The legends of Chicago Baseball

The 2005 White Sox and 2016 Cubs are the greatest teams in modern baseball history from Chicago, each representing the pinnacle of their respective eras. Both teams finished atop their divisions and leagues, securing the 1 seed before embarking on memorable postseason runs to World Series glory. The White Sox captured Chicago’s first baseball championship since 1917, while the Cubs ended their historic 108-year drought, cementing themselves in baseball lore. The White Sox were an unheralded team that went on a run, flying under the radar even as they won almost 100 games, while the Cubs had a very popular, nationally recognized team with multiple silver sluggers, gold gloves, and the NL MVP that season.

Each team dominated in different ways, leading the league in many statistical categories. The Cubs were primarily an offensive juggernaut, blending power and patience at the plate, while the White Sox primarily relied on elite pitching and a scrappy, contact-heavy offense. Both were still elite on the other side of the diamond though. With both teams seemingly elite at nearly every position, it begs the question: Which team would prevail in a head-to-head series matchup?

Conventional wisdom suggests the answer isn’t obvious off the bat (pun intended). A quick glance at Baseball Reference shows the Cubs’ superiority at the plate but highlights the White Sox’s edge on the mound. Yet this comparison alone doesn’t account for the differences in the MLB landscape over 11 years. While the gap between 2005 and 2016 might not seem large, the league underwent significant changes. From an explosion in home runs to a rise in strikeouts, MLB evolved in ways that make direct comparisons between these teams challenging.

We will explore the question in-depth, using a variety of baseball statistics, including new era-adjusted statistics which account for a constantly changing talent pool and implicitly account for different baseball environments. More details on these era-adjusted statistics can be found here. Comprehensive details for calculating the talent pool can be found here

Would the White Sox’s pitching dominance stymie the Cubs’ high-powered offense? Or would the Cubs’ balanced lineup and stellar defense prevail? Let’s find out.

Data and Packages

We will load the following built in R packages:

library(ggplot2)
library(dplyr)
library(knitr)
library(kableExtra)
library(tidyverse)
library(ggrepel)
library(readr)
library(ggimage)
load("C://Users//imksy//Downloads//cubssoxdata.RData")

We will also be scraping team and player data from baseball reference, including the Cubs 2016 stats, the White Sox 2005 stats, and overall average stats from 2005 and 2016, and WAR values from baseball reference and fangraphs.

Team W-L Records

One thing statisticians use when it comes to comparing teams in baseball is Pythagorean record. This formula estimates a team’s expected winning percentage based on runs scored and runs allowed, providing a measure of how well their performance on the field aligns with their actual record. It identifies underperforming and overperforming teams that may have lucked out game to game more than others. The formula is:

\[W\% = \frac{(\text{Runs Scored})^{1.81}}{(\text{Runs Scored})^{1.81} + (\text{Runs Allowed})^{1.81}}\]

Record and Ballpark Comparison
Team Wins Losses Pythagorean_Wins Pythagorean_Losses Runs_Scored Runs_Allowed Run_differential Park_Factor_Bat Park_Factor_Pitch
2005 White Sox 99 63 91 71 741 645 +96 104 103
2016 Cubs 103 58 107 54 808 556 +252 95 93

The 2016 Cubs, with their dominant run differential, slightly underperformed in the regular season compared to their Pythagorean expectation. Their actual record was 103-58, but their Pythagorean expectation suggested an even better performance, indicating they outscored opponents by a wide margin but didn’t convert all that dominance into wins. Generally though, they would crush teams when they would win.

In contrast, the 2005 White Sox overperformed relative to their Pythagorean record. While their actual record of 99-63 was strong, their smaller run differential suggests a knack for winning close games. This ability to eke out narrow victories was a hallmark of their team and perhaps indicative of their pitching-focused approach and ability to control late-game situations.

Regular Season vs Postseason

Regular season dominance is one thing, but postseason and playoff performance is where teams cement their legacy. Here, the two teams followed slightly divergent paths.

In one of the most dominant postseason runs in MLB history, the White Sox went 11-1, outscoring opponents 67-34. Their combination of elite pitching, timely hitting, and near-flawless defense made them nearly unstoppable. They famously completed the ALCS sweep of the Angels with four consecutive complete games, a testament to the depth and consistency of their starting rotation.

The Cubs’ postseason journey was more dramatic. They went 11-6, including overcoming a 3-1 deficit in the World Series to defeat Cleveland in seven games. The series itself was a nail-biter, tied 27-27 in runs over the seven games, culminating in a 10th-inning victory in Game 7. While the Cubs didn’t dominate as the White Sox did, their ability to win under pressure added to their historical significance.

Clutchness in Context

Both the 2005 White Sox and 2016 Cubs displayed incredible resilience in the postseason, delivering clutch moments that boosted their win probabilities and led to historic World Series victories—ending title droughts of 88 and 107 years, respectively.

For the White Sox, key moments included Orlando Hernandez escaping a jam in the ALDS (+35.2% WP), Joe Crede’s walk-off double in the ALCS (+38.7%), Paul Konerko’s grand slam in Game 2 of the World Series (+57.9%), and Scott Podsednik’s walk-off homer (+41.8%). Geoff Blum’s 14th-inning homer (+41.2%) and Jermaine Dye’s go-ahead RBI single (+23.8%) sealed their sweep of Houston.

The Cubs also had their share of dramatic moments, from Javier Báez’s solo shot in the NLDS (+30.2%) to their Game 4 comeback against the Giants (+83.9%). Miguel Montero’s grand slam in the NLCS (+35.2%) and Aroldis Chapman’s eight-out save in Game 5 kept their run alive. Finally, Ben Zobrist’s go-ahead double (+32.3%) in Game 7 propelled them to a historic comeback over Cleveland.

Both teams thrived under pressure, with clutch hits, dominant pitching, and momentum-shifting moments defining their championship runs. While comparing their “clutchness” is difficult, their legacies remain equally remarkable.

Park Factors

One often overlooked aspect in team comparisons is park factors, which account for the unique characteristics of MLB stadiums. Unlike other sports, baseball stadiums are not standardized—dimensions, wall heights, and weather conditions vary widely, creating distinct home-field advantages or disadvantages. There are no set standards for stadiums in the MLB, so every stadium has a different distance for home runs, wall height, etc. There’s a difference between the Cubs’ Wrigley Field, and the White Sox’s Comiskey Park

2005 White Sox - Comiskey Park: During the 2005 season, Comiskey Park was considered a pitcher-friendly environment. Its expansive outfield dimensions and cooler Midwest weather suppressed offensive numbers, making it advantageous for a team built around elite pitching. This likely contributed to the White Sox’s ability to win close, low-scoring games by leveraging their pitching dominance.

2016 Cubs - Wrigley Field: In 2016, Wrigley Field leaned more toward favoring hitters. With its small foul territory, unpredictable winds, and friendly confines, Wrigley amplified offensive production. This aligns with the Cubs’ strength as an offensive powerhouse, helping them rack up large run differentials in high-scoring games.

Contextualizing the difference in numbers: T-Test for statistical significance

A direct comparison between years can tell us one thing, but to really understand the difference between these two seasons, we must compare them to a larger sample size of MLB season averages. For that, we can use a t-test. The t-test is just exploratory, and not adjusted for multiple comparisons.

The t-test results provide a compelling analysis of the differences between the 2005 and 2016 MLB seasons relative to the broader 21st-century league averages. In 2005, the Chicago White Sox demonstrated a well-balanced approach, marked by strong pitching control and efficient contact hitting. Notable significant differences include a better WHIP (walks and hits per inning pitched) compared to the league average, highlighting the team’s pitching efficiency. Additionally, lower strikeouts per nine innings (SO9) and walks per nine innings (BB9) reflect a control-oriented pitching strategy. A notably lower batting average on balls in play (BAbip) suggests better fielding or pitching outcomes on balls in play, reinforcing a narrative of defensive dominance. The White Sox also showed higher overall offensive production, as seen in significant differences in OPS, hits, batting average (BA), and on-base percentage (OBP). However, metrics like ERA, runs batted in (RBI), and slugging percentage (SLG) were not significantly different, indicating that while the team excelled in getting runners on base, their power metrics aligned more closely with league averages. This season’s success appears rooted in a traditional baseball strategy emphasizing contact hitting, pitching efficiency, and fielding.

In contrast, the 2016 Chicago Cubs reflect the evolution of modern baseball, dominated by power pitching and the “Three True Outcomes” (strikeouts, walks, and home runs). The Cubs also achieved a significantly better WHIP than the league average, showcasing pitching efficiency. Higher SO9 demonstrates the team’s reliance on strikeouts as a primary tool for run prevention. While their HR9 (home runs allowed per nine innings) was marginally higher, this aligns with the broader mid-2010s trend toward a “boom or bust” approach in the sport. The Cubs saw significantly fewer balls in play (BIP), consistent with their emphasis on strikeouts and home runs. Offensively, higher walk rates and strikeouts suggest a patient hitting strategy characterized by longer plate appearances. However, metrics like BAbip, hits, BA, OPS, and SLG did not show significant differences from the league, indicating offensive production was closer to league norms than pitching performance.

Comparing these two seasons highlights stark contrasts in style. The 2005 White Sox leaned heavily on traditional baseball dynamics, with defensive dominance reflected in lower BAbip and a reliance on balls in play. In contrast, the 2016 Cubs embodied the modern game’s trends, driven by power pitching and a strategy focused on strikeouts and walks. Both seasons reflect unique statistical narratives, with 2005 emphasizing balance and efficiency, while 2016 illustrates the growing dominance of power-focused strategies in the sport.

T-Test Results: 2005 vs 21st Century MLB Seasons
Year Stat Team_Value League_Mean p_value t_statistic Significant
t 2005 ERA 4.290 4.239 0.301 -1.058 FALSE
t1 2005 WHIP 1.306 1.344 0.001 3.663 TRUE
t2 2005 SO9 6.700 7.548 0.000 4.576 TRUE
t3 2005 BB9 2.900 3.256 0.000 8.987 TRUE
t4 2005 HR9 1.200 1.104 0.001 -3.674 TRUE
t5 2005 HR9 1.200 1.104 0.001 -3.674 TRUE
t6 2005 BAbip 0.290 0.297 0.000 10.035 TRUE
t11 2005 H 9.050 8.741 0.000 -4.173 TRUE
t12 2005 HR 1.030 1.093 0.016 2.601 TRUE
t21 2005 RBI 4.370 4.344 0.603 -0.527 FALSE
t31 2005 SB 0.530 0.571 0.009 2.822 TRUE
t41 2005 BB 3.130 3.220 0.022 2.448 TRUE
t51 2005 SO 6.300 7.466 0.000 6.572 TRUE
t61 2005 BA 0.264 0.256 0.000 -4.499 TRUE
t7 2005 OBP 0.330 0.325 0.011 -2.755 TRUE
t8 2005 SLG 0.419 0.414 0.089 -1.771 FALSE
t9 2005 OPS 0.749 0.739 0.030 -2.313 TRUE
t10 2005 BIP 27.150 25.764 0.000 -5.524 TRUE
T-Test Results: 2016 vs 21st Century MLB Seasons
Year Stat Team_Value League_Mean p_value t_statistic Significant
t 2016 ERA 4.180 4.239 0.236 1.215 FALSE
t1 2016 WHIP 1.303 1.344 0.001 3.955 TRUE
t2 2016 SO9 8.300 7.548 0.000 -4.058 TRUE
t3 2016 BB9 3.100 3.256 0.001 3.938 TRUE
t4 2016 HR9 1.200 1.104 0.001 -3.674 TRUE
t5 2016 HR9 1.200 1.104 0.001 -3.674 TRUE
t6 2016 BAbip 0.298 0.297 0.068 -1.912 FALSE
t11 2016 H 8.710 8.741 0.681 0.416 FALSE
t12 2016 HR 1.160 1.093 0.010 -2.784 TRUE
t21 2016 RBI 4.270 4.344 0.139 1.532 FALSE
t31 2016 SB 0.520 0.571 0.002 3.507 TRUE
t41 2016 BB 3.110 3.220 0.006 2.994 TRUE
t51 2016 SO 8.030 7.466 0.004 -3.182 TRUE
t61 2016 BA 0.255 0.256 0.415 0.829 FALSE
t7 2016 OBP 0.322 0.325 0.085 1.799 FALSE
t8 2016 SLG 0.417 0.414 0.318 -1.021 FALSE
t9 2016 OPS 0.739 0.739 0.940 0.076 FALSE
t10 2016 BIP 25.150 25.764 0.022 2.449 TRUE

Traditional Team Totals

We can now compare team totals. The 2016 Cubs led the league in regular season WAR with 56, while the 2005 White Sox were 7th in the league in regular season WAR with 46. The White Sox were at the top with 26 WAR from pitching, while the Cubs were second with 23. This means compared to the rest of their respective leagues in their seasons, the Cubs were best overall, and the White Sox were best in pitching.

In terms of traditional stats, we can take a look at the teams’ batting and pitching statistics to see why the Cubs batting WAR was higher and why the White Sox pitching WAR was higher.

Comparison of Batting Statistics for 2005 White Sox and 2016 Cubs
Tm R/G PA AB R H 2B 3B HR RBI SB CS BB SO BA OBP SLG OPS OPS+ LOB
2016 Chicago Cubs 4.99 6335 5503 808 1409 293 30 199 767 66 34 656 1339 0.256 0.343 0.429 0.772 104 1217
2005 Chicago White Sox 4.57 6146 5529 741 1450 253 23 200 713 137 67 435 1002 0.262 0.322 0.425 0.747 95 1032
Comparison of Pitching Statistics for 2005 White Sox and 2016 Cubs
Tm RA/G W-L% ERA CG tSho SV IP H9 HR BB9 SO9 LOB WHIP FIP ERA+ BF WP R ER
2016 Chicago Cubs 3.43 0.640 3.15 5 15 38 1459.2 6.9 163 3.1 8.9 998 1.110 3.77 133 5933 80 556 511
2005 Chicago White Sox 3.98 0.611 3.61 9 10 54 1475.2 8.5 167 2.8 6.3 1104 1.254 4.12 125 6176 65 645 592

The 2016 Cubs displayed a superior overall pitching performance with a significantly lower ERA (3.15 compared to 3.61) and WHIP (1.11 vs. 1.254). They also had a higher strikeout rate (8.9 SO9 vs. 6.3 SO9), underscoring their reliance on power pitching to dominate opposing batters. While the 2005 White Sox excelled in pitching efficiency, as seen in their higher number of complete games (9 vs. 5), shutouts (10 vs. 15 combined total), and allowed fewer walks (2.8 BB9 compared to the Cubs’ 3.1 BB9), they allowed more hits (8.5 H9 vs. 6.9 H9). Interestingly, the White Sox recorded more saves (54 vs. 38), reflecting their ability to win close games and handle high-pressure situations effectively.

Offensively, the Cubs outperformed the White Sox in key metrics such as runs per game (4.99 vs. 4.57), on-base percentage (OBP, .343 vs. .322), and OPS (.772 vs. .747). They achieved this through a more patient approach at the plate, drawing significantly more walks (656 vs. 435) and recording more extra-base hits (522 combined doubles, triples, and homers compared to the White Sox’s 476). However, the White Sox compensated with a slightly higher batting average (.262 vs. .256), more total hits (1450 vs. 1409), and far greater stolen base production (137 vs. 66), which showcased their efficiency and traditional small-ball tactics.

The 2005 White Sox epitomized a contact-heavy, situationally aware offense that relied on putting the ball in play, moving runners, and creating opportunities on the basepaths. In contrast, the 2016 Cubs demonstrated the modern sabermetrics-driven approach with a focus on walks, strikeouts, and home runs—the “Three True Outcomes.” While the White Sox excelled in manufacturing runs and were more efficient at the plate, the Cubs thrived on explosive innings, leveraging their ability to reach base and hit for power.

The comparison underscores how these two championship-winning teams adapted their strategies to their respective eras. The 2005 White Sox succeeded with a balanced combination of efficient offense and durable pitching, while the 2016 Cubs rode the wave of modern baseball trends, emphasizing power pitching and an analytics-driven offense to outscore and overpower their opponents.

Contextualization: 2016 and 2005 Peer Comparison

As with the previous example of comparing the 2005 and 2016 yearly averages to other yearly averages, we need to compare the 2016 Cubs to other 2016 teams and the 2005 White Sox to other 2005 teams. Again, we use a t-test. Again, the t-test is just exploratory, and not adjusted for multiple comparisons.

The 2005 Chicago White Sox and the 2016 Chicago Cubs excelled in their respective eras but displayed strengths shaped by different strategic emphases and league contexts. The White Sox’s pitching was a cornerstone of their success, as their ERA (3.61) was significantly better than the league average (4.28), supported by superior WHIP and H/9 metrics. Their ability to limit baserunners and hits was critical, even though their strikeout rate (SO9) was not significantly above league norms, reflecting a reliance on pitch-to-contact strategies rather than overpowering hitters. Conversely, the 2016 Cubs dominated nearly all pitching categories, including ERA, WHIP, H/9, and FIP, with standout statistical significance. They also excelled in strikeouts, highlighting a modern trend toward emphasizing swing-and-miss pitching.

Offensively, the 2005 White Sox relied on a traditional power-speed combination, excelling in home runs and stolen bases but struggling in plate discipline metrics like OBP. Their offense was efficient rather than explosive, complementing their pitching strengths. Meanwhile, the 2016 Cubs thrived in metrics emblematic of the sabermetric revolution, including OBP, SLG, and OPS. Their offensive approach, which valued walks and extra-base hits, reflected the growing importance of plate discipline in modern baseball. While their stolen base numbers were below league averages, this was consistent with a reduced emphasis on base-stealing in favor of power and efficiency.

These statistical profiles highlight not only the unique strengths of each team but also the evolution of baseball strategies over time. The 2005 White Sox embodied a balance of strong pitching and traditional offensive metrics, thriving in an era that prioritized efficiency. In contrast, the 2016 Cubs leveraged sabermetrics and modern trends, excelling in power and plate discipline to achieve success. Both teams’ significant deviations from league norms in key categories underscore their ability to capitalize on their strengths and dominate in their respective eras.

T-Test Results: 2005 Chicago White Sox
Team Stat Team_Value League_Mean p_value t_statistic Significant
t 2005 Chicago White Sox ERA 3.610 4.2843750 0.0000001 6.9655189 TRUE
t1 2005 Chicago White Sox ERA+ 125.000 102.0000000 0.0000000 -10.7369899 TRUE
t2 2005 Chicago White Sox ER 592.000 1306.5312500 0.2589275 1.1500270 FALSE
t3 2005 Chicago White Sox WHIP 1.254 1.3699063 0.0000001 7.0665485 TRUE
t4 2005 Chicago White Sox FIP 4.120 4.2928125 0.0022189 3.3356018 TRUE
t5 2005 Chicago White Sox SO9 6.300 6.3781250 0.4247691 0.8088444 FALSE
t6 2005 Chicago White Sox H9 8.500 9.1593750 0.0000005 6.3437769 TRUE
t7 2005 Chicago White Sox BB9 2.800 3.1718750 0.0000182 5.0568562 TRUE
t10 2005 Chicago White Sox BA 0.262 0.2644063 0.0624574 1.9326719 FALSE
t11 2005 Chicago White Sox OBP 0.322 0.3301250 0.0000434 4.7540909 TRUE
t21 2005 Chicago White Sox SLG 0.425 0.4187187 0.1011927 -1.6893219 FALSE
t31 2005 Chicago White Sox OPS 0.747 0.7490000 0.6841645 0.4106354 FALSE
t41 2005 Chicago White Sox OPS+ 95.000 96.8437500 0.1503252 1.4748959 FALSE
t51 2005 Chicago White Sox H 1450.000 2795.2500000 0.3192386 1.0122839 FALSE
t61 2005 Chicago White Sox HR 200.000 318.7812500 0.4394887 0.7831374 FALSE
t71 2005 Chicago White Sox R 741.000 1418.5625000 0.3228929 1.0045454 FALSE
t8 2005 Chicago White Sox RBI 713.000 1350.1250000 0.3286534 0.9924677 FALSE
t9 2005 Chicago White Sox SB 137.000 163.0000000 0.7401216 0.3346786 FALSE
T-Test Results: 2016 Chicago Cubs
Team Stat Team_Value League_Mean p_value t_statistic Significant
t 2016 Chicago Cubs ERA 3.150 4.1840625 0.0000000 13.2615587 TRUE
t1 2016 Chicago Cubs ERA+ 133.000 101.7187500 0.0000000 -16.6191992 TRUE
t2 2016 Chicago Cubs ER 511.000 1278.7187500 0.2161393 1.2626261 FALSE
t3 2016 Chicago Cubs WHIP 1.110 1.3250313 0.0000000 14.3208284 TRUE
t4 2016 Chicago Cubs FIP 3.770 4.1903125 0.0000001 6.7891680 TRUE
t5 2016 Chicago Cubs SO9 8.900 8.0968750 0.0000000 -8.1094152 TRUE
t6 2016 Chicago Cubs H9 6.900 8.7843750 0.0000000 17.8605822 TRUE
t7 2016 Chicago Cubs BB9 3.100 3.1312500 0.5806973 0.5582297 FALSE
t10 2016 Chicago Cubs BA 0.256 0.2552813 0.6713290 -0.4283902 FALSE
t11 2016 Chicago Cubs OBP 0.343 0.3214375 0.0000000 -11.4210622 TRUE
t21 2016 Chicago Cubs SLG 0.429 0.4172812 0.0019363 -3.3871157 TRUE
t31 2016 Chicago Cubs OPS 0.772 0.7387188 0.0000001 -6.8267828 TRUE
t41 2016 Chicago Cubs OPS+ 104.000 96.8437500 0.0000003 -6.5277478 TRUE
t51 2016 Chicago Cubs H 1409.000 2686.2812500 0.3250002 1.0001103 FALSE
t61 2016 Chicago Cubs HR 199.000 356.4687500 0.3602156 0.9287085 FALSE
t71 2016 Chicago Cubs R 808.000 1381.6562500 0.3892472 0.8732389 FALSE
t8 2016 Chicago Cubs RBI 767.000 1318.1875000 0.3859290 0.8794406 FALSE
t9 2016 Chicago Cubs SB 66.000 161.2187500 0.2248901 1.2383290 FALSE

The 2005 Chicago White Sox and the 2016 Chicago Cubs excelled in their respective eras but displayed strengths shaped by different strategic emphases and league contexts. The White Sox’s pitching was a cornerstone of their success, as their ERA (3.61) was significantly better than the league average (4.28), supported by superior WHIP and H/9 metrics. Their ability to limit baserunners and hits was critical, even though their strikeout rate (SO9) was not significantly above league norms, reflecting a reliance on pitch-to-contact strategies rather than overpowering hitters. Conversely, the 2016 Cubs dominated nearly all pitching categories, including ERA, WHIP, H/9, and FIP, with standout statistical significance. They also excelled in strikeouts, highlighting a modern trend toward emphasizing swing-and-miss pitching.

Offensively, the 2005 White Sox relied on a traditional power-speed combination, excelling in home runs and stolen bases but struggling in plate discipline metrics like OBP. Their offense was efficient rather than explosive, complementing their pitching strengths. Meanwhile, the 2016 Cubs thrived in metrics emblematic of the sabermetric revolution, including OBP, SLG, and OPS. Their offensive approach, which valued walks and extra-base hits, reflected the growing importance of plate discipline in modern baseball. While their stolen base numbers were below league averages, this was consistent with a reduced emphasis on base-stealing in favor of power and efficiency.

These statistical profiles highlight not only the unique strengths of each team but also the evolution of baseball strategies over time. The 2005 White Sox embodied a balance of strong pitching and traditional offensive metrics, thriving in an era that prioritized efficiency. In contrast, the 2016 Cubs leveraged sabermetrics and modern trends, excelling in power and plate discipline to achieve success. Both teams’ significant deviations from league norms in key categories underscore their ability to capitalize on their strengths and dominate in their respective eras.

Team WAR Comparisons

Next, we can take a look at team overall WAR values, from baseball reference, fangraphs, and era-adjusted for both.

Cubs WAR by Category
Category ebWAR efWAR fWAR bWAR
Pitching 38.32 32.12 22.1 27.80
Batting 48.79 50.97 36.4 32.38
Totals 87.11 83.09 58.5 60.18
White Sox WAR by Category
Category ebWAR efWAR fWAR bWAR
Pitching 32.45 26.49 22.9 26.99
Batting 26.44 25.95 19.2 20.74
Totals 58.89 52.44 42.1 47.73
Total WAR by Team
Team ebWAR efWAR fWAR bWAR
Cubs 87.11 83.09 58.5 60.18
White Sox 58.89 52.44 42.1 47.73

The Cubs’ clear advantage in era-adjusted WAR suggests that the talent pool in the MLB during the 2016 season was far more robust than it was in 2005. This era adjustment accounts for the evolution of player performance, reflecting the higher level of competition and advancements in training, scouting, and player development over the course of a decade. The difference of 29.2 wins between the two teams in era-adjusted WAR speaks volumes, especially when considering the wider context of the 2016 Cubs, who benefitted from a more competitive league overall. In comparison, the 2005 White Sox, while still impressive, played in a less competitive environment. The Cubs not only had a higher overall talent pool, but their production in both pitching and batting was significantly more efficient relative to their era, further amplifying their edge.

Breaking it down by category, the Cubs hold an advantage in both pitching and batting, though they are notably stronger in the latter. In 2016, the Cubs’ pitchers contributed 38.32 in ebWAR compared to the White Sox’s 32.45. However, the Cubs’ batting performance was an even larger standout, with a massive 48.79 in ebWAR, almost 22 points higher than the White Sox’s 26.44. This was the year of a balanced offense in Chicago, with contributions from key hitters like Kris Bryant, Anthony Rizzo, and Ben Zobrist driving their production. The White Sox, while solid in their own right, relied more heavily on their pitching staff, making their offensive output a bit more inconsistent. Despite these differences, the White Sox’s pitching, which includes strong performances from Mark Buehrle and Jon Garland, helped close the gap, particularly in postseason play. But it’s the Cubs’ depth across both categories that allowed them to sustain a higher level of success through the course of the season.

When we consider the totals, it’s worth noting that the Cubs’ superiority in both adjusted and unadjusted WAR can be interpreted as a reflection of their broader depth. Their pitching staff had more elite contributions (both in terms of raw WAR and era-adjusted performance), while their batting lineup was also more dominant, particularly with regard to their offensive production. This gives the Cubs an almost unmatched balance between their pitching and hitting, which was a key factor in their success, ultimately culminating in their first World Series championship in over a century. On the other hand, the White Sox, while their success in 2005 should not be understated, played in a different era where their pitching was often enough to carry them through. Their team structure reflected a more pitching-centric approach, and while they were able to string together strong performances, they lacked the all-around firepower that the 2016 Cubs brought to the table. Despite the talent gap, the White Sox’s ability to navigate the postseason with timely pitching and clutch performances, such as in their World Series sweep, shows that, in baseball, the gap between regular season WAR and postseason success can sometimes be misleading.

Individual Player Comparisons

Now we must take a look at the individual players on each team and how they stack up against each other. We can split between batters, starting pitchers, and bullpen pitchers, looking at WAR.

Total efWAR, ebWAR, fWAR, and bWAR for White Sox 2005 Batters
name position PA efWAR ebWAR fWAR bWAR
A J Pierzynski C 497 1.93 2.58 1.6 2.26
Paul Konerko 1B 664 4.08 4.15 3.8 4.04
Tadahito Iguchi 2B 582 3.70 3.10 3.3 2.82
Juan Uribe SS 540 2.62 2.62 2.2 2.19
Joe Crede 3B 471 2.15 2.04 1.8 1.64
Scott Podsednik LF 568 2.24 2.05 1.9 1.72
Aaron Rowand CF 640 4.17 3.91 3.8 3.71
Jermaine Dye RF 579 2.62 2.90 2.2 2.50
Carl Everett DH 547 0.63 0.93 -0.2 0.02
Pablo Ozuna 3B 217 0.33 0.82 -0.2 0.38
Timo Perez OF 196 -1.85 -2.00 -1.7 -1.21
Chris Widger C 154 0.21 0.54 -0.3 0.15
Willie Harris 2B 139 0.82 0.93 0.5 0.47
Frank Thomas DH 124 0.76 0.81 0.4 0.43
Geoff Blum IF 99 0.93 0.52 0.5 -0.09
Ross Gload UT 44 0.11 -0.02 -0.5 -0.41
Brian Anderson OF 35 0.21 0.33 0.0 0.08
Joe Borchard DH 12 0.29 0.23 0.1 0.04
Total efWAR, ebWAR, fWAR, and bWAR for Cubs 2016 Batters
name position PA efWAR ebWAR fWAR bWAR
Miguel Montero C 284 1.86 0.84 1.1 -0.33
Anthony Rizzo 1B 676 5.24 6.18 4.9 5.78
Ben Zobrist 2B 631 4.45 4.00 4.0 3.39
Addison Russell SS 598 3.79 4.29 3.3 3.70
Kris Bryant 3B 699 8.00 7.58 7.9 7.30
Jorge Soler LF 264 1.59 1.06 0.8 0.19
Dexter Fowler CF 551 5.13 4.54 4.6 3.99
Jason Heyward RF 592 1.91 2.04 1.0 0.97
Javier Báez IF 450 3.02 3.52 2.2 2.80
Willson Contreras UT 283 3.20 2.43 2.5 1.68
David Ross C 205 2.60 2.32 1.9 1.58
Matt Szczur LF 200 1.42 1.14 0.6 0.20
Tommy La Stella 3B 169 1.32 1.25 0.6 0.43
Chris Coghlan LF 128 0.92 0.91 -0.3 -0.61
Albert Almora CF 117 1.25 1.35 0.6 0.69
Tim Federowicz C 33 0.24 0.28 -0.4 -0.33
Munenori Kawasaki 2B 26 0.61 0.92 0.2 0.43
Jeimer Candelario 3B 14 0.12 0.16 -0.2 -0.05
Ryan Kalish OF 10 0.38 0.42 0.1 0.12
Kyle Schwarber LF 5 0.05 0.06 -0.1 -0.06
Jon Lester P 75 0.49 0.43 0.0 -0.12
Jake Arrieta P 70 1.40 1.32 0.9 0.78
Jason Hammel P 69 1.14 0.94 0.5 0.32
John Lackey P 69 0.39 0.37 -0.2 -0.31
Kyle Hendricks P 68 0.45 0.44 -0.1 -0.16
Total efWAR, ebWAR, fWAR, and bWAR for White Sox 2005 Starting Pitchers
name position IP efWAR ebWAR fWAR bWAR
Mark Buehrle SP 236.2 5.97 5.61 5.9 4.81
Freddy Garcia SP 228.0 4.45 4.25 4.0 3.63
Jon Garland SP 221.0 3.75 5.21 3.5 4.62
José Contreras SP 204.2 3.42 3.77 3.3 3.60
Orlando Hernandez SP 128.1 1.71 1.16 1.3 0.37
Total efWAR, ebWAR, fWAR, and bWAR for Cubs 2016 Starting Pitchers
name position IP efWAR ebWAR fWAR bWAR
Jon Lester SP 202.2 4.86 6.69 4.2 5.63
Jake Arrieta SP 197.1 4.12 4.18 3.5 3.76
Kyle Hendricks SP 190.0 4.74 6.30 4.2 5.40
John Lackey SP 188.1 3.47 3.60 2.9 2.85
Jason Hammel SP 166.2 2.18 2.49 1.3 1.35
Total efWAR, ebWAR, fWAR, and bWAR for White Sox 2005 Bullpen Pitchers
name position IP efWAR ebWAR fWAR bWAR
Dustin Hermanson CL 57.1 1.41 2.22 1.3 2.01
Luis Vizcaino RP 70.0 0.79 1.24 0.3 0.87
Cliff Politte RP 67.1 1.34 3.31 1.1 2.67
Neal Cotts RP 60.1 1.54 2.15 1.4 2.01
Damaso Marte RP 45.1 0.55 0.83 0.2 0.57
Brandon McCarthy RP 67.0 0.89 1.50 0.5 1.19
Bobby Jenks RP 39.1 1.03 0.89 1.0 0.72
Shingo Takatsu RP 28.2 -0.36 0.31 -0.9 -0.08
Total efWAR, ebWAR, fWAR, and bWAR for Cubs 2016 Bullpen Pitchers
name position IP efWAR ebWAR fWAR bWAR
Héctor Rondón CL 51.0 0.99 0.99 0.6 0.46
Trevor Cahill RP 65.2 0.65 1.49 -0.1 0.97
Travis Wood RP 61.0 0.68 1.22 0.0 0.62
Justin Grimm RP 52.2 0.93 0.87 0.5 0.19
Pedro Strop RP 47.1 1.03 1.20 0.7 0.81
Mike Montgomery RP 38.1 1.51 2.36 1.0 2.09
Carl Edwards Jr RP 36.0 0.87 0.74 0.6 0.30
Adam Warren RP 35.0 0.62 0.42 -0.5 -0.41
Aroldis Chapman RP 26.2 3.19 3.46 2.7 2.59
Spencer Patton RP 21.1 0.21 -0.54 -0.2 -0.45
Rob Zastryzny RP 16.0 0.49 0.57 0.5 0.62
Joe Smith RP 14.1 0.48 1.09 -0.3 0.62
Clayton Richard RP 14.0 1.10 1.19 0.5 0.40

There is not a universal formula for WAR, so two main ones are used; bWAR, from baseball reference, and fWAR, from fangraphs. They also have era-adjusted WAR values for each of those websites, ebWAR and efWAR, which will help us compare the key players from each team.

According to Darin W. White, there are a few key differences between fWAR and bWAR. Both assign a total of 1000 WAR to all players, but fWAR allocates 570 to position players, while bWAR allocates 590 to position players, meaning Fangraphs favors pitching by 2%.

The other main difference White points out is in the calculation from each statistical database. Both factor in batting, running, fielding, pitching, and adjustment for averages, but they differ in how they calculate player fielding, as Fangraphs uses Ultimate Zone Rating, and Baseball Reference uses Defensive Runs Saved. Frangraphs’ version takes into account 3 years’ worth of data, but Baseball Reference takes into account only 1 year worth of data, making it more susceptible to noise, especially with young players.

Jon Lester (2016 Cubs) had an fWAR of 4.0 but a higher bWAR of 5.3 due to Baseball-Reference’s inclusion of runs allowed and adjustment for team defense, which benefited from a historically strong Cubs defense. When adjusted for era, his efWAR might highlight his performance relative to the league’s higher scoring environment in 2016, while ebWAR would similarly account for his contribution to the team’s run prevention within the same context.

Mark Buehrle (2005 White Sox) saw a lower fWAR of 3.6 compared to his bWAR of 4.9, as his success relied heavily on inducing weak contact and his defense converting plays behind him. This discrepancy illustrates how bWAR credits him for team-influenced outcomes. Adjusting to era, ebWAR would acknowledge the White Sox’s more balanced scoring environment in 2005, while efWAR would downplay his reliance on defensive outcomes.

For position players, these differences are less pronounced but still important. Anthony Rizzo (2016 Cubs) earned an fWAR of 5.2 compared to a bWAR of 5.7, reflecting small variances in how offensive contributions and positional adjustments are weighted. Paul Konerko (2005 White Sox) exhibited a similar trend, with his fWAR slightly below his bWAR, highlighting his reliance on traditional offensive metrics like RBIs that bWAR values more than fWAR.

These examples demonstrate how fWAR and efWAR emphasize individual skill and minimize team context, while bWAR and ebWAR incorporate defense, team dynamics, and contextual adjustments. The era-adjusted metrics provide additional insights into how players’ contributions compare across different historical conditions, allowing for meaningful cross-era analysis of teams like the 2005 White Sox and 2016 Cubs.

The comparison of WAR and era-adjusted WAR values between the 2005 White Sox and the 2016 Cubs highlights the different roles their key players filled in their success. For starting pitchers, both teams had standout performers, but the Cubs’ rotation was remarkably consistent, with Jon Lester (4.86 efWAR, 6.69 ebWAR) and Kyle Hendricks (4.74 efWAR, 6.30 ebWAR) anchoring the staff. In contrast, the White Sox leaned heavily on Mark Buehrle (5.97 efWAR, 5.61 ebWAR) and Freddy Garcia (4.45 efWAR, 4.25 ebWAR), whose contributions were complemented by strong seasons from Jon Garland (3.75 efWAR, 5.21 ebWAR). The Cubs’ edge in depth and performance across their rotation reflects a more modern approach to pitching reliance compared to the White Sox’s greater variance, with a more top-heavy approach.

For bullpen contributions, the Cubs’ Aroldis Chapman (3.19 efWAR, 3.46 ebWAR) was a dominant force, offering unmatched high-leverage value, while Mike Montgomery (1.51 efWAR, 2.36 ebWAR) provided versatility. Meanwhile, the White Sox bullpen was steadied by Neal Cotts (1.54 efWAR, 2.15 ebWAR) and Cliff Politte (1.34 efWAR, 3.31 ebWAR), who excelled in their roles. Both bullpens effectively supported their rotations, though the Cubs’ reliance on Chapman as a game-changer epitomized modern closer usage.

Offensively, Kris Bryant’s MVP season performance (8.00 efWAR, 7.58 ebWAR) stands out as a defining factor for the Cubs, with significant contributions from Anthony Rizzo (5.24 efWAR, 6.18 ebWAR) and Dexter Fowler (5.13 efWAR, 4.54 ebWAR). In contrast, the White Sox relied more on balanced contributions across their lineup, led by Paul Konerko (4.08 efWAR, 4.15 ebWAR) and Aaron Rowand (4.17 efWAR, 3.91 ebWAR). While the Cubs’ top-tier players provided an elite offensive core, the White Sox showcased a deeper spread of competent performers, reflecting their strategic reliance on balance rather than a few superstars. Overall, the Cubs’ emphasis on standout individual performances contrasts with the White Sox’s collective contributions, offering insight into the evolution of team-building strategies over a decade.

We can also plot WAR values of players to see which team has higher WAR players, in the upper right quadrants. We can compare directly each WAR value for each player, with proper data visualization.

Batter Comparison

When comparing the Cubs’ and White Sox’s WAR values across various metrics, visualized through scatter plots, distinct trends emerge that highlight the contrasting team compositions and performances.

The Cubs’ 2016 roster showcases superior top-end talent, as evidenced by the clustering of elite players in the upper-right quadrants of the graphs, representing high WAR values on both fWAR and bWAR axes. Kris Bryant, Anthony Rizzo, and Dexter Fowler stand out as dominant contributors, significantly outperforming the best 2005 White Sox players on both adjusted (efWAR, ebWAR) and raw (fWAR, bWAR) metrics. These players are clustered at the extremes, reflecting their excellence both offensively and defensively, with Bryant in particular achieving MVP-caliber levels.

In contrast, the 2005 White Sox display greater depth across the board. The scatter plots show a noticeable pattern: starting from the top-right corner of the graph (highest WAR), there is a small group of red points (Cubs), followed by a much denser cluster of blue points (White Sox), and finally another smaller cluster of red points toward the bottom-left (lower WAR). This distribution illustrates that while the Cubs boasted higher peaks, the White Sox maintained a more consistent contribution across their roster. Players like Paul Konerko, Jermaine Dye, and A.J. Pierzynski formed a reliable core with moderate WAR values, supported by other steady contributors.

The era-adjusted metrics, efWAR and ebWAR, highlight these trends even more starkly. Adjusting for offensive environments and run-scoring conditions, the Cubs’ top stars remain in the upper right but shift slightly, emphasizing their dominance in a higher-scoring era. Meanwhile, the White Sox’s adjusted metrics cluster closer to the center of the graphs, indicating solid, era-consistent performances across the team.

Starting Pitcher Comparison

When analyzing the starting and bullpen pitchers for the 2005 White Sox and 2016 Cubs, the scatter plots based on their WAR values (both fWAR and bWAR) reveal key differences between the two teams, reflecting their pitching strategies and player performances.Font size indicates innings pitched.

For the Cubs, the pitching staff’s distribution on the graphs leans toward a concentration of above-average WAR values for a few key starters, such as Jon Lester and Jake Arrieta. These pitchers occupy the upper-right quadrant, where the top contributors are located. Arrieta’s performance in particular stands out, with both his fWAR and bWAR consistently ranking among the highest for starting pitchers in 2016. However, the Cubs’ bullpen doesn’t show the same level of dominance. While they were effective, the bullpen contributions are less clustered in the upper quadrants compared to the starting rotation. The bullpen’s WAR values show more spread, with few truly elite performers. This distribution suggests that while the Cubs’ starting pitchers carried the team, their bullpen was solid but not exceptional.

The 2005 White Sox, on the other hand, have a different pattern in their pitching graph. The starting rotation, headlined by Mark Buehrle and Freddy Garcia, shows consistent, if slightly lower, WAR values compared to the Cubs’ top starters. These pitchers are typically clustered in the mid-range of the graph, reflecting solid but not dominant performances. What stands out for the White Sox, however, is the depth of their bullpen. The team had a remarkably strong and deep bullpen, with players like Shingo Takatsu and Bobby Jenks contributing significantly. These bullpen players appear more frequently in the upper quadrants, indicating their critical role in the White Sox’s success. This depth was a hallmark of the team, allowing them to maintain performance through extended innings and in high-leverage situations.

The era-adjusted metrics (efWAR and ebWAR) provide further nuance. For the Cubs, these adjusted values highlight the relative dominance of their starting pitchers in a higher-scoring era, with Arrieta again ranking highly. The White Sox’s bullpen, when adjusted, appears even more impressive, with players like Jenks emerging as stronger contributors in comparison to their raw WAR values. This adjustment underscores the importance of the bullpen in their World Series run, where pitching depth was a key factor.

These trends in WAR values and era-adjusted metrics show how the Cubs relied on their starting pitchers to lead the way, while the White Sox built their success on a more balanced, deep pitching staff, with particular strength in their bullpen, though they were still top-heavy in their starting rotation.

Bullpen Comparison

We can also do the same for bullpen pitchers.

The Cubs went through a ton of pitchers in search for a consistent bullpen in 2016, including deadline day deals for Mike Montgomery (who got the final out in the World Series) and Aroldis Chapman (who was one of the best closers in baseball). Chapman was the best reliever among both teams, followed by three White Sox pitchers in Neal Cotts, Cliff Politte, and Dustin Hermanson, then two more Cubs in Mike Mntgomery and Pedro Strop. Neither bullpen was elite, but both ended up being solid, and good enough to get the job done considering how good the starting pitching, batting, baserunning, and fielding was.

Who Wins?

In a 7-game series between the 2005 White Sox and the 2016 Cubs, the key difference in expected outcomes lies in the WAR metrics and era-adjusted statistics (efWAR and ebWAR). The Cubs’ superior starting pitching, highlighted by Jon Lester and Jake Arrieta, significantly outmatches the White Sox’s rotation, with the Cubs posting higher WAR values across the board. Arrieta’s 6.0 fWAR in 2016, for example, would likely dominate against the White Sox’s starters, such as Mark Buehrle (3.7 fWAR). The Cubs’ rotation, which was a major strength during the regular season, maintained this dominance throughout the postseason, as evidenced by their key role in the team’s success in the World Series. In contrast, the White Sox’s rotation was effective but lacked the same high-end talent, relying more on their bullpen and timely hitting.

When comparing the bullpen, the White Sox again have an edge with a deeper and more impactful relief corps. Bobby Jenks and Shingo Takatsu, each with significant fWAR contributions in 2005, provided the White Sox with a competitive advantage in late-game situations, a critical factor for success in a playoff series. However, the Cubs’ bullpen, while strong, is slightly less reliable, and their postseason struggles—especially in close games—suggested that the White Sox’s bullpen might be able to exploit weaknesses in key moments. While the Cubs’ bullpen had Aroldis Chapman’s overpowering presence, the White Sox’s depth, and ability to close out games effectively, would likely balance the playing field in terms of pitching performance. With both teams having great bullpens, though, the battle in high-leverage moments could be an intriguing subplot.

In terms of advanced fielding, the 2016 Cubs significantly outperformed the 2005 White Sox. The Cubs were known for their elite infield defense, particularly from players like Kris Bryant and Javier Báez, both of whom ranked highly in defensive metrics like UZR and DRS. This complemented their overall team defensive efficiency, which was a major asset in their World Series run. The White Sox, while solid defensively, did not have the same depth or range as the Cubs, and their defensive metrics were not as advanced. The Cubs’ elite defense, particularly in the infield and outfield, would provide them with a significant advantage in a postseason series, potentially turning more plays into outs and limiting extra-base hits.

As for clutch performance, the White Sox demonstrated a reputation for stepping up in critical moments, particularly during their postseason run in 2005. Their ability to come through in tight spots was evidenced by key clutch hits from players like Paul Konerko and Geoff Blum in the World Series. Notably, Konerko’s grand slam in Game 2 and Blum’s 14th-inning homer in Game 3 added enormous win probability swings, showing the White Sox’s ability to rise to the occasion. Meanwhile, the Cubs’ 2016 team also had its share of clutch performances, such as Javier Báez’s home run in Game 1 of the NLDS and Kris Bryant’s dramatic two-run homer in Game 3 of the NLDS. The Cubs’ comeback win in Game 4 of the NLDS and Ben Zobrist’s key hit in Game 7 of the World Series demonstrated their resilience. Both teams showcased an incredible amount of grit, and while the White Sox’s bullpen may have had an edge in late-game scenarios, the Cubs’ timely hitting in key moments, particularly in the 2016 postseason, put them in a favorable position. This makes it difficult to definitively say which team was more clutch, as both exhibited similar abilities to come through in high-leverage moments.

On offense, the Cubs were significantly more potent, as shown by the WAR contributions of Kris Bryant, Anthony Rizzo, and Dexter Fowler. Bryant’s 7.9 fWAR and Rizzo’s 5.8 fWAR in 2016 vastly outshine the White Sox’s top hitters like Paul Konerko and Jermaine Dye, whose WAR contributions, while strong, do not compare to the Cubs’ offensive firepower. This disparity is underscored by the Cubs’ era-adjusted WAR metrics, where their efWAR and ebWAR are substantially higher, showing that their offensive and defensive contributions in the higher-scoring 2016 environment were more efficient than the White Sox’s production in the lower-scoring 2005 environment. The Cubs also had an exceptional ability to get on base, as evidenced by their .343 OBP, which ranked in the top half of MLB teams, compared to the White Sox’s .323 OBP. While the White Sox offense, fueled by timely hitting, could be dangerous, it lacked the same overall balance and depth seen in the Cubs’ lineup, giving the Cubs a clear edge in terms of sustained offensive production.

Thus, while the White Sox possess strengths in pitching depth, bullpen dominance, and clutch moments, the Cubs’ combination of elite starting pitching, offensive firepower, and defensive prowess provides a significant edge. The Cubs’ superior era-adjusted WAR values further highlight their efficiency in the 2016 season, and they would likely outperform the White Sox in a series, despite the White Sox’s strong postseason pedigree. In a 7-game series, I predict that the Cubs would likely emerge victorious with a 4-2 win, although the White Sox’s depth, timely hitting, and bullpen would make the series highly competitive. Ultimately, while both teams share a legacy of clutch performances, the Cubs’ higher overall WAR, especially in the context of era-adjusted statistics, would be the determining factor in their triumph. This projection is mostly based on the statistics we have covered in this article, but in the future, we could create a simulation with the era adjusted statistics to run this matchup in a 7 game series 10,000 times, and really see the most likely outcome, instead of only using human prediction.

References

Sabermetrics 101: Understanding the Calculation of WAR, Darin W. White, PhD, Samford University, https://www.samford.edu/sports-analytics/fans/2023/Sabermetrics-101-Understanding-the-Calculation-of-WAR

The Full House Model for cross-era comparisons of baseball players (results and fun digressions version 2.0)

Daniel J. Eck and Shen Yan and Adrian Burgos Jr. and Christopher Kinson https://htmlpreview.github.io/?https://github.com/ecklab/era-adjustment-app-supplement/blob/main/writeups/era_adjusted_V2_I.html

Baseball Reference 2005 White Sox 2016 Cubs

Fangraphs Win Probability Game Graphs

Acknowledgements

Thank you to Professor Daniel J. Eck, the University of Illinois, URES, and the entire Eck MLB Era-adjustment lab for allowing me to write this piece.