Chris Archer pitches currently for the Minnesota Twins. Initially, the Cleveland Indians drafted Chris in the 5th round of the 2006 MLB June Amateur Draft. He was traded twice as a minor leaguer, but eventually became a two-time All-Star for the Tampa Bay Rays. He finished in the top 5 ERA for pitchers in 2015. The Rays traded him to the Pittsburgh Pirates in 2018, after which Chris struggled with his health and performance. Finally, Chris joined the Twins in 2022, and at the time of this post he has earned an ERA of 3.08.
We Would Like to Make the Case for Chris Archer-but Which Case Should We Make?
We would like to make the case for Chris Archer following his thoracic outlet syndrome surgery. However, statistics support two different cases-one for Chris Archer’s successful recovery from TOS, and one against. Perhaps you would like to read the case for each side.
We Would Like to Make the Case for Chris Archer Based on Statistics
In Chris Archer’s redemption arc comes full circle with the Twins, Noah Yingling states, “Chris Archer has made a good comeback with the Minnesota Twins…Entering the month of July, Minnesota Twins starting pitcher Chris Archer has made a successful comeback. While the club has been vigilant with his pitches and innings (only pitching more than 80 pitches in a start once), he has seen good results with them.”
“In 15 starts, Archer has a 3.08 ERA, which is the lowest in his career. He is also only allowing 6.9 hits per nine innings, which is also the lowest in his career.”
Indeed, it seems hard to argue with Yingling’s case for Chris Archer. Overall, the statistical outlook appears hopeful for Chris Archer, a two-time All-Star earlier in his career.
We Would Like to Make the Case for Chris Archer… Not So Fast, the Statistics Argue
McCann states, “Some of Archer’s ‘baseball card’ numbers have looked good so far, posting an ERA of 4.10 [Editor: currently 3.08] and striking out 8.2 batters per nine innings. His peripheral numbers, however, have been less-than-encouraging signs.”
McCann posts the following selected advanced baseball statistics to support his case:
ERA vs. FIP:
Strikeouts per 9 innings:
Walks per 9 innings:
BABIP (Batting Average on Balls in Play) Against:
Batting Average vs. Expected:
Expected Batting Average
League Expected BA
Slugging vs. Expected:
Expected Slugging Average
League Expected SA
McCann continues, “As you can see above, the peripheral numbers are expecting much worse outcomes than Archer has been receiving, which is surprising given the .465 SLG against already. Hitters have barreled up 10.3% of batted balls against Archer, which quite a bit above the league average of 6.7%. Basically, hitters are squaring up Archer’s pitches at a high rate, but as his BABIP shows, they are turning into outs. In terms of pitches, his 4-seamer has been targeted the most successfully, which batters have hit hard on 52.2% of contact.”
What are the Differences Between Standard Baseball Statistics and Advanced Statistics?
For those who aren’t students of advanced baseball statistics, or ‘sabermetrics,’ we would like to provide some simplified explanations:
FIP, fielding independent pitching, focuses only on those variables under the pitcher’s control. Specifically, FIP includes strikeouts, unintentional walks, home runs, and hit by pitch counts. FIP calculation assumes that once a ball is put in play, the pitcher has no control over the outcome of the play. Thus, a pitcher with a weak defense would not have his contribution to the play weakened by a weak defense or by simple bad luck. On the other hand,ERA, earned run average, tells us only how many runs scored while the pitcher was on the mound. As a result, ERA does not tell us how many of those runs were allowed by the pitcher, and how many by the defense or luck.
In the case of Chris Archer, his ERA of 4.10 is significantly lower than his FIP of 5.83. In effect, luck has gone Chris’ way more often that not this season, and if one eliminated defense and luck, Archer’s ERA should rise. Archer’s contribution to scored runs should be higher, but lucky bounces have helped him produce a lower than expected ERA. Over time, regression to the mean should bring his ERA closer to his FIP as those bounces even out.
BABIP, Batting Average on Balls in Play, represents corollary to FIP. BABIP includes only balls put in play, excluding home runs, sacrifice bunts, and sacrifice flies. In this situation, three factors generally affect the number of balls in play that become hits. These three factors consist of defense, luck, and talent level of the hitter. A better defense will prevent more hits than a poor defense, lowering BABIP. Luck allows hard-hit balls to be caught for outs, while soft bloop hits can fall for hits. More talented players create more hard hit balls than less-talented players. Typical BABIP for hitters is .300. Over time (typically two seasons or more), better players can maintain a higher BABIP (up to .350 for the best hitters).
In a similar manner, BABIP can be used to assess a pitcher’s performance. However, a hitter has more control over his BABIP than does a pitcher. Pitchers’ BABIP typically ranges between .290 and .310 over a period long enough to eliminate short-term trends. Typically, assessment of a pitcher’s BABIP takes longer than does that of a hitter; usually three full seasons’ worth of data.
In the case of Chris Archer, his career BABIP is dead average for the league, but his 2022 season BABIP is far lower. Thus, a combination of defense, luck, and hitters’ skill has lowered Chris Archer’s 2022 batting average against, making his performance appear better than previously in his career (and much better than expected for any pitcher, given enough time for short-term trends to stabilize). In other words, Chris Archer controls very little of this low BABIP. While the numbers make him look good, but they are in fact not related to his performance.
What is the Value of a New Paradigm?
In this post, we have compared two articles regarding the success of Chris Archer following surgical treatment for thoracic outlet syndrome. Each of these two articles applies different paradigms to arrive at two very different conclusions. The first article relies on time-tested standard baseball statistics to propose success. However, the second article utilizes newer baseball statistics to conclude the opposite. Interestingly, these articles refer specifically to a pitcher recovering from TOS. But in the broader sense, they illustrate how TOS specialists currently use many different paradigms to understand thoracic outlet syndrome.
Most scientific disciplines grow over time, from rudimentary understanding to complex modeling. During this growth, progress takes frequent pauses but makes unexpected leaps. Typically, researchers work to fill in the gaps or to solve anomalies that remain unexplained over time. Some researchers grow knowledge in a slow but steady, linear and incremental manner. But on occasion, a researcher brings forth an entirely new and unexpected paradigm that fills in a large gap or explains away an existing anomaly. While this new paradigm represents a leap forward, many researchers may remain resistant to such a large and unexpected change. Over time, the new paradigm can explain anomalies and fill gaps in our current understanding, allowing new solutions. But the human resistance to large and sudden change may delay incorporation of this new knowledge for some time. Eventually, the new paradigm is incorporated into the accepted knowledge base, and the cycle repeats.
As regards baseball statistics, all baseball fans understand the rich and well-established history of the game through standard statistics. Indeed, serious baseball fans readily quote their favorite player’s ERA, strikeouts, win-loss records, home runs, batting average, and RBIs. However, a disruptive paradigm arose in the 1960s and gained momentum in the 1980s, now known as Sabermetrics.
What are Sabermetrics?
Earnshaw Cook published the book Percentage Baseball in 1964, and many consider him the first sabermetrician. The baseball establishment quickly dismissed Cook’s work. In the late 1970s, Bill James began publishing his annual Baseball Abstracts, which met similar resistance. However, James and the the Society for American Baseball Research persisted for decades, and James eventually became the public face of sabermetrics.
At the same time, Davey Johnson, second baseman for the Baltimore Orioles, began writing computer programs to take advantage of this new paradigm. He continued this practice as a minor league manager and subsequently as a major league manager for the New York Mets. In the 1990s, Sandy Alderson, general manager of the Oakland A’s began using sabermetrics in earnest. Billy Beane replaced Alderson as general manager in 1997 and, with Paul DePodesta, fully installed the principles of sabermetrics in a major league team. In 2002, the low-budget A’s broke the American League record by winning 20 consecutive games. In 2003, Michael Lewis introduced the rest of the world to sabermetrics in his book Moneyball: The Art of Winning an Unfair Game .
I recall reading Moneyball when it first came out. I recall my fascination with these new paradigms, finding new truths in the hundred year-old data set of baseball numbers. But I also recall my astonishment on observing the emotional reactions the book engendered from the baseball establishment. Surely they could see how powerful the new paradigm could be! But I should not have been surprised by human nature. The book stirred dogma and controversy amongst baseball experts, management, and fans, who refused to accept or even consider its value. At the same time, the book grew the field of sabermetrics by winning new devotees, who in turn found new paradigms to help grow the power of sabermetrics.
What Can TOS Patients and Doctors Learn from the Case of Chris Archer?
Those of us who work with TOS patients have seen the same dogma and controversy. Although the first description of thoracic outlet syndrome appeared over 200 years ago, many current practitioners hold onto established dogma and remain resistant to new paradigms. At the same time, our knowledge of thoracic outlet syndrome must be considered incomplete, with gaps and anomalies that remain unsolved. As a prime example, presently there exists no widely-accepted gold standard for the diagnosis of neurogenic thoracic outlet syndrome. Thus, any outcome study could be questioned simply due to the unknown presence of disease within the study population. Despite these challenges, I believe we now have the tools to bridge those gaps and explain those anomalies, which bodes well for future TOS patients.
So let’s consider the case of Chris Archer as a paradigm for our current state of understanding of thoracic outlet syndrome. We can consider the presence of new paradigms as potentially powerful tools in diagnosis and treatment of thoracic outlet syndrome. Finally, let’s hope the best for Chris Archer, that he continues towards a full recovery!