 5. Largest wRC+ residuals from the offensive z-score model.

The residual equation is:

\text{Residual}_i = wRC^+_i - \widehat{wRC^+}_i

Positive residuals mean the player’s FanGraphs wRC+ is higher than the z-score model predicts.

Negative residuals mean the player’s FanGraphs wRC+ is lower than the z-score model predicts.

The largest positive residuals include:

Edwin Encarnacion
David Freese
Dick Allen
Cal Ripken Jr.
Deacon White
Joe Torre
Larry Parrish
Wade Boggs

These players had higher wRC+ values than the offensive z-score model predicted.

The largest negative residuals include:

Ossie Vitt
Art Devlin
Jim Gilliam
Jose Ramirez
Billy Werber
Bob Jones
Chone Figgins
Hans Lobert

These players had lower wRC+ values than the model predicted.

The residuals are not merely mistakes. They show where the two systems differ.

Interpreting the Positive Residuals

Positive residuals are especially interesting because they identify players whose wRC+ is better than our average offensive z-score model expects.

For example, Edwin Encarnacion has a large positive residual. His career wRC+ is much stronger than his average third-base z-score profile suggests. This may reflect the fact that much of his offensive identity was formed outside a long traditional third-base career. Since our model focuses on qualified third-base seasons, while FanGraphs career wRC+ reflects his broader batting career, the comparison can produce differences.

David Freese also appears as a positive residual. His wRC+ is higher than expected from the third-base z-score model.

Dick Allen is another important case. He had enormous offensive quality, and his wRC+ remains higher than the model predicts, even though the model already rates him strongly.

Wade Boggs is also above prediction. That may reflect the way wRC+ values his on-base skill and batting quality more directly than a model that also includes runs, RBI, power, and baserunning components.

Interpreting the Negative Residuals

Negative residuals tell the opposite story.

Ossie Vitt is much lower in wRC+ than the offensive z-score model predicts. Art Devlin, Jim Gilliam, Jose Ramirez, Billy Werber, Bob Jones, Chone Figgins, and Hans Lobert also fall below prediction.

These cases require careful interpretation.

Some players may be rewarded in our Model C framework because they separated from their third-base peers in components that do not translate as strongly into wRC+. Runs, RBI, stolen-base value, and contact profile can influence the z-score model differently than wRC+.

Jose Ramirez is especially interesting. The model predicts a higher wRC+ than his current FanGraphs career mark. That may reflect his strong same-position separation across multiple components, including power, walks, baserunning, runs, and RBI. It may also reflect the fact that his career is still active.

A negative residual does not mean the z-score model is wrong. It means the z-score model and wRC+ are measuring offense from different angles.

That difference is analytically useful.

What the wRC+ Validation Shows

The wRC+ validation supports the offensive model in three ways.

First, the relationship is strong:

R^2 = 0.740

Second, the slope is meaningful:

\beta_1 = 5.41

That means each additional average offensive z-score point corresponds to about 5.41 points of wRC+.

Third, the negative-control test works:

R^2_{\text{Defense Only}} = 0.022

Traditional defense does not predict wRC+.

That is exactly what should happen if the model is behaving properly.

Why This Complements the WAR Validation

The WAR validation and wRC+ validation answer different questions.

The WAR validation asked:

Do offense and traditional defense together predict total value?

The answer was yes.

The career-level offense-plus-defense model for regular third basemen had:

R^2 = 0.814

The wRC+ validation asks:

Does the offensive z-score model predict offensive quality?

The answer is also yes.

The average offensive score model has:

R^2 = 0.740

Together, these two validation studies are stronger than either one alone.

WAR validates the broader two-dimensional structure.

wRC+ validates the offensive dimension specifically.

The negative control confirms that the defensive dimension is not pretending to be offense.

This gives the project a stronger methodological foundation.

What the Study Does Not Prove

This chapter should not be overread.

It does not prove that Model C is better than wRC+. It does not prove that wRC+ is perfect. It does not prove that every residual is meaningful. It does not prove that the z-score model captures park effects, full run values, league quality, or all contextual differences.

The FanGraphs file used here is at the career level. That means this chapter does not yet test season-by-season wRC+ against season-by-season z-scores.

A season-level wRC+ study would be even cleaner because it would compare:

\text{Season Offensive Z-Score}

directly against:

\text{Season } wRC^+

That should be the next step if we obtain a season-level FanGraphs export.

For now, this chapter provides strong career-level validation.

Conclusion

The wRC+ validation study answers a direct question:

Do third-base offensive z-scores predict an established offensive metric?

Yes.

Among third-base regulars, the average Model C offensive score per qualified season strongly predicts FanGraphs career wRC+:

wRC^+ = 100.89 + 5.41(\text{Average Offensive Score})

R^2 = 0.740

The cumulative offensive score also predicts wRC+, but less strongly:

R^2 = 0.661

Traditional defense does not meaningfully predict wRC+:

R^2 = 0.022

That is exactly the pattern we wanted.

The offensive model predicts offense.

The defensive model does not.

The combined validation framework now has both breadth and specificity.

The WAR study showed that offense plus defense predicts total value.

The wRC+ study shows that the offensive z-score model predicts offensive quality.

That is a major validation result for the third-base project.

Below the Line: The Lowest-Scoring Qualified Offensive Third Basemen

Introduction

The earlier chapters looked at greatness.

They asked which third basemen separated most strongly from their positional peers. That led naturally to players such as Mike Schmidt, Chipper Jones, Eddie Mathews, George Brett, Wade Boggs, Jose Ramirez, and others. Those players live in the upper tail of the distribution. They are the positive outliers.

The previous chapter reversed the question and studied the center. It asked which third basemen were most average, which players sat closest to the offensive norm of the position.

This chapter moves to the other side.

It asks: Which qualified third basemen were farthest below the offensive standard of their own positional peers?

That question is not the same as asking who the worst third basemen were. This study is offense-only. It does not include defense, throwing, range, durability beyond qualification, leadership, baserunning beyond the offensive variables included in Model C, postseason value, or WAR. A player could score poorly here and still have had defensive value. He could have stayed in the lineup because of glove work, team need, positional scarcity, reputation, or organizational context.

For that reason, the most accurate wording is: lowest-scoring qualified offensive third basemen.

That wording is important. All these men were professional athletes. Many of us would love to be on this list.

The results are still interesting. In the combined Model A and Model C framework, the lowest-scoring multi-season offensive third baseman is Ken Reitz. He is followed by Aurelio Rodriguez, Charley Smith, Ke’Bryan Hayes, Lee Tannehill, Pedro Feliz, Bob Aspromonte, Bubba Phillips, Placido Polanco, and Frank O’Rourke.

The single-season list is different. The lowest Model C third-base season belongs to Jimmy Austin in 1912, followed by Chris Truby in 2002, Matt Dominguez in 2014, Chris Johnson in 2014, Eddie Mulligan in 1921, and Billy Purtell in 1910.

Together, these lists ask a deeper baseball question: How can a player qualify repeatedly while remaining far below the offensive center of his position?

The answer is likely found in the parts of the game this model does not measure.

The Framework

The same basic scoring system used in the dominance chapters is used here.

Each player-season is compared only to other qualified third basemen from the same season. This means a third baseman from 1912 is not directly compared to one from 2014. Each player is judged against the offensive expectations of his own season and position.

The basic z-score equation is:

Where:

A z-score of zero means the player was exactly average in that category. A positive z-score means he was above average. A negative z-score means he was below average.

The Model A season score is:

Model A emphasizes on-base skill, slugging, home-run rate, walks, runs, and RBI.

The Model C season score is:

Model C uses a broader offensive framework. It includes isolated power, walks, contact, net stolen-base value, run scoring, and RBI production.

Isolated power is:

Net stolen-base rate is:

The strikeout component is inverted because fewer strikeouts are better:

The raw score is then weighted by playing time:

For the lowest-scoring study, the logic is simple.

In the dominance chapters, higher scores were better.
In this chapter, lower scores identify weaker offensive separation.

A strongly negative season score indicates the player was far below the third-base peer group across all categories.

Measuring Multi-Season Weakness

Single seasons can be strange. A player can have one unusually poor season due to injury, age, bad luck, or a temporary collapse.

A multi-season regular is different.

For that reason, this chapter also calculates average season score for players with at least five qualified third-base seasons.

The career average is:

where (n) is the number of qualified seasons.

The combined Model A and Model C average score is:

This combined score identifies players who were low-scoring under both definitions of offense.

That is important because a player might look poor under one model but less poor under another. The combined list is stricter. It asks whether players who remained far below average were defined differently under the Model A power/run-production framework or the broader Model C framework.

The Lowest-Scoring Multi-Season Third Basemen

The combined Model A and Model C results identify the lowest-scoring multi-season third basemen.

Rank	Player	Years	Qualified Seasons	Combined Avg. Score
1	Ken Reitz	1973–1980	8	-5.28
2	Aurelio Rodriguez	1969–1980	12	-4.69
3	Charley Smith	1961–1967	5	-4.49
4	Ke’Bryan Hayes	2021–2025	5	-4.01
5	Lee Tannehill	1904–1909	5	-3.90
6	Pedro Feliz	2004–2010	7	-3.81
7	Bob Aspromonte	1962–1971	8	-3.69
8	Bubba Phillips	1957–1963	6	-3.59
9	Placido Polanco	2001–2013	6	-3.29
10	Frank O’Rourke	1926–1930	5	-3.23
11	Chris Johnson	2010–2014	5	-3.11
12	Maikel Franco	2015–2022	7	-2.97
13	Ed Sprague	1993–1999	7	-2.96
14	Ray Knight	1979–1987	7	-2.92
15	Enos Cabell	1976–1982	6	-2.87

Ken Reitz is the most prominent result. His combined average score of -5.28 is far below the third-base peer baseline. He ranked first in the Model A low-score list and second in the Model C low-score list. That means his offensive weakness was not a product of one particular model. It appeared under both definitions.

Aurelio Rodriguez is second. His result is especially notable because he had twelve qualified third-base seasons in the study. That is a long run. A player who qualifies that often is doing something valuable enough to stay in the lineup. In this case, the value almost certainly lies outside this offensive model.

Charley Smith ranks third overall and first in Model C alone. That makes him one of the clearest examples of a player whose broad offensive profile sat far below the third-base baseline.

Ke’Bryan Hayes ranks fourth in the combined list through 2025. That is a striking modern result. It should be interpreted carefully because he is still an active player, and his defensive reputation is not part of the model. In fact, Hayes is a useful reminder of why this chapter must remain offense-only. A low offensive score does not equal low total player value.

Pedro Feliz, Bob Aspromonte, Bubba Phillips, Placido Polanco, and others reinforce the same point. Several of these players had reputations or roles that extended beyond offensive production. The model captures only their offensive separation from third-base peers.

The Lowest-Scoring Model C Regulars

Model C alone gives a slightly different list.

The top ten lowest-scoring Model C third-base regulars are:

Rank	Player	Years	Qualified Seasons	Avg. Model C Score
1	Charley Smith	1961–1967	5	-5.41
2	Ken Reitz	1973–1980	8	-4.95
3	Aurelio Rodriguez	1969–1980	12	-4.52
4	Lee Tannehill	1904–1909	5	-3.93
5	Pedro Feliz	2004–2010	7	-3.63
6	Jim Presley	1985–1990	6	-3.46
7	Chris Johnson	2010–2014	5	-3.40
8	Ed Sprague	1993–1999	7	-3.16
9	Bob Aspromonte	1962–1971	8	-3.14
10	Ke’Bryan Hayes	2021–2025	5	-3.03

Model C pushes Charley Smith to the top. It also moves Jim Presley into the top ten, while some players who looked especially poor under Model A fall slightly.

This is significant because Model C includes low strikeout rate and net stolen-base value. A player who was weak under Model A might recover somewhat in Model C if he made contact, ran well for the position, or contributed in ways not captured by slugging and home-run rate. Conversely, a player can look worse in Model C if he lacks those broader offensive contributions.

The Model C list therefore does not simply duplicate Model A. It identifies players whose offensive weakness remained visible even when the model became broader.

The Lowest-Scoring Individual Seasons

Single-season results tell a different story.

The lowest-scoring Model C third-base seasons are:

Rank	Player-Season	Model C Score
1	Jimmy Austin, 1912	-8.69
2	Chris Truby, 2002	-8.47
3	Matt Dominguez, 2014	-8.12
4	Chris Johnson, 2014	-7.83
5	Eddie Mulligan, 1921	-7.76
6	Billy Purtell, 1910	-7.65
7	Jose Hernandez, 2003	-7.43
8	Ray Knight, 1987	-7.19
9	Terry Pendleton, 1996	-7.16
10	Todd Cruz, 1983	-7.16
11	Travis Jackson, 1936	-7.11
12	Brooks Robinson, 1958	-7.10
13	Brandon Drury, 2019	-7.07
14	Charley Smith, 1965	-7.04
15	Pete Suder, 1941	-7.01

Jimmy Austin’s 1912 season is the lowest Model C third-base season in the dataset. Chris Truby’s 2002 season is close behind. Matt Dominguez and Chris Johnson both appear in 2014, suggesting that the modern third-base peer group that year set a difficult offensive baseline for weaker performers.

The single-season list includes some surprising names. Brooks Robinson appears for 1958. Terry Pendleton appears for 1996. Ray Knight appears for 1987. Travis Jackson appears for 1936. These are reminders that a poor offensive season does not define a player’s entire career. A great defender, a former star, an aging veteran, or a player with a different value profile can still appear on a low offensive season list.

That is why the chapter separates seasons from regulars.

A bad season is a moment.
A low multi-season score is a pattern.

Model A Versus Model C: Agreement and Disagreement

The next question is whether the two models agree about the lowest-scoring third-base regulars.

The relationship between Model A average score and Model C average score is positive but not especially strong: R²=0.218

This is an important result. The two models do not agree perfectly on offensive weakness.

Some players are poor under both definitions. Ken Reitz, Aurelio Rodriguez, Lee Tannehill, Pedro Feliz, Bob Aspromonte, and Bubba Phillips fall into this group. Their low scores are relatively stable.

Other players are more model-sensitive. Placido Polanco, for example, ranks fourth on the Model A low-score list but thirty-fourth on the Model C low-score list. That means Model C saw more offensive value in his broader profile than Model A did. Enos Cabell shows a similar pattern, ranking seventh in Model A but forty-sixth in Model C.

Ke’Bryan Hayes is another interesting case. He ranks second in Model A and tenth in Model C. Model C does not erase the offensive weakness, but it makes it less extreme.

Charley Smith moves in the opposite direction. He ranks tenth in Model A but first in Model C. That suggests his broader offensive profile was even weaker than his Model A profile.

The low () is therefore not a problem. It is informative. It shows that offensive weakness, like offensive greatness, depends partly on how offense is defined.

Why Did These Players Qualify?

This is the baseball question beneath the numbers.

If these players scored so poorly on offense, why did they qualify for multiple seasons?

The answer is almost certainly that teams were not evaluating them by this offensive model alone.

Several explanations are possible.

First, some players had defensive value. Third base requires reaction time, arm strength, and infield skill. A weak hitter could remain in the lineup if he saved runs with the glove. Ken Reitz, Aurelio Rodriguez, Pedro Feliz, Ke’Bryan Hayes, and Brooks Robinson all remind us that third base has never been purely an offensive position.

Second, offensive expectations change by era. A third baseman who looks weak in one period may have been more tolerable because the league or position valued defense more heavily. The same-season peer adjustment controls for the offensive environment, but it does not control for managerial tolerance or roster construction.

Third, some players may have held jobs because of scarcity. Teams need someone to play third base every day. A club may accept weak offense if the alternatives are worse, injured, inexperienced, or defensively unplayable.

Fourth, reputation matters. Veterans sometimes continue to receive playing time after their offense declines. Single-season low scores often capture this. A player can be valuable earlier in his career and still produce a very poor qualified season later.

Fifth, team context matters. A weak-hitting third baseman on a strong offensive team may be easier to carry than the same player on a weak offensive team.

This is what makes the low-score study valuable. It does not merely identify poor offensive performances. It points toward the hidden parts of player value and team decision-making.

The Ethics of the Label

A chapter like this needs careful language.

It would be easy to call these players “the worst third basemen.” That would be inaccurate.

The model measures only offense. It does not measure defense. It does not measure total value. It does not measure WAR. It does not measure the reasons a manager kept writing a player’s name into the lineup.

A better phrase is:

lowest-scoring qualified offensive third basemen

or:

the weakest offensive third-base regulars in this peer-adjusted framework

That phrasing keeps the result honest.

Ken Reitz may rank first here, but the statement is not “Ken Reitz was the worst third baseman.” The statement is:

Among players with at least five qualified third-base seasons, Ken Reitz had the lowest combined average offensive score in the Model A and Model C framework.

That is precise.

Precision matters, especially when the result is negative.

Comparison With Averageness

This chapter also helps clarify the difference between average and weak.

The previous chapter identified Casey Blake as the most average combined third-base regular. Blake was close to the center of the third-base offensive distribution. His profile was neither strongly positive nor strongly negative.

Ken Reitz is different. He was not centered. He was far below the offensive center. His negative average score means he repeatedly trailed his third-base peers across the model categories.

The distinction can be summarized this way:

Casey Blake = closest to the center

Ken Reitz = farthest below the center among multi-season regulars

Mike Schmidt = farthest above the center

That gives the third-base study a complete structure.

Dominance.
Averageness.
Weakness.

All three are relative to the same positional baseline.

What This Adds to the Larger Study

The low-score chapter adds an important dimension to the project.

The dominance chapters showed the upper tail. The average chapter showed the center. This chapter shows the lower tail.

Together, they make the distribution visible.

A position is not defined only by its stars. It is also defined by the players who stayed in the lineup despite weak offense. Those players reveal the position’s tolerance limits. They show where defense, reputation, scarcity, and roster construction may have mattered enough to overcome poor offensive production.

At third base, the lower tail includes both obscure names and recognizable ones. It includes long-career regulars, defensive specialists, aging veterans, and players with uneven offensive records. That variety makes the list more interesting than a simple ranking of failure.

The numbers identify the pattern. Baseball history explains why the pattern existed.

Conclusion

The lowest-scoring third-base study completes the first full positional distribution.

The main results are:

Lowest combined multi-season offensive regular: Ken Reitz

Lowest Model C multi-season offensive regular: Charley Smith

Lowest Model C individual third-base season: Jimmy Austin, 1912

Most notable modern low-score regular: Ke’Bryan Hayes

Most important caution: defense and WAR are not included

The results should be interpreted carefully. This is not a list of the worst third basemen in total value. It is a list of the lowest-scoring qualified offensive third basemen within this peer-adjusted framework.

That distinction makes the chapter stronger.

The most interesting question is not merely who scored lowest. It is why they played. A player who repeatedly qualifies despite weak offense must have offered something else, or must have occupied a context in which the team accepted the offensive cost.

That is where the baseball story begins.

The numbers show the lower tail.
The roster decisions explain why the lower tail existed.

Third base now has three points of reference:

Mike Schmidt: the upper tail

Casey Blake: the center

Ken Reitz: the lower tail

Together, they describe the full offensive shape of the position.

The Center of Third Base: Measuring the Most Average Regulars

Introduction

Most baseball analysis begins at the edges, the extremes of performance.

We study the greatest players, the worst players, the outliers, the records, the peaks, the collapses, and the seasons that do not seem to belong to ordinary baseball history. This is natural. The edges of the distribution are dramatic. They are diagnostic. They produce arguments.

But a distribution has a center. If the earlier chapters asked which third basemen stood farthest above their positional peers, this chapter asks a different question:

Which third basemen were closest to the positional norm?

This is not the same as asking who was mediocre. It is not the same as asking who was bad. A player can be a qualified major-league regular and still sit near the offensive center of his position. In fact, that is the point. The most average regulars are not failures. They are the players who define what “normal” looked like for a position.

For third base, this produces a surprisingly interesting result. The most average multi-season third baseman in the combined Model A and Model C framework is Casey Blake. Other names near the top include Edwin Encarnacion, Phil Garner, Ty Wigginton, Roy Howell, Tony Boeckel, Ossie Bluege, Todd Zeile, Ken McMullen, and Willie Jones.

That list does not look like a Hall of Fame ballot. It looks like something more useful for this chapter: the position’s working center.

The study of dominance tells us who escaped the ordinary. The study of averageness tells us what the ordinary was.

The Logic of Averageness

The earlier dominance models were built from z-scores. A z-score measures how far a player is from the same-season positional average in a given category.

The basic formula is:

Where:

A player with a z-score of zero is exactly average in that category. A player with a z-score of +1 is one standard deviation above average. A player with a z-score of -1 is one standard deviation below average.

The dominance chapters cared about positive separation. The higher the combined z-score, the more dominant the player-season.

Averageness reverses the question.

Instead of asking:

How far above average was this player?

It asks:

How close to average was this player?

That requires a Euclidean distance measure.

A player can be above average in one category and below average in another. For example, a third baseman might walk more than average but hit for less power. Another might hit for average power but run less than average. The question is not whether the z-scores are positive or negative. The question is how far they are from zero.

To measure that distance, this chapter uses a Typicality Score.

The Typicality Equation

For a player-season with (k) z-score components, the Typicality Score is:

This is the root-mean-square distance from zero.

Lower is more average.

A score of 0 would mean the player was exactly average in every category. In practice, no real player-season does that. But the closer the score is to 0, the closer that season is to the offensive center of the position.

For Model C, the categories are:

OBP

ISO

BB/PA

Low SO/PA

Net SB/PA

R/PA

RBI/PA

So the Model C Typicality Score is:

For Model A, the categories are:

OBP

SLG

HR/PA

BB/PA

R/PA

RBI/PA

So the Model A Typicality Score is:

This creates two ways of measuring averageness. Model A measures closeness to the traditional power, patience, and run-production center. Model C measures closeness to a broader offensive-skill center.

Because both models have value, this chapter also uses a combined measure:

This combined score identifies players who were average under both definitions.

Seasons and Careers Are Different Questions

There are two kinds of averageness.

The first is single-season averageness. This asks which individual player-season was closest to the same-season positional norm.

The second is multi-season regular averageness. This asks which players stayed close to the positional norm over multiple qualified seasons.

Those questions are not the same.

A player can have one perfectly ordinary season and then never repeat it. Another player can have six, eight, or eleven qualified seasons that all sit near the positional center. The second player is more interesting for the idea of a “typical regular.”

For that reason, this chapter uses a minimum standard for regulars:

At least five qualified third-base seasons

The single-season list tells us which seasons were most typical.
The regular list tells us which careers were most typical.

The Most Average Third-Base Seasons

The first result looks only at Model C. Model C is useful here because it includes the broadest offensive shape: on-base ability, isolated power, walks, contact, baserunning, run scoring, and RBI rate.

The most average Model C third-base season was Lonnie Chisenhall in 2014, with a Typicality Score of 0.218. That is extremely close to the center of the third-base offensive distribution.

The top ten Model C third-base seasons were:

Rank	Player-Season	Model C Typicality
1	Lonnie Chisenhall, 2014	0.218
2	Casey Blake, 2008	0.229
3	Rico Petrocelli, 1973	0.251
4	Charlie Reilly, 1897	0.254
5	Manny Machado, 2019	0.261
6	Hubie Brooks, 1984	0.266
7	Ken Caminiti, 1989	0.270
8	Evan Longoria, 2015	0.273
9	Ossie Bluege, 1928	0.273
10	Red Smith, 1912	0.274

This list immediately shows why “average” should not be used as an insult. Manny Machado and Evan Longoria appear in the top ten. These were not meaningless players. They were established major-league third basemen whose specific seasons happened to sit close to the offensive center of the position.

That is an important distinction. Averageness is not career quality. It is a shape. It is in proximity to the positional baseline.

Chisenhall’s 2014 season is a good example. His OBP, isolated power, walk rate, strikeout profile, baserunning contribution, runs, and RBI rate were all close to what qualified third basemen were doing in that season. No one category pulled him far from the center. That is what the Typicality Score captures.

Casey Blake’s 2008 season is also important because Blake later becomes the leading combined multi-season regular. His appearance near the top of the single-season list is not accidental. Blake’s offensive profile repeatedly hovered near the middle of the third-base distribution.

The Most Average Multi-Season Third Basemen

Single seasons are interesting, but the more important question is sustained averageness.

For multi-season regulars, the combined Model A and Model C ranking is the best main list. It identifies players who were not merely average under one offensive definition, but close to the positional center under both.

The top fifteen combined third-base regulars were:

Rank	Player	Years	Qualified Seasons	Combined Avg. Typicality
1	Casey Blake	2003–2010	6	0.521
2	Edwin Encarnacion	2006–2010	5	0.529
3	Phil Garner	1977–1986	7	0.546
4	Ty Wigginton	2003–2011	5	0.551
5	Roy Howell	1975–1980	6	0.555
6	Tony Boeckel	1919–1923	5	0.556
7	Ossie Bluege	1923–1933	10	0.559
8	Todd Zeile	1991–2003	11	0.562
9	Ken McMullen	1965–1972	8	0.573
10	Willie Jones	1949–1959	11	0.576
11	Steve Buechele	1986–1994	8	0.585
12	Tom Burns	1886–1890	5	0.593
13	Andy High	1922–1929	7	0.620
14	Charlie Irwin	1894–1902	8	0.628
15	Rico Petrocelli	1971–1975	5	0.628

The headline is simple:

Casey Blake is the most average multi-season third baseman in the combined Model A and Model C framework.

Again, that should not be read as an insult. Blake was a useful major-league player. His ranking means that, across his qualified third-base seasons, he stayed unusually close to the offensive center of the position.

That is a different kind of regularity.

The same can be said for Todd Zeile and Willie Jones. Both had long third-base regular profiles. Their inclusion is especially useful because they were not merely one-season accidents. Zeile had eleven qualified seasons in this framework. Willie Jones also had eleven. They represent sustained proximity to the positional norm.

Ossie Bluege is another useful case. He had ten qualified third-base seasons and ranks seventh in the combined list. That suggests a long career near the center of the third-base offensive distribution.

The top of the list is not dominated by one era. It includes nineteenth-century players, early twentieth-century players, mid-century players, and modern players. That matters because the method compares each player only to his same-season positional peers. A player from 1897 is not being compared directly to a player from 2008. Each is being compared to the third-base norm of his own season.

Model A Versus Model C Averageness

The combined list is useful because a player can be average under one model but less average under another.

The next figure compares Model A average typicality with Model C average typicality for third-base regulars.

The diagonal line represents equal typicality under both models. Players near the lower-left are the most average under both definitions. Players far from the line are more average under one model than the other.

This figure helps explain why Casey Blake leads the combined list. He is not merely low in Model A or low in Model C. He sits near the low end of both.

Edwin Encarnacion is different. He ranks first in Model C regular typicality but ninth in Model A. That means his early third-base seasons were especially average under the broader Model C framework, but somewhat less centered under the Model A power/run-production framework.

Roy Howell is the reverse case. He ranks second in Model A but twentieth in Model C. That suggests he looked very typical under the Model A categories but less so once Model C added contact and net stolen-base value.

Todd Zeile is an interesting middle case. He ranks third in Model C regular typicality and twelfth in Model A, resulting in a strong combined ranking. His long qualified window makes him one of the better examples of a sustained third-base regular near the offensive center.

This is why two models help. Averageness, like dominance, depends on definition. A player can be average in a power model and less average in a broader model, or the reverse.

The combined measure rewards players who remain near the center regardless of the offensive lens used.

Season Averageness Versus Career Profile Averageness

There is one more distinction worth making.

A player’s average season Typicality Score measures how close his seasons were to average, season by season.

But we can also ask about his career profile. This takes each player’s average z-score profile across his qualified seasons and then calculates the distance of that career profile from zero.

In simplified form:

Then:

This is slightly different from the typicality of the average season. A player might have individual seasons that vary above and below average, but cancel each other out over a career. Another player might be consistently a little above average in one category and below average in another.

The next figure compares these two forms of averageness.

The lower-left region is the ideal location for sustained averageness. Players there were not only close to average season by season, but their career profiles also remained close to the center.

Casey Blake, Edwin Encarnacion, Todd Zeile, Ken McMullen, Willie Jones, Tony Boeckel, and Ty Wigginton all sit in the more typical region. This strengthens the conclusion that they were not merely average by one mathematical accident. Their overall profiles were also close to the third-base center.

This distinction could become important in later positional chapters. Some players may be average because their strengths and weaknesses cancel across a career. Others may be average because each individual season is consistently centered. Those are subtly different forms of ordinary.

Why Casey Blake Leads

Casey Blake’s combined result is useful because it feels intuitively plausible.

He was a solid regular, not a star. He had power, but not elite power. He walked some, struck out some, drove in runs, scored runs, and occupied third base without becoming a positional outlier. His offensive profile was useful, but it did not pull hard toward any extreme.

That is exactly what the Typicality Score is designed to find.

Blake’s 2008 season was the second-most average Model C third-base season in the dataset. Across his qualified third-base seasons from 2003 to 2010, he ranked first in Model A regular typicality and tenth in Model C regular typicality. Combined, that made him the leading multi-season third-base regular.

In plain language:

Casey Blake was not the greatest third baseman in the study. He was the most third-base-like third baseman.

That is a different kind of distinction.

It is also an important one.

What the List Tells Us About Third Base

The third-base averageness list helps define the offensive center of the position.

It suggests that the typical qualified third baseman was not simply a slugger. Third base has often been treated as a power position, but the center of the distribution includes a mixture of moderate power, moderate on-base ability, moderate run production, and limited but not absent baserunning contribution.

Players near the top of the average list tend to be competent regulars rather than specialists. They are not extreme walkers, extreme sluggers, extreme contact hitters, or extreme baserunners. They are balanced enough to qualify, but not strong enough in one direction to separate dramatically from the peer group.

That gives the averageness study a useful interpretive role. The dominance chapters tell us what greatness at third base looked like: Schmidt, Chipper, Mathews, Jose Ramirez, Brett, Boggs, and others.

This chapter tells us what the center looked like: Blake, Zeile, Garner, Bluege, McMullen, Wigginton, and others.

The two ideas need each other.

Without the center, dominance has no reference point.
Without the outliers, the center has no contrast.

Average Does Not Mean Replaceable

It is important not to confuse average with replacement level.

The players in this chapter were qualified regulars. They met playing-time and positional thresholds. That means they were good enough to hold major-league jobs and play substantial time at third base.

Average among qualified regulars is not the same thing as average among all possible players. The pool has already been filtered. These are not random minor leaguers or bench players. They are major-league third basemen who played enough to qualify in the study.

That makes the term “average” more meaningful.

A qualified average regular has value. He gives a team stability. He fills a position. He avoids collapse. He may not define greatness, but he helps define the league.

In that sense, this chapter is not about mediocrity. It is about the structure of normal professional competence.

Conclusion

The third-base averageness study reverses the logic of the dominance chapters.

Instead of asking who stood farthest above the positional norm, it asks who stood closest to it.

The main results are:

Most average Model C third-base season: Lonnie Chisenhall, 2014

Most average combined Model A / Model C regular: Casey Blake

Best long-career examples of third-base averageness: Todd Zeile, Willie Jones, Ossie Bluege

The most important conclusion is not simply that Casey Blake ranks first. The deeper conclusion is that averageness can be measured, and that it reveals a different part of baseball history.

Great players define the limits of the game.
Average regulars define the middle of the game.

The middle is not glamorous. It does not usually produce monuments. But it is where the position lives most of the time.

Third base, in this framework, is not only Schmidt, Chipper, Mathews, and Brett. It is also Casey Blake, Todd Zeile, Willie Jones, Phil Garner, Ossie Bluege, and Ken McMullen.

They are the center of the distribution, and without the center, there is no such thing as an outlier.

Model C and the Third-Base Question: Schmidt Still Wins, But the Argument Changes

The original third-base study produced a clean and stunning result.

Mike Schmidt was the offensive winner.

Using Model A, which emphasized OBP, SLG, HR/PA, BB/PA, R/PA, and RBI/PA, Schmidt separated clearly from the rest of the field. Eddie Mathews, Chipper Jones, Ron Santo, Home Run Baker, Alex Rodriguez, and others formed the next group, but Schmidt stood alone at the top.

That result made intuitive sense. Schmidt’s combination of power, walks, run production, and long career fit the Model A framework almost perfectly.

But Model A was only one way to define offensive dominance.

It rewarded power and run production heavily. It also included some overlap because SLG and HR/PA both capture parts of the power profile. That does not make the model suspect; it just means the model has a particular shape.

So the next step was a Sensitivity Test.

What happens if we broaden the definition of “offensive”?

That is the purpose of Model C. Yes, there was a Model B, but it was deemed too similar to the initial model (Model A) and was scrubbed.

The Model C Framework

Model C keeps the basic structure of the original study. A third baseman is still compared only to other third basemen in the same season. The qualification rules are unchanged:

At least 50 games at third base

At least 300 plate appearances

Same-year third-base peer group

The difference is in the offensive categories.

Model C uses:

OBP

ISO

BB/PA

SO/PA, inverted

Net SB/PA

R/PA

RBI/PA

This changes the question.

Model A asked: who dominated through on-base ability, slugging, home-run rate, walks, runs, and RBI?

Model C asks something broader: who combined on-base skill, isolated power, plate discipline, contact, baserunning value, and run production?

The key change is that Model C replaces SLG and HR/PA with ISO, adds strikeout avoidance, and adds net stolen-base value. A lower strikeout rate is considered better. Net stolen bases are calculated as stolen bases minus caught stealing, scaled by plate appearances.

This is still not a complete offensive-value model. It does not calculate linear weights or wRC+. But it is broader than Model A and less redundant in its treatment of power.

Figure 1: Model C Career Offensive Dominance

The headline result is simple:

Mike Schmidt still wins.

But the margin is dramatically smaller.

Schmidt finishes first with a Model C career score of 112.9. Chipper Jones is almost even at 112.1. That is not a landslide. That is essentially a photo finish.

The top five:

Rank	Player	Career Score
1	Mike Schmidt	112.9
2	Chipper Jones	112.1
3	Eddie Mathews	90.4
4	Jose Ramirez	78.6
5	George Brett	72.1

This is a major shift in the argument’s structure.

Schmidt remains the career winner, but Chipper Jones becomes a much stronger challenger. That makes sense. Chipper’s game was better suited to Model C. He retained his on-base and walk advantages, but he benefited from better contact and a more rounded offensive profile.

Schmidt, meanwhile, remained powerful and patient, but Model C penalized his strikeout rate. He still wins because his positives are enormous. But the broader model narrows the gap.

Eddie Mathews remains third, which is important. He was not just a Model A power beneficiary. His overall offensive separation still survives the broader test.

Then comes the biggest modern movement: Jose Ramirez.

Jose Ramirez Changes the Peak Argument

Jose Ramirez ranks fourth in career score, but that understates what Model C does for him. His career is still ongoing, and the model is already placing him among the most important offensive third basemen in the study.

The reason is straightforward. Ramirez is not just a slugger. He brings power, plate discipline, low strikeouts, baserunning value, runs, and RBI. Model C rewards that broader offensive footprint.

That becomes even clearer in the peak chart.

Figure 2: Model C Seven-Season Peaks

This may be the most important figure in the Model C third-base study.

Jose Ramirez has the highest seven-season peak score among third basemen:

Rank	Player	Peak 7 Score
1	Jose Ramirez	71.3
2	Chipper Jones	69.1
3	Mike Schmidt	62.3
4	Alex Rodriguez	60.9
5	George Brett	58.9
6	Eddie Mathews	57.4

That is a very different story from Model A.

Under the original framework, Schmidt was the clean career and power-dominance winner. Under Model C, Ramirez becomes the peak leader. Chipper Jones becomes the balanced-score leader. Schmidt remains the career-score leader.

So third base no longer has one simple answer.

It has three answers, depending on what we are asking:

Career Score: Mike Schmidt

Peak 7 Score: Jose Ramirez

Balanced Score: Chipper Jones

That does not overturn Schmidt’s case. It refines it.

Schmidt is still the long-career offensive dominance winner. But Model C shows that the broader-skill version of the third-base argument is much more open than Model A suggested.

Figure 3: Who Moved Most From Model A to Model C?

The rank-change figure shows how different the models are.

Some players rise sharply because Model C rewards contact, baserunning, and broad offensive production. Others fall because Model A rewarded power and run production more directly.

The most meaningful risers near the top of the list are:

Jose Ramirez

Wade Boggs

David Wright

George Brett

Scott Rolen

Chipper Jones

Jose Ramirez moves from 18th in Model A to 4th in Model C.

Wade Boggs moves from 31st to 9th.

David Wright moves from 21st to 10th.

Those changes are not random. They reveal what Model C values.

Boggs, for example, was never going to thrive in a model strongly shaped by home-run rate and slugging. But once OBP, contact, and broader offensive profile become more important, he rises dramatically.

Ramirez rises because he is almost the perfect Model C player: power, speed, low strikeouts, net stolen-base value, and run production.

Chipper Jones rises because his offensive profile is more balanced than Schmidt’s. He does not overwhelm the model with one trait. He scores well across many of them.

Some power-heavy players fall. That is also expected. Model C does not ignore power, but it no longer lets power dominate the same way.

Figure 4: Career Versus Peak

The career-versus-peak scatterplot shows the new shape of third-base greatness.

Schmidt and Chipper Jones sit farthest to the right. They are the two great career cases. But Chipper is higher on the y-axis, meaning he has the better seven-season peak under Model C.

Jose Ramirez sits above both of them in peak value, though not as far right in career value. That is exactly what we would expect from an active player with a concentrated run of broad-skill excellence.

Eddie Mathews remains strong on both axes. Alex Rodriguez is also high on peak, though his third-base career is shorter than Schmidt’s, Chipper’s, or Mathews’s.

George Brett sits in an interesting middle space. He does not quite match Schmidt or Chipper in career score, but Model C treats him more favorably than Model A because his profile includes batting average, contact, OBP, and run production.

The scatterplot makes the third-base debate more nuanced.

Schmidt is still the career answer.
Chipper is the balance answer.
Ramirez is the peak answer.
Mathews remains the great historical power anchor.
Brett becomes more visible in a broader offensive model.

That is exactly what a sensitivity test should do. It should not simply repeat the original result. It should show where the result is stable and where it depends on the definition.

Figure 5: Best Third-Base Seasons Under Model C

The single-season list also changes.

The top Model C third-base season is Miguel Cabrera in 2013, with a score of 14.8. That was already a huge season under Model A, and it remains enormous under Model C.

The top five:

Rank	Player	Year	Score
1	Miguel Cabrera	2013	14.8
2	Chipper Jones	1999	14.1
3	Jose Ramirez	2018	13.9
4	George Brett	1985	13.8
5	Alex Rodriguez	2007	13.3

This list is revealing.

Cabrera’s 2013 season survives because it was not merely a power season. It combined elite OBP, strong ISO, run production, and enough overall separation to remain first.

Chipper’s 1999 season ranks second. That fits the broader Model C story. Chipper’s peak was not a statistical illusion. It was a complete offensive peak.

Jose Ramirez’s 2018 season ranks third, and he also places several other seasons on the list. That reinforces his new status as a major Model C figure.

George Brett’s 1985 and 1980 seasons also appear high. Brett benefits from the broader model because it recognizes contact, OBP, low strikeouts, and run production.

Alex Rodriguez’s 2007 season remains high because the power and run production were overwhelming, even though Model C is less purely power-driven than Model A.

Figure 6: Component Profiles

The component heat map explains why the rankings changed.

Schmidt’s profile is obvious: huge ISO, walks, runs, and RBI. But he takes a major hit in the low-strikeout category. That is the price of his profile in Model C.

Chipper Jones is more balanced. He scores well in OBP, walks, contact, net steals, and run production. His ISO is not Schmidt-level, but he has fewer weaknesses across the model.

Jose Ramirez has a very different shape. His net stolen-base value is exceptional, and his low-strikeout profile helps him substantially. He is not as dominant as Schmidt in isolated power or walks, but he gains value from being good everywhere.

George Brett also benefits from the low-strikeout component. His profile looks less explosive than Schmidt’s, but more balanced.

Eddie Mathews remains powerful and patient, but like Schmidt, he is hurt by strikeouts.

This is the clearest explanation of the Model C result. The model is not saying Schmidt was worse than before. It says that once the definition of the offense widens, other players begin to catch up.

What Changed From Model A?

Model A gave us a clean Schmidt result.

Model C yields a more complex third-base landscape.

The career winner is still Schmidt. That means the original conclusion was not fragile. Schmidt’s offensive dominance survives a broader model.

But the details change significantly.

Chipper Jones nearly catches Schmidt in career score and passes him in balanced score.

Jose Ramirez becomes the peak leader.

Wade Boggs, George Brett, David Wright, and other broader-skill players rise.

Some power-heavy profiles lose ground.

This is exactly what we should expect. Model C rewards a different offensive ecology.

It asks not only who hit for power and drove in runs, but who combined many offensive skills at once.

The New Third-Base Conclusion

After Model C, I would revise the third-base conclusion this way:

Mike Schmidt remains the best career offensive third baseman in the study.

That is still true.

But it is no longer the whole story.

Chipper Jones now has the strongest balanced argument. His career score is almost identical to Schmidt’s, and his seven-season peak is higher.

Jose Ramirez has the strongest argument for the Model C peak. That is a major finding and one that deserves attention, especially because his career is still active.

Eddie Mathews remains the great historical power challenger.

George Brett, Wade Boggs, and David Wright look better when the model rewards broader offensive skill.

The result is not a contradiction of Model A. It is an enrichment of it.

Model A showed Schmidt’s dominance in power and patience.

Model C shows that third base has a second story: the rise of complete offensive profiles.

Conclusion

The third-base sensitivity test worked exactly as it should.

It did not erase the original result. Schmidt still wins career score. That gives the original Model A conclusion credibility.

But it did change the argument.

The old story was:

Mike Schmidt is the clear offensive third-base winner.

The new story is:

Mike Schmidt is still the career winner, but Chipper Jones and Jose Ramirez become central once the model rewards broader offensive skills.

That is a better conclusion.

It is more nuanced. It is more honest. It shows how much a ranking depends on the offensive definition being used.

Schmidt’s case survives. But Model C reminds us that offensive greatness is not one thing. It can be power and patience. It can be contact and on-base skill. It can be speed and run creation. It can be a long career, a concentrated peak, or a balanced combination of both.

At third base, Model A gave us Schmidt.

Model C gives us Schmidt, Chipper, and Ramirez.

And that is not a problem for the study.

That is what makes the study more interesting.