Jameson Taillon’s career path has been a whirlwind. Drafted out of high school, Taillon was selected second overall by the Pittsburgh Pirates in 2010’s MLB draft. A decade-plus since being integrated into the Pirates’ system, Taillon’s path to becoming a Chicago Cub hasn’t come without disruption
Between April 2014 Tommy John surgery and a hernia, he wouldn’t make his big-league debut until 2016. Once Taillon debuted, he showed why he had been a top prospect. Outside of 2018, Taillon’s 3.38 ERA over 104 IP would mark a career-best. Despite being treated for testicular cancer in 2017, his 3.2 fWAR would also mark a career-best outside of 2018.
Further distanced from Tommy John surgery and his testicular cancer diagnosis, Taillon established himself by way of healthy 2018 production. Spending 2018 completely uninjured, he produced a 3.20 ERA, 3.46 FIP, 2.49 WPA, and 179 SO with a 16.9% K-BB%. He hasn’t matched any aforementioned measure since.
During his completely healthy 2018, Taillon’s four-pitch arsenal became a five-pitch arsenal when he adopted a Slider.
The pitch quickly claimed 18.3% usage, and proved very impactful, producing a Run Value of -10. Every pitch in Taillon’s arsenal, outside of his Changeup, produced quality Run Value in 2018. His Changeup, however, was and has continued to be his least utilized pitch, claiming 4.5% usage – down from 9.8% a year prior and hasn’t measured higher than 8.4% since.
Come 2019, Taillon’s Slider, by way of a 31.9% Slider percentage, became his pitch of choice. But after only 37.1 IP, he would undergo Tommy John surgery again. Undergoing TJS again, his 2019 was over and his 2020, albeit shortened anyway, was over too.
Before he would pitch again, Taillon became a New York Yankee via a January 2021 deal where he was exchanged for a package containing SP Roansy Contreras and three other Yankees prospects.
Since 2021, Taillon has been very durable considering a second TJS, having maintained his health over 71 GS and 321.2 IP. That being said, a particular part of his pitcher profile has become exacerbated since becoming a Yankee. Taillon isn’t overpowering, and he wasn’t even before TJS.
His control is superb and is evidenced by a minimal 5.9% career BB%. And while his 2022 BB% of 4.4% was a career-high (94th percentile), his control doesn’t help diminish quality contact or power produced, to which he’s extremely vulnerable. Since 2021, Taillon has been even more vulnerable. His 2021 and 2022 Barrel% of 8.2% and 8.3% are nearly double his 2016, 2017, and 2018 Brl% of 4.2, 4.1, and 4.3.
Predictably, his higher Barrel Rate has coincided with more HR conceded. Since Taillon began pitching for New York, he’s conceded 50 – 40% of which Statcast has considered a “No Doubter”. Last year, by a sample of 45 SP who had a minimum of 164 IP, his 26 HR conceded placed Taillon tied with Kansas City’s Jordan Lyles for 37th.
With his total Barrels being 13th and 3rd percentile, it’s clear people who’ve opposed him aren’t having a problem producing power versus him. Further exemplifying how vulnerable he has been conceding quality contact and detrimental power is his xBA, xSLG, and xISO percentile. His xBA (19th percentile), xSLG (17th percentile), and xISO (20th percentile) each placed him 38th among the 45 aforementioned SPs (minimum 164 IP).
Neither xSLG nor xISO factor walk value, which is a considerable part of his profile. His xOBP, which his walk minimization does factor into, would show an r2 which doesn’t quite measure up to how each aforementioned metric does. By his xOBP (.297), he placed 25th of 45 (minimum 164 IP).
While 25th is favorable comparatively, minimizing BB hasn’t been enough for keeping his ER down because of how much valuable contact and power has been produced. For a comprehensive feel determined by his xwOBA (.317), he’d place 34th of 45.
How Isolated Power is preferable for determining power production, a preferable metric for determining broader weighted production is xwOBA, which is formulated as:
xwOBA = (xwOBAcon + wBB * (BB – IBB) + wHBP * HBP)/(AB + BB – IBB + SF + HBP)
Even if Brl% would be preferable for a prediction of future value, xwOBA can be used for considering his ER by way of a comprehensive metric.
A .380 xwOBAcon placed Taillon tied for 38th of 45, directly below Colorado’s Kyle Freeland, furthering how detrimental power production conceded has been despite a low BB%.
Either way, Taillon has produced considerable value via his control, consistently, and lately, by being durable. A much-aforementioned sample size of only 45 SP who achieved 164 IP would exemplify how valuable being durable is for SP. You can’t produce if you can’t pitch.
That being said, his ERA and FIP haven’t been extraordinary, which is partly because of each aforementioned metric. Over his 6-year career and 787.2 IP, he’s produced a 3.84 ERA, 3.80 FIP, 3.89 xFIP, and 4.01 SIERA. Respectable, and extremely valuable considering how durable Taillon has been after his second TJS, it’s worth wondering how low his FIP could be if he minimized detrimental power production.
Last year, distanced from his second TJS, each of his ERA (3.91), FIP (3.94), and xFIP (3.79) decreased considerably from his 4.30 ERA, 4.43 FIP, and 4.69 xFIP of a year prior. Counting his 37.1 IP before his 2019 TJS, his ERA and SIERA have been above 4.00 for half of his career, while his FIP was only above 4.00 once (2021).
Taillon hasn’t matched his production from before his second TJS, when his FIP was 3.48 (2017) and 3.46 (2018). Worth 7.1 fWAR between 2017 and 2018, his 2.3 fWAR from a year ago wasn’t quite 3.2 (2017) or 3.9 (2018).
If Taillon can decrease his Barrel%, diminishing each previously aforementioned metric with which ER coincided, both his ERA and FIP would decrease cohesively. The relationship of each metric was used for description, but when employing a popular projection system projection for a purpose of prediction, his projected ERA and FIP don’t embody optimization.
Furthermore, his ZiPS’ projection would show a 2023 ERA of 4.02, 4.00 FIP, and 1.9 fWAR. Steadily declining into 2025, each projection of ZiPS’ from 2023 is a high mark.
The question is, which part of his profile should be adjusted for optimizing performance?
A 4.4%, 94th percentile BB% (tying Justin Verlander and Max Fried for 4th among qualified SP) is a solid foundation, and optimized performance can be achieved if maintained alongside enhanced K% and synonymously, a bolstered K-BB%.
Thus far, a considerably low BB% hasn’t been properly exploited, which is partly a consequence of a 35th percentile K%. Combining a higher K% while having previously produced a 94th percentile BB% would be extremely beneficial for his performance.
The purpose of exploring comparable SP isn’t designed for determining whose performance is comparable already, but for determining whose he could emulate should Taillon enhance his own.
Such a comparison also isn’t proposing both a low BB% and high K% are mandatory for achieving adequate production. Each aforementioned pitcher, and every aforementioned metric aside, provided desirable value simply by being part of a minuscule group of 45 SP who produced a minimum of 164 IP.
Dylan Cease’s spectacular campaign proved optimal production can be achieved by only one (K% or BB%) being upstanding, having paired a 30.4% K% (88th percentile, 5th of 45 qualifying SP) and 10.4% BB% (16th percentile, 45th of 45 qualifying SP). Finishing second by American League Cy Young voting having produced a 2.20 ERA and 4.4 fWAR, both he and Verlander (who won) exemplified how optimal production isn’t achieved one specific way.
That being said, each qualifying SP whose K% was below Taillon’s (20.7%) won’t be confused with a bulk of those whose K% was above.
Previously mentioned was Taillon’s 19th percentile xBA (.260), which placed him 38th of 45 qualifying SPs. Of every qualifier who conceded a higher xBA (José Berrios, Kyle Freeland, Adam Wainwright, Marco Gonzales, Jordan Lyles, Kyle Gibson, Corey Kluber), each can be found above left of him.
Previously mentioned was Taillon’s 19th percentile xBA (.260), which placed him 38th of 45 qualifying SP. Of every qualifier who conceded a higher xBA (José Berrios, Kyle Freeland, Adam Wainwright, Marco Gonzáles, Jordan Lyles, Kyle Gibson, Corey Kluber), each can be found above left of him.
Generating K’s should be considered a pivotal way for decreasing power produced. It’s very simple, but it’s logical and amplified by specific pitcher performance. A higher Strikeout Rate would be wildly beneficial for decreasing power production conceded by simply decreasing BBE alone. Coupled with a considerably low Walk Rate, it’s even more of a definitive recipe for superior production.
Here’s every pitcher who produced .360 or below xSLG and a Strikeout Rate of 26% plus (ordered by Strikeout Rate): Zac Gallen, Justin Verlander, Aaron Nola, Shane McClanahan, Dylan Cease, Corbin Burnes, Shohei Ohtani, Carlos Rodón. That’s a very elite group.
Aside from Zac Gallen (.278 xwOBA), that elite group is every pitcher who produced an xwOBA below .270 and a K% of 26% or greater. Obviously, a straightforward way of avoiding every example of evidenced detrimental contact would be by avoiding contact altogether. Simplicity here is equal, but a higher Whiff Rate, a higher Strikeout Rate.
Determining Whiff Rate isn’t sophisticated and is extremely observable when determining a characteristic of K%. (Like Run Value, Whiff% is defined on a per pitch basis instead of a per PA basis).
The undeniable correlation between Whiff% and K% has been adequately explored, is understood and agreed upon, and can be evidenced by pitcher performance over each era.
Jim Albert’s 2017 blog from Exploring Baseball Data with R is one example of calculated analysis that has adequately evidenced how Whiff Rate and K Rate directly coincide.
Albert’s selected sample of SP from above doesn’t have IP criteria like the sample I’ve continuously referenced for evidencing each metric shared by Taillon’s peers but is a beneficial example by showing how pitcher’s Whiff Rate and K Rate coincided previously.
Bartolo Colón’s placement on Albert’s plot from 2017 is a perfect example of a pitcher who shared each admirable characteristic of Taillon’s. Notorious for his career’s longevity, Colón averaged 172 IP between 2011 and 2018. Retiring after 2018 when he was 45, Colon had produced 1,385 IP since he was 38.
Colón’s production wasn’t achieved by a high K%. Even when he was younger, stacking SO wasn’t part of his eloquence. He only achieved a K Rate above 20% twice (2000, 26.3%; 2001, 21.2%), and when he won 2005’s American League Cy Young award his K% was only 17.3%.
Between 2011 and 2018, his average K% was 15.8%, below his career K% of 17.3%. But like Taillon, Colón’s BB% was outstanding. Over his career, he achieved a 6.5% BB%, which was even lower when only considering his performance from when he was 38 until he was 45 when his average BB% was 4.1%. During his late career stretch, his career low BB% (2.9%!) came in 2015 when he was 45.
Always admirable production from Colón was seldom a product of stacking SO, and his improvement over an already impressively low BB% helped him achieve 16.6 fWAR over a six-year stretch which began when he was already.
Exploring each shared characteristic of Colón’s shouldn’t have been my focus here, but it’s a worthwhile example. From Albert’s plot, Corey Kluber’s location is of major significance too, but I’ll be tying Kluber into another example later. Furthermore, Albert’s work also showed how Whiff Rate and K Rate don’t only coincide when viewing pitcher performance.
Insert Joey Gallo. There isn’t a single MLB player who’s been more synonymous with K% of late. Before Gallo, Chris Davis held Gallo’s crown for being MLB’s K% king. No analysis here, considering Gallo and Davis’ contact deficiency has been acknowledged enough, and mere mention (with visualization) is enough. Pictured below, you can see last year presented a shared idea.
Even if why Whiff% and K% directly coincide can be easily understood, such a strong correlation can be used for explaining how a higher K% can be achieved by a pitcher. Similarly simplistic, and bringing back a previously mentioned aspect of both decreasing detrimental contact and elevating a pitcher’s K% is extremely achievable if contact is avoided altogether.
The determined group of 45 SPs mentioned earlier can prove a simple yet quantifiable concept. Last year, Marco Gonzalez (45th, 13.2%), Cal Quantrill (44th, 16.6%), and Adam Wainwright (41st, 17.8%) comprised 3 of the group’s 5 lowest marks by K%. Those three could also be found in 45th (Wainwright, 230), 44th (Quantrill, 263), and 43rd (Gonzáles, 264) by Swing and Misses.
Therefore if enhanced performance can be achieved by producing a higher K%, and a higher K% has directly coincided with a higher Whiff%, how closely are Whiff% and Swing and Misses associated? Very.
The three SP (Wainwright, Quantrill, Gonzalez) who placed 45th, 44th, and 43rd apiece by Swing and Misses similarly placed 45th (Wainwright, 16.9%), 44th (González, 18.3%), and tied for 43rd (Quantrill, 18.5%) by Whiff%.
Using Wainwright, Quantrill, and Gonzales for displaying a pitcher who wasn’t overpowering shouldn’t be considered minimizing each’s campaign from a year ago.
Each SP is one of only 45 who achieved 164 IP, and each (like Taillon) was a crucial part of a rotation for a team who obtained a postseason berth. IPs are valuable, and producing 164 is praiseworthy. Moreover, each’s production and contribution would be highly coveted by any organization.
That being said, each metric describing how said production was achieved is useful for exemplifying performance level. By each examined metric, higher-end production has been achieved by SPs who have unquestionably solidified themself and are considered a premier performer.
Separation between each group is evidenced by each metric, and because of production he’s conceded and a low K Rate, comparing Taillon should be done using SP within the group is much closer to the lower end of a sample size of SP whose production was absolutely admirable by simply meeting the criteria for inclusion.
Sam Sharpe did phenomenal work covering xwOBA, which I covered previously for being a preferable metric for perceiving production. While a pitcher’s xwOBA and K% doesn’t match xwOBA and ER’s correlation, Sharpe’s work concluded:
“xwOBA predicts future pitching outcomes as well as FIP (which is also not designed to be predictive). Furthermore, xwOBA is more useful when projecting neutral environments (unknown team after free agency) and performs even better than xFIP”.
Sam Sharpe
The plot above is depicting a clear performance discrepancy between SPs sampled. Nobody is confusing Freeland, Berríos, Lyles, Gibson, and Irvin’s production for Gallen, Burnes, Verlander, McClanahan, and Cease. Moreover, Sharpe’s work also concluded Barrel% (where we began) + BB% — K% (both covered extensively) is also considerably predictive of a pitcher’s production.
For maximizing production, Brl%, BB%, and K% was seemingly a solid foundation. Again, I hope I’ve made my stance clear by confirming how his previous performance should be considered extremely coveted. Chicago obviously valued everything Taillon has achieved of late, handing him a 4-year deal worth $72 million.
The foundational idea here is: Chicago signed a good SP who created value from his IP he produced, and his performance continued being valuable because his admirable control masked suboptimal production he conceded. If health is preserved, producing a higher K% by avoiding contact altogether should be considered a clear formula for elevated performance and which could place his production among MLB’s elite.
My question is, when Taillon was signed via free agency, was Chicago operating under a belief he could shrink suboptimal production by producing a higher K%, avoiding contact altogether by modifying his arsenal, and overhauling his Slider for a Sweeper?
Chicago’s decision to award Taillon a sizable deal probably came from visualizing a version of his performance which had been boosted by a higher K% which minimized production conceded. That vision, from Chicago’s view, can be achieved by him developing a Sweeper.
The ‘Sweeper’ has become extremely popular and was partly popularized by his former organization. Nestor Cortes Jr. is only one of many Yankees who have adopted a Sweeper, a pitch known for enhancing horizontal movement using a theory of seam-shifted wake (SSW).
For example, Cortes’ 2020 Slider had 6.1 inches of horizontal break. By 2021, having adopted a Sweeper, his pitch showed 14.5 inches of horizontal break.
MLB’s “Tech Boom” has made maximizing performance easier by properly identifying where a pitcher’s arsenal can be optimized by observing his movement and spin profile. Last year, Cortes had a career year. Now, Chicago is hoping Taillon’s arsenal can be optimized by developing a Sweeper of his own.
There’s a lot that goes into identifying who is a candidate for a Slider/Sweeper exchange, and a pitcher’s arm slot is frequently considered a pivotal part of the determination. A lower arm slot is a characteristic of a ‘Sweeper candidate’ because horizontal movement can be achieved easier by way of a lower arm slot. For a more articulate breakdown, Driveline Baseball does phenomenal work and is a source I utilize often.
When considering how much Cortes’ delivery varied by pitch, Lucas Kelly provided a valuable example from a July AB vs. Cleveland’s Myles Straw.
Insert
Last year, Cortes’ release point varied significantly and can also be seen below by his pitch’s average release height. On average, he released his 4SFB from 5 ft. 8 inches above the ground, and his average Slider (Sweeper), highlighted yellow, came from 5 ft. 2 inches above ground level. Every pitcher’s arm slot doesn’t vary how Cortes’ does, but for Cortes pitching from often a varied slot is clearly working by maximizing each pitch’s movement profile.
From Cubs camp, veteran SP Kyle Hendricks said, “The sweeper, that’s what everybody is doing,”. Hendricks isn’t adopting a Sweeper, but another freshly acquired Cubs hurler is.
Former Detroit Tiger Michael Fulmer, who was also signed via free agency, is actively developing a Sweeper too. “They wanted me to add a little bigger breaking ball,” Fulmer said. “I was mainly cutter last year. It worked well, but they wanted to see some more swing-and-miss stuff.
Fulmer’s specific mention of “swing-and-miss stuff” is directly tied into each pitcher performance metric I explored earlier. Chicago’s clear focus for each freshly signed hurler is targeting a specific area of each pitcher’s arsenal for optimizing performance via adoption of a pitch that can help avoid contact.
Cubs aGM and VP of pitching, Craig Breslow, also spoke of how analyzing previous performance is Chicago’s first step, having said:
“Where we want to start with is looking at performance and working backwards,” Breslow said. “Any pitch with a lot of horizontal movement is going to favor same-sided hitters. So we can look at how his performance has been as a right-handed pitcher versus right-handed hitters, especially putaway counts where we’re prioritizing swing-and-miss. That’s a decent place to start in terms of zoomed-out view, ‘What’s the problem we’re trying to solve?’”
Chicago’s conclusion of how each pitcher’s performance can be optimized is profoundly backed by a plethora of data available. Cameron Grove’s PitchingBot is a very useful tool for understanding precisely why Chicago deemed such a change would be beneficial.
Taillon’s expected Whiff% has continuously outperformed his actual Whiff%. Furthermore, by evaluating his arsenal’s performance by expected Run Value you can easily understand how much Taillon would benefit from fetching advantageous production while ahead. By xRV, his 49th and 32nd xRV/100 percentile while ahead 0–2 and 1–2 apiece would clearly back Chicago’s belief. And Fulmer’s xRV by count would even further confirm Chicago’s conclusion is proper.
Both developing Sweeper has become Chicago’s plan for performance optimization. The pitch becoming popular has helped achieve a separate designation from a typical Slider. When using Baseball Prospectus’ PITCHf/x, you can find ‘Sweeper’ by a solo designation.
Last year, Shohei Ohtani used his Sweeper more frequently than any other pitcher who’s adopted one. (977). Of 34 SP who threw at least 200, Ohtani’s 30.22% PutAway% (rate of two-strike pitches resulting in a SO) was unmatched. For context, Houston’s Lance McCullers Jr. (29.23%) and Christian Javier (27.43%) placed 2nd and 3rd by PutAway%. The Sweeper of Ohtani and both Astros was clearly a force when chasing a SO.
Another Cub who was signed via free agency by Chicago, Marcus Stroman, was also part of a 34 SP Sweeper sample (minimum 200 pitches). Throwing 494, Stroman’s 17.7% Sweeper PutAway% was considerably lower. However, he conceded very minimal production while employing his Sweeper. The pitch produced a .182 BAA, .273 SLG, and .091 ISO.
Ohtani’s Curve (35.0%) also led by PutAway% (again, minimum 200 pitches). However, his Curve conceded a .238 BAA, .405 SLG, and .167 ISO. Despite being used much more frequently (977 vs. 231 thrown), his Sweeper conceded only a .160 BAA, .255 SLG, and .091 ISO.
Last year, Jameson Taillon employed a six-pitch arsenal. Only one pitch, his Curveball (22.8%) achieved a PutAway% above 18%. Moreover, outside of his Sinker (6.5%) and Changeup (12.9%), his arsenal by PutAway% echoed each other. Realized by his 4SFB (17.5%), Cutter (17.9%), and Slider (also 17.9%) putaway percentage is how while his arsenal is sizeable, it’s void of a devastating putaway pitch.
By production conceded, his Curve was also supreme by way of a .168 BAA and .288 SLG. Every other pitch from his arsenal conceded a BAA of .233 or above and a SLG of .427 or above. Despite being his third pitch by usage (14.8%), his Curve’s production is further evidenced by a considerably lower xwOBA and Whiff%. Having identified one stand-out pitch of six he’s employed, it’s clear his sizable arsenal should be modified for optimized performance.
Remember when I mentioned Corey Kluber? This is where Kluber’s placement on Jim Albert’s 2017 plot is best explained. A 2021 Yankees SP himself, Kluber’s Whiff and K rate placing him admirably shouldn’t be a surprise.
Pitching for Cleveland, Kluber won 2017’s American League Cy Young award by way of a 34.1% K% and 7.9 fWAR. And Kluber’s dominance is mentioned here because he employs a devastating Sweeper himself.
During Kluber’s 2017 Cy Young campaign, his Sweeper had a 37.4% putaway percentage. Out of every pitch employed by every pitcher, his Sweeper wasn’t outdone by putaway percentage.
Until a year ago when Kluber achieved 164 IP but produced a 4.50 ERA for Tampa, his ERA hadn’t exceeded 3.85 when he had over 164 IP. Despite declining production, Kluber’s 23.4% Sweeper putaway percentage would have exceeded any from Taillon’s six-pitch arsenal a year ago.
How much widespread curiosity is because of Kluber’s dominance isn’t certain but his absurd 2017 probably played a part once considering when MLB’s technology advanced. Also worth mentioning is how both Kluber and Ohtani feature a lower arm slot.
More data for analyzing such a popular pitch has seemingly become available now. As Tom Tango mentioned below, Chirs Bassitt became Baseball Savant’s first pitcher with a Sweeper designation about a week ago.
It hasn’t been completely executed yet, but designated pitch data becoming available and acknowledged for public use is awesome. For example, here’s a part of Nestor Cortes’ arsenal from a 3D zone view.
Clearly pictured is how much sweep his ‘Slider’ (Yellow) has. Since Cortes Cortes is a lefty, it’s beneficial having a LHH pictured. Also visualized and highlighted from Cortes’ POV is where each pitch came from, another aforementioned area.
Mention of Cortes being a LHP wasn’t a waste. Craig Breslow having cited RHP vs. RHH performance being evaluated when modifying a pitch/arsenal is determined should be considered valuable. It’s a straightforward idea, but maximizing advantageous production for a RHP vs. a RHH (or LHP vs. LHH) should be standard.
Alex Chamberlain’s Pitch Leaderboard (Tableau) is also extremely beneficial for analyzing pitch data, and Cortes’ LHH and RHH pitch frequency can be adequately highlighted.
Versus LHH, Cortes Jr. was 28% Sweeper, but only 17% when he faced RHH. It’s clear why, but it’s worth exploring because Breslow cited performance versus RHH being considered when determining who should develop a pitch.
Out of 527, Taillon’s Slider split was 433 versus RHH and only 94 versus LHP.
The 94 of 2,801 total pitches (3.4%) is clearly too few for weighing performance, but a 7.1% K% does show.
By Slider versus RHP, 433 was 15.5% of his complete usage. And versus RHH alone, his 27.4% Whiff% would’ve been a high mark of any pitch he employed outside of his Curveball. That being said, 20.6% K% by Slider versus RHP doesn’t scream putaway worthy whatsoever.
Location is a clear way of viewing where he placed each pitch and can be viewed while keeping RHH vs. LHH factored in.
Locating even lower and further away, chasing a ‘chase’ created admirable production. Where he located by zone determined production. By a ‘Chase’ Slider versus RHP, 106 pitches produced a .071 BAA, .141 wOBA, 65.6% Whiff%, and 43.8 K% on 32 swings. By a ‘Waste’ pitch he also produced admirably and is depicted using a sample of total pitches. Noticeably, every barreled Slider by a righty (9) was located In Zone.
This isn’t atypical, and one would hope any Out of Zone pitch isn’t providing suboptimal production. But each Swing/Take metric is useful when you consider Fulmer’s was similar, aside from the minor discrepancy of where each measured poorly In Zone.
Pitch detection and designation isn’t perfect, but Sam Sharpe did a great job when he dove into MLB’s system and methodology and provided a comprehensive breakdown here. Pitch physics for optimized performance is continuously being explored and is being displayed and popularized. “In terms of our understanding of pitch physics, we’re in a place where we can optimize the movement profile of pitch,” said Craig Breslow.
The wealth of data available from tech deployed around MLB becoming has already changed how every pitcher’s performance is evaluated and considered for production optimization forever. It’s exhilarating for people who care about player analysis and evaluation, and Chicago’s use and quest for modifying each acquired pitcher’s arsenal should be embraced by every organization, and soon.
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