| The K Zone Overall Ranking | Mike’s Ranking | Ian’s Ranking | Mojo’s Ranking | Maddie’s Ranking | Jack’s Ranking | Aaron’s Ranking | |
| 1 | CF Mike Trout (LAA) | CF Mike Trout (LAA) | CF Mike Trout (LAA) | CF Mike Trout (LAA) | CF Mike Trout (LAA) | CF Mike Trout (LAA) | RF Mookie Betts (BOS) |
| 2 | RF Mookie Betts (BOS) | RF Mookie Betts (BOS) | RF Mookie Betts (BOS) | RF Mookie Betts (BOS) | RF Mookie Betts (BOS) | RF Mookie Betts (BOS) | CF Mike Trout (LAA) |
| 3 | 3B Jose Ramirez (CLE) | 3B Jose Ramirez (CLE) | 3B Jose Ramirez (CLE) | 3B Jose Ramirez (CLE) | 3B Jose Ramirez (CLE) | 3B Jose Ramirez (CLE) | 3B Jose Ramirez (CLE) |
| 4 | DH J.D. Martinez (BOS) | 3B Manny Machado (SD) | DH J.D. Martinez (BOS) | SS Francisco Lindor (CLE) | DH J.D. Martinez(BOS) | RF Bryce Harper (PHI) | DH J.D. Martinez |
| 5 | RF Bryce Harper (PHI) | RF Christian Yelich (MIL) | SS Francisco Lindor (CLE) | DH J.D. Martinez (BOS) | RF Aaron Judge (NYY) | DH J.D. Martinez (BOS) | 3B Alex Bregman (HOU) |
| 6 | RF Aaron Judge (NYY) | SS Francisco Lindor (CLE) | RF Aaron Judge (NYY) | RF Aaron Judge (NYY) | 3B Alex Bregman (HOU) | 3B Alex Bregman (HOU) | SS Francisco Lindor (CLE) |
| 7 | SS Francisco Lindor (CLE) | DH J.D. Martinez (BOS) | RF Bryce Harper (PHI) | LF Juan Soto (WSH) | RF Bryce Harper (PHI) | SP Max Scherzer (WSH) | RF Aaron Judge (NYY) |
| 8 | 3B Manny Machado (SD) | RF Bryce Harper (PHI) | 3B Manny Machado (SD) | SS Corey Seager (LAD) | SS Francisco Lindor (CLE) | 2B Jose Altuve (HOU) | SP Chris Sale (BOS) |
| 9 | 3B Alex Bregman (HOU) | 1B Freddie Freeman (ATL) | SS Alex Bregman (HOU) | 1B Paul Goldschmidt (STL) | 1B Freddie Freeman(ATL) | SP Jacob deGrom (NYM) | RF Bryce Harper (PHI) |
| 10 | 1B Paul Goldschmidt (STL) | 1B Paul Goldschmidt (STL) | RF Christian Yelich (MIL) | RF Bryce Harper (PHI) | RF Christian Yelich (MIL) | 1B Joey Votto (CIN) | RF Christian Yelich (MIL) |
| 11 | 1B Freddie Freeman (ATL) | SP Chris Sale (BOS) | 1B Paul Goldschmidt (STL) | 1B Joey Votto (CIN) | 3B Manny Machado | 3B Manny Machado (SD) | SP Jacob deGrom ( NYM) |
| 12 | RF Christian Yelich (MIL) | SP Max Scherzer (WSH) | 1B Freddie Freeman (ATL) | 3B Kris Bryant (CHC) | SP Max Scherzer (WSH) | 1B Paul Goldschmidt (ARI) | 3B Manny Machado (SD) |
| 13 | SP Max Scherzer (WSH) | SP Jacob deGrom (NYM) | SP Chris Sale (BOS) | 1B Freddie Freeman (ATL) | SP Jacob deGrom (NYM) | 1B Freddie Freeman (ATL) | SP Max Scherzer (WSH) |
| 14 | SP Jacob deGrom (NYM) | SP Justin Verlander (HOU) | SP Max Scherzer (WSH) | LF Ronald Acuna (ATL) | 1B Paul Goldschmidt (STL) | RF Aaron Judge (NYY) | SS Javier Baez (CHC) |
| 15 | SP Chris Sale (BOS) | SP Blake Snell (TB) | SP Jacob deGrom (NYM) | 3B Matt Chapman (OAK) | DH Giancarlo Stanton (NYY) | SS Trevor Story (COL) | 1B Paul Goldschmidt (STL) |
| 16 | 2B Jose Altuve (HOU) | 2B Jose Altuve (HOU) | 2B Jose Altuve (HOU) | 3B Nolan Arenado (COL) | 2B Jose Altuve (HOU) | 2B Scooter Gennett (CIN) | 2B Jose Altuve (HOU) |
| 17 | 1B Joey Votto (CIN) | DH Giancarlo Stanton (NYY) | 1B Joey Votto (CIN) | 3B Anthony Rendon (WSH) | 1B Joey Votto (CIN) | DH Giancarlo Stanton (NYY) | 3B Nolan Arenado (COL) |
| 18 | DH Giancarlo Stanton (NYY) | RF Aaron Judge (NYY) | DH Giancarlo Stanton (NYY) | 3B Justin Turner (LAD) | SP Chris Sale (BOS) | SP Justin Verlander (HOU) | 1B Freddie Freeman (ATL) |
| 19 | LF Juan Soto (WSH) | 2B Scooter Gennett (CIN) | SS Trea Turner (WSH) | 3B Manny Machado (SD) | 2B Scooter Gennett (CIN) | SP Chris Sale (BOS) | SS Trevor Story (COL) |
| 20 | SS Corey Seager (LAD) | SS Trea Turner (WSH) | SP Justin Verlander (HOU) | 3B Alex Bregman (HOU) | 3B Nolan Arenado (COL) | RF Mitch Haniger (SEA) | SP Blake Snell (TB) |
Category Archives: Uncategorized
Top 10 Starting Pitchers
| The K Zone Overall Ranking | Mike’s Ranking | Ian’s Ranking | Mojo’s Ranking | Maddie’s Ranking | Jack’s Ranking | Aaron’s Ranking | |
| 1 | Chris Sale (BOS) | Chris Sale (BOS) | Chris Sale (BOS) | Clayton Kershaw (LAD) | Max Scherzer(WSH) | Max Scerzer (WSH) | Chris Sale (BOS) |
| 2 | Max Scherzer (WSH) | Max Scherzer (WSH) | Max Scherzer (WSH) | Chris Sale (BOS) | Jacob deGrom (NYM) | Jacob deGrom (NYM) | Jacob deGrom (NYM) |
| 3 | Jacob deGrom (NYM) | Jacob deGrom (NYM) | Jacob deGrom (NYM) | Jacob deGrom (NYM) | Chris Sale (BOS) | Justin Verlander (HOU) | Max Scherzer (WSH) |
| 4 | Justin Verlander (HOU) | Justin Verlander (HOU) | Justin Verlander (HOU) | Max Scherzer (WSH) | Aaron Nola (PHI) | Chris Sale (BOS) | Blake Snell (TB) |
| 5 | Clayton Kershaw (LAD) | Blake Snell (TB) | Clayton Kershaw (LAD) | Justin Verlander (HOU) | Justin Verlander (HOU) | Clayton Kershaw (LAD) | Justin Verlander (HOU) |
| 6 | Corey Kluber (CLE) | Corey Kluber (CLE) | Corey Kluber (CLE) | Corey Kluber (CLE) | Corey Kluber (CLE) | Aaron Nola (PHI) | Corey Kluber (CLE) |
| 7 | Aaron Nola (PHI) | Aaron Nola (PHI) | Gerrit Cole (HOU) | Aaron Nola (PHI) | Blake Snell (TB) | Trevor Bauer (CLE) | Trevor Bauer (CLE) |
| 8 | Blake Snell (TB) | Gerrit Cole (HOU) | Carlos Carrasco (CLE) | Luis Severino (NYY) | Trevor Bauer (CLE) | Walker Buehler (LAD) | Gerrit Cole (HOU) |
| 9 | Trevor Bauer (CLE) | Patrick Corbin (WSH) | Trevor Bauer (CLE) | Blake Snell (TB) | Luis Severino(NYY) | Blake Snell (TB) | Aaron Nola (PHI) |
| 10 | Gerrit Cole (HOU) | Luis Severino (NYY) | Patrick Corbin (WSH) | Gerrit Cole (HOU) | Patrick Corbin (WSH) | Gerrit Cole (HOU) | Luis Severino (NYY) |
Last year, starting pitching was headlined by a clear “big four,” but going into this season, the lines are far more blurred. Many pitchers each qualify for many different positions, and almost everyone has both some kind of question or pushback, and some kind of draw. While he’s experienced a shaky start to 2019, Chris Sale lead off our preseason ranks after finishing with a 6.97 K/BB and 1.98 FIP. Sale finished second in AL starters in WAR, despite starting seven fewer games than the leader and most of those behind him. Max Scherzer, who has modeled consistency since breaking out in 2013, ranks second. Scherzer posted a 2.53 ERA last year, which is the lowest in his past six seasons, even though his ERA fell below 3.15 in all of them. NL Cy Young winner Jacob DeGrom claims the third overall spot, as his 9.0 fWAR and 5.85 K/BB added together to form a 1.99 FIP. Number four starting pitcher Justin Verlander looks to just keep getting better, despite his 36 years of age. JV’s 12.20 K/9 and 1.56 BB/9 lead to a 2.52 2018 ERA, the lowest of his career. Corey Kluber stands as ace in a strong Indians rotation. His strikeouts were low in 2018, but so were his walks, leading to a mere 0.99 WHIP. Phillies breakout Aaron Nola ranks seventh overall. Last year, he put up a 2.37 ERA, accompanied by a 3.01 FIP. Nola is followed by another ERA breakout guy, Blake Snell, whose 1.89 mark was an incredibly encouraging sign for the Rays. Snell is followed by yet another breakout starter, and yet another Indian, Trevor Bauer. Bauer’s 2.21 ERA was backed up by a 2.44 FIP and 11.34 K/9. Astros acquisition Gerrit Cole returned to excellency last year in the form of a 2.70 FIP, 12.40 K/9, and 6.0 WAR, enough to bring him onto the list with the 10th spot. For top quality pitching mounds, check out Anytime Baseball Supply
Top 10 Middle Relievers
| The K Zone Overall Ranking | Mike’s Ranking | Ian’s Ranking | Mojo’s Ranking | Maddie’s Ranking | Jack’s Ranking | Aaron’s Ranking | |
| 1 | Josh Hader (MIL) | Dellin Betances (NYY) | Josh Hader (MIL) | Josh Hader (MIL) | Josh Hader (MIL) | Josh Hader (MIL) | Josh Hader (MIL) |
| 2 | Dellin Betances (NYY) | David Robertson (PHI) | Dellin Betances (NYY) | Dellin Betances (NYY) | Andrew Miller (STL) | Jeremy Jeffress (MIL) | Dellin Betances (NYY) |
| 3 | Andrew Miller (STL) | Andrew Miller (STL) | Andrew Miller (STL) | Andrew Miller (STL) | Dellin Betances(NYY) | Andrew Miller (STL) | Andrew Miller (STL) |
| 4 | Jeremy Jeffress (MIL) | Josh Hader (MIL) | Jeremy Jeffress (MIL) | Will Harris (HOU) | Jeremy Jeffress (MIL) | Seranthony Dominguez (PHI) | Chad Green (NYY) |
| 5 | Seranthony Dominguez (PHI) | Jeremy Jeffress (MIL) | Craig Stammen (SD) | David Robertson (PHI) | Seranthony Dominguez (PHI) | Jared Hughes (CIN) | Seranthony Dominguez (PHI) |
| 6 | Adam Ottavino (NYY) | Craig Stammen (SD) | Seranthony Dominguez (PHI) | Jeremy Jeffress (MIL) | Chad Green (NYY) | Dellin Betances (NYY) | Jeremy Jeffress (MIL) |
| 7 | David Robertson (PHI) | Adam Ottavino (NYY) | Will Harris (HOU) | Will Smith (SF) | Adam Ottavino(NYY) | Chad Green (NYY) | Adam Ottavino (NYY) |
| 8 | Chad Green (NYY) | Pat Neshek (PHI) | Adam Ottavino (NYY) | Craig Stammen (SD) | Will Harris (HOU) | Adam Ottavino (NYY) | David Robertson (PHI) |
| 9 | Will Harris (HOU) | Ryan Pressly (HOU) | Ryan Pressly (HOU) | Adam Ottavino (NYY) | Pat Neshek (PHI) | Will Harris (HOU) | Lou Trivino (OAK) |
| 10 | Craig Stammen (SD) | Chad Green (NYY) | Pat Neshek (PHI) | Pat Neshek (PHI) | Ryan Pressly (HOU) | Ryan Pressly (HOU) | Ryan Pressly (HOU) |
Even as versatile bullpens bullpens become more and more popular, relief pitching remains one of the most undervalued, but also one of the most inconsistent aspects of the game. With five out of six first place votes, Brewers breakout Josh Hader takes first place. The first of multiple Brewers on this list (and the trend of single teams dominating the list will continue), Hader punched out 15.82 batters per nine innings last season, adding up to a 2.23 FIP. Now 31 years old, Dellin Betances ranks second after his third straight season of 15 K/9, and a 1.95 xFIP in 2018. Andrew Miller comes in third after a down year last season. In the three years prior, his ERA’s read 1.44, 1.45, and 2.02. The second Brewer on the list ranks fourth, and his name is Jeremy Jeffress, who tossed 76 innings of 1.29 ERA ball last year. Seranthony Dominguez may get some saves this season, but since the Phillies have not yet named one closer, he sits at #5 on this list. Dominguez broke out in 2018 with 11.49 K/9 and an ERA just under 3.00. Another Yankee, (this time a new one), Adam Ottavino, takes the sixth spot. He managed to put up a 2.43 ERA last season in Coors, largely thanks to strong strikeout and ground ball numbers. David Robertson falls two spots below his fellow Philly after making 2018 his eighth straight season with at least 60 innings pitched an 10.4 K/9. Chad Green, the third Yankee on this list (and fourth if you count Chapman from the closers list), comes next. Unlike many of the pitchers around him, Green was able to limit walks while still obtaining K’s, leaving him with a 6.27 K/BB ratio. Houston reliever Will Harris edges out Ryan Pressly to become the only Houston reliever to make the conglomerate chart. Harris wrapped up 2018 with a 2.44 FIP and 4.57 K/BB. San Diego vet Craig Stammen wraps up the top 10, completing last season with a 2.19 FIP and only 1.94 BB/9 in 79 innings pitched.
Top 10 Catchers
| The K Zone Overall Ranking | Mike’s Ranking | Ian’s Ranking | Mojo’s Ranking | Maddie’s Ranking | Jack’s Ranking | Aaron’s Ranking | |
| 1 | Gary Sanchez (NYY) | Gary Sanchez (NYY) | Gary Sanchez (NYY) | Yasmani Grandal (MIL) | J.T Realmuto (PHI) | J.T. Realmuto (PHI) | J.T. Realmuto (PHI) |
| 2 | J.T. Realmuto (PHI) | J.T. Realmuto (PHI) | J.T. Realmuto (PHI) | Gary Sanchez (NYY) | Gary Sanchez (NYY) | Gary Sanchez (NYY) | Gary Sanchez (NYY) |
| 3 | Yasmani Grandal (MIL) | Wilson Ramos (NYM) | Yasmani Grandal (MIL) | J.T. Realmuto (PHI) | Yasmani Grandal (MIL) | Wilson Ramos (NYM) | Yasmani Grandal (MIL) |
| 4 | Wilson Ramos (NYM) | Yasmani Grandal (MIL) | Wilson Ramos (NYM) | Buster Posey (SF) | Wilson Ramos (NYM) | Yasmani Grandal (MIL) | Wilson Ramos (NYM) |
| 5 | Buster Posey (SF) | Francisco Cervelli (PIT) | Buster Posey (SF) | Willson Contreras (CHC) | Buster Posey (SF) | Yadier Molina (STL) | Yadier Molina (STL) |
| 6 | Willson Contreras (CHC) | Jorge Alfaro (MIA) | Francisco Cervelli (PIT) | Yadier Molina (STL) | Willson Contreras (CHC) | Buster Posey (SF) | Willson Contreras (CHC) |
| 7 | Yadier Molina (STL) | Willson Contreras (CHC) | Willson Contreras (CHC) | Wilson Ramos (NYM) | Yadier Molina (STL) | Willson Contreras (CHC) | Buster Posey (SF) |
| 8 | Francisco Cervelli (PIT) | Yadier Molina (STL) | Yadier Molina (STL) | Austin Barnes (LAD) | Francisco Cervelli (PIT) | Francisco Cervelli (PIT) | Francisco Cervelli (PIT) |
| 9 | Austin Barnes (LAD) | Buster Posey (SF) | Yan Gomes (WSH) | Francisco Cervelli (PIT) | Austin Barnes (LAD) | Austin Barnes (LAD) | Salvador Perez (KC) |
| 10 | Jorge Alfaro (MIA) | Kurt Suzuki (WSH) | Kurt Suzuki (WSH) | Yan Gomes (WSH) | Salvador Perez (KC) | Salvador Perez (KC) | Austin Barnes (LAD) |
On an annual basis, catcher is perhaps both the hardest, and the most fun position to rank. The overall offensive weakness, but also the defensive complexity of the position both contribute position two’s uniqueness. Along with the usual Fangraphs and Baseball Reference, Baseball Prospectus and Baseball Savant deserve special shoutouts here for helping to quantify defensive catching metrics. There was heavy controversy among the writers as to who deserved the top spot, but Yankee’s backstop Gary Sanchez won out. Sanchez didn’t even bat the Mendoza line in his shortened 2018, but he did carry a .197 BABIP, 12.3% BB%, and 18 home runs in 89 games, along with a powerful throwing arm. New Phillies’ acquisition J.T. Realmuto claims the second overall spot after putting up 4.8 WAR in 2018 and leading the league in pop time. New Brewer Yasmani Grandal, ranked third overall, was worth 4.9 WAR last season, in part due to his .349 OBP. Grandal may go down as the best pitch framer in history, as he was worth 16.3 framing runs last season. Grandal is followed by yet another player new to his 2019 roster, Wilson Ramos, who is just joining the Mets. Ramos has had his share of health issues, but he hit .308 in 111 games last season, good for 131 wRC+. Ramos, however, is followed by a player far from new to his team, Buster Posey. Posey’s decline has seemed imminent over the past few seasons, but he still put up a .359 OBP last year between catcher and first. The young Willson Contreras follows Posey, and while his offense was merely average last year, he hopes to rebound to his rookie and sophomore numbers in 2019, when he was worth 126 and 122 wRC+, respectively. Another veteran, Yadier Molina, ranks seventh. Molina nearly set a career high in power last year, with 20 home runs in only 123 games, and was worth over 2.0 WAR for the eleventh straight year in his career. His division rival, Francisco Cervelli, Cervelli’s .259/.378/.431 batting line last season helped him reach 2.6 WAR despite below average defense. #9 catcher Austin Barnes will step into the primary role as Dodgers’ catcher this season, and while his offense ranked below average last season, he was the best catcher in baseball in 2017 on a per at bat basis (142 wRC+, 3.7 WAR in 102 at bats). Finally, thanks solely to Mike’s extreme optimism, Jorge Alfaro made the charts at the tenth position. Alfaro was actually worth 3.1 wins last season, thanks to good pitch framing, and a great arm, with a pop time of just 1.94 seconds.
Top 10 Closers
| The K Zone Overall Ranking | Mike’s Ranking | Ian’s Ranking | Mojo’s Ranking | Maddie’s Ranking | Jack’s Ranking | Aaron’s Ranking | |
| 1 | Edwin Diaz (NYM) | Edwin Diaz (NYM) | Edwin Diaz (NYM) | Kenley Jansen (LAD) | Kenley Jansen (LAD) | Blake Treinen (OAK) | Edwin Diaz (NYM) |
| 2 | Blake Treinen (OAK) | Blake Treinen (OAK) | Kenley Jansen (LAD) | Edwin Diaz (NYM) | Edwin Diaz (NYM) | Edwin Diaz (NYM) | Blake Treinen (OAK) |
| 3 | Kenley Jansen (LAD) | Aroldis Chapman (NYY) | Blake Treinen (OAK) | Blake Treinen (OAK) | Craig Kimbrel (FA) | Craig Kimbrel (FA) | Aroldis Chapman (NYY) |
| 4 | Aroldis Chapman (NYY) | Kenley Jansen (LAD) | Aroldis Chapman (NYY) | Craig Kimbrel (FA) | Aroldis Chapman (NYY) | Aroldis Chapman (NYY) | Kenley Jansen (LAD) |
| 5 | Craig Kimbrel (FA) | Corey Knebel (MIL) | Corey Knebel (MIL) | Sean Doolittle (WSH) | Blake Treinen (OAK) | Felipe Vazquez (PIT) | Craig Kimbrel (FA) |
| 6 | Sean Doolittle (WSH) | Sean Doolittle (WSH) | Craig Kimbrel (FA) | Aroldis Chapman (NYY) | Corey Knebel (MIL) | Kenley Jansen (LAD) | Corey Knebel (MIL) |
| 7 | Corey Knebel (MIL) | Brad Hand (CLE) | Sean Doolittle (WSH) | Roberto Osuna (HOU) | Roberto Osuna (HOU) | Sean Doolittle (WSH) | Brad Hand (CLE) |
| 8 | Roberto Osuna (HOU) | Roberto Osuna (HOU) | Roberto Osuna (HOU) | Felipe Vazquez (PIT) | Sean Doolittle (WSH) | Brad Hand (CLE) | Sean Doolittle (WSH) |
| 9 | Felipe Vazquez (PIT) | Craig Kimbrel (FA) | Jose LeClerc (TEX) | Corey Knebel (MIL) | Felipe Vazquez (PIT) | Roberto Osuna (HOU) | Roberto Osuna (HOU) |
| 10 | Brad Hand (CLE) | Felipe Vazquez (PIT) | Felipe Vazquez (PIT) | Jose LeClerc (TEX) | Brad Hand (CLE) | Jose LeClerc (TEX) | Felipe Vazquez (PIT) |
Closers are often the most existing baseball players to watch, and they often exhibit some of the nastiest pure stuff in the game. Edwin Diaz, just traded from the Mariners to the Mets this past offseason, takes the top spot. Diaz displayed 15.22 K/9 last season and a 1.78 xFIP last season, leading to 3.5 WAR as a relief pitcher. Blake Treinen broke out at age 30 last year in the form of a 0.87 earned run average. He put up 3.6 total WAR. #3 closer Kenley Jansen actually took a step back last season, but in the five years before that, his FIPs read 1.31, 1.44, 2.14, 1.91, and 1.99. Velocity machine Aroldis Chapman places fourth overall, with an astounding 16.31 K/9 mark, and a 2.09 FIP. Craig Kimbrel still doesn’t have a team, but word is that he’s lowering his asking price, so once he signs and prepares for the season he could be ready to live up to his #5 preseason rank. Kimbrel put 13.86 K/9 in 2018, albeit with some lost control. He’s only a year removed, though, from one of the greatest closer years all time, when he struck out 16.43 per nine innings while walking only 1.83. Underrated Nationals arm Sean Doolittle places sixth, with a 1.89 2018 FIP and exactly 10.00 K/BB. Despite a questionable mid-season demotion, Corey Knebel experienced his third straight season of 14 K/9 last year. He also put up a 2.40 xFIP, enough to land seventh. Roberto Osuna places eighth overall. He had serious off-the-field issues last season, but still walked less than a batter per game when playing, good for a 2.37 ERA. Filipe Vazquez places an the ninth best closer in baseball. A 2.43 FIP led him to 2.1 total WAR. Finally, Brad Hand was traded to the Indians at the deadline, and he helped to solidify an otherwise shaky bullpen, topping 4 K/BB.
Top 10 Players At Each Position
Every year, The K Zone engages in our tradition of ranking the top 10 players at each position. If you want to embarrass us, you can look at our charts from before the 2018 and 2017 years. Rankings, made during the preseason, are based on our projections for the coming season, so the following are our predictions for who will finish as a top 10 player at each position, and how high they will fall among the top 10 in 2019:
Top 10 Starting Pitchers
Top 10 Middle Relievers
Top 10 Closers
Top 10 Right Fielders
Top 10 Center Fielders
Top 10 Left Fielders
Top 10 Third Basemen
Top 10 Shortstops
Top 10 Second Basemen
Top 10 First Basemen and Designated Hitters
Top 10 Catchers
And of course, the Top 20 Overall Baseball Players
Image attributed to Sports Illustrated. Cited statistics in all articles are from Fangraphs, Baseball Reference, Baseball Savant, and Baseball Prospectus.
Exploring the Crossover Effect
– The K Zone –
Exploring the Crossover Effect, by Ian Joffe
February 25th, 2019
It is a well documented fact that Joey Votto is one of my favorite baseball players. I wrote my very first opinion article about how good he really was, and I have drafted him in fantasy baseball for several years in a row. However, this year my seemingly everlasting love for the Red’s first baseman hit a snag. Votto’s home run power plummeted in 2018 to 12 total bombs, his lowest full-season total ever, yet he did that despite maintaining his regularly high average exit velocity (88.1 mph) and launch angle (13.3 degrees). His line drive rate (31.4%) also remained exceptional. My first thought was that Votto was having a lot of near misses, balls were hit hard but died on the warning track. But, the Statcast data contested that theory too, as his barrel rate of only 6.7% matched his low home run total.
So, Votto had his normal high average exit velocity and strong launch angle, yet he was rarely getting barrels, which is defined as combination of the two. My theory became that he was still hitting balls hard and still hitting balls high, but in 2018 those types of hits did not coincide on the same at-bats. He had a lot of soft flyouts, and a lot of hard groundouts, but few well-hit balls angled for the stands. At first thought, one would think those two events — hitting balls hard, and hitting balls high — are independent. In other words, doing one does not make the other more likely on any specified at-bat. If this were the case, then Votto would be a victim of bad luck. One could expect his hard hits to coincide with his high hits at a normal rate again next season, and we can imagine 2018’s lack of intertwined hard and high hits like a low BABIP, where it will regress towards a mean. However, it is also possible that the two events are dependent, and that certain types of players are better at doing both at once than others. In that case, it is possible that Votto has experienced a legitimate decline in his skill level.
To test whether the events were independent or not, I examined data from 332 hitters that had at least 150 balls in play in 2018. The goal was to examine how often their hard hits and high hits actually coincided, versus how often they should have coincided, and to test whether those numbers differed by a reasonable margin. For this study, I looked at a statistic that I am calling crossover (CR), which is defined by a baseball hit with at least 99 mph of exit velocity and at least 22 degrees of launch angle. It’s similar to barrels, but a little less complicated. Barrels did not work for my purpose because their required launch angle differs based on exit velocity. The numbers 99 and 22 are admittedly somewhat arbitrary, but were decided upon by looking at where distribution of home runs started to accelerate. Crossover rate, or CR%, is defined as crossovers divided by crossover opportunities. A crossover opportunity, in turn, is the sum of a players hard hit balls and high hit balls, minus crossovers (so that crossovers only count for one at bat). The league average CR% was 13.1%, and Joey Gallo led the league with a 43.7%, although that number is over 10 points higher than the next best, which is Tyler Austin at 32.5%. From there, a right-skewed distribution starts:
Next, I made a formula to determine the expected crossover rate of every player based on their hard hit rate and high hit rate. A player’s total expected crossovers (xCR) is the product of his hard hit rate and his high hit rate, times his number of ball in play. To find expected crossover rate (xCR%), put xCR over the sum of hits and high hits minus xCR, like with experimental CR%. My final statistic was CRd, or crossover differential. CRd is defined as CR% minus xCR%, times 100 (to make it more readable). A positive CRd indicates that a player had more crossovers than expected, and a lower, negative CRd indicates that a player had fewer crossovers than expected. A CRd of 0.0 means that the player’s crossover rate is the same as the expected number. Here is the distribution of CRd:
Interestingly, the league average value was -2.3. The league leader in CRd was, once again, Joey Gallo with an astronomical 18.1, with Tyler Austin next at 10.5. After Austin came a new name, Matt Joyce, at 10.4. At the bottom of the charts was Yuli Gurriel, at -13.0, followed by Jose Bautista at -12.6.
The next step was to determine if having a higher CRd than expected meant that a player was lucky, or meant that a player was skilled. To do this, I analyzed how consistent CRd was between two halves. If a player’s first half CRd was predictive of the second half, it could be legitimate skill. If it was not, CRd is due to luck. Here is the scatter plot comparing the two halves for players who had sufficient balls in play in each:
From that plot, is looks like there is a very significant correlation between crossover differentials in each half. The statistics would back your eye test up, as the graph produces a resounding r value of 0.51 and a P-Value just over 10^-12, meaning the probability of crossover differential being entirely luck is, for all intents and purposes, zero. In fact, this makes crossover rate seem like even more of a controllable, intentional skill than the extreme peripheral of hard hit rate itself, which has an r value of 0.33.
If half-to-half correlation is strong, I would expect the year-to-year correlation to be even stronger, due to the larger sample. My assumption was correct:
This chart churned out a correlation coefficient of 0.61 and another near-zero P-value. Interestingly, there was a lot less variation in 2017 than 2018, and no extreme upper outliers. I can’t explain exactly why that is, but I can confirm that players like Gallo, who led the league in 2018, also did so in 2017, just with a lower overall number. To build on the case of the high stability of CRd, look at how close most players’ 2018 numbers were to their 2017:
| Change in CRd (In Either Direction) | Percent Frequency |
| 0-1 | 24% |
| 1-2 | 22% |
| 2-3 | 14% |
| 3-4 | 15% |
| 4-5 | 9% |
| 5+ | 16% |
Almost a quarter of individuals differ by less than one percentage point between two years of tracking this statistic. 60% of players will deviate in CRd by less than 3 percentage points between two seasons. That’s a very low deviation between years, especially compared to very volatile statistics like batting average. To be honest, these results are the opposite of what I expected. I thought that hard hits and high hits would be independent of one another, and that differentiation would be up to luck. I thought that the statistic would regress to a league average, not a career average. But, it appears that my initial hypothesis was wrong. CRd is a very stable peripheral that is grounded heavily in the skill to do two important things at the same time.
Let’s get back to my friend Joey Votto. My original expectation was that he was getting unlucky by having his hard hits and high hits fall on different at-bats. I was wrong for two reasons. First, crossover rate is not up to luck. Second, his 2018 CR% was actually higher than his xCR%, 16.6% to 12.2%, so, even if it were luck, that would not explain his drop in power. Instead, we have to look at Votto’s case through what we do know: that crossover differential is based in skill, meaning if a player keeps the same skills, they should keep a similar CRd. Votto dropped 3.37 points in CRd between 2017 and 2018, from 7.68 to 4.31. That’s puts him in the bottom 20% of the league in CRd, which is a convincing argument that he has legitimately down-skilled. Votto is still an incredibly valuable MLB and fantasy asset due to OBP alone, but he is 35 years old, and I sadly must admit that it’s possible we will never see his old power totals again. In fact, based on what I have found in this article, I would not bet that he will hit for power again.
I used this set of stats to analyze Joey Votto, but you could, of course, just as easily apply it to any player. For your convenience, I have taken all the statistics invented for this article and written them into the following Google Sheet files:
2018 Stats
2017 Stats
Stats Glossary
Enjoy!
If you liked this article, please follow The K Zone on Twitter and be the first to know when more original research, opinion, and interviews, come out!
Sources:
Baseball Savant
Fangraphs
Baseball Reference
MLB.com
Photographs Attributed to:
US Presswire
MLB.com
The Mysterious Case of Ryan Schimpf
– The K Zone –
January 22, 2019
The Mysterious Case of Ryan Schimpf by Mojo Hill
By now, most hardcore baseball fans have heard about Joey Gallo and how he is already having one of the strangest MLB careers of all time. Gallo, the man who has more career home runs than singles and has a career batting line of .203/.317/.498. No other qualified player in the history of baseball has ever had an average under .205 while maintaining an OPS over .600. And not only is his OPS above .600, but it’s actually a very good .815. And he does it with a 38% strikeout rate. Amazing. And Gallo, who has one of the highest fly ball rates in baseball, has never hit a sac fly. Hard to believe, right?
I present to you the man, the myth, the legend: the one and only Ryan Schimpf, who would probably have a lot to talk about if he were to have lunch with Joey Gallo. Schimpf is a lot like Gallo, but arguably even more extreme. In case you’ve never heard of Schimpf, allow me to enlighten you.
Unlike Gallo, Schimpf is not an everyday MLB player, which is why he is not known nearly as well and doesn’t have as many MLB plate appearances. But Schimpf’s sample size is still large enough (534 plate appearances) that we can have some fun looking at the numbers.
Schimpf made his MLB debut for the Padres in 2016 after spending seven years in the Blue Jays organization. When San Diego called him up for the first time at the age of 28, he was murdering Triple-A pitching to the tune of a monstrous 201 wRC+ and .373 ISO. And he hit well in his debut season, which turned out to be his most successful season to date (so far). Recording 330 plate appearances over 89 games, the second and third baseman batted .217/.336/.533, good for a 128 wRC+ and 2.5 fWAR.
Schimpf took a step back in 2017, playing 69 games in Triple-A with a 98 wRC+ and 53 games for the Padres, where he hit .158/.284/.424.
After 2017, Schimpf was shuffled around a bit. After being traded to the Rays in the offseason for minor league shortstop Deion Tansel, he was designated for assignment and traded to Braves for cash in early March. Then on March 31, he was traded again, this time to the Angels for catcher Carlos Perez.
Schimpf only played five games for the Angels last year, going 1-5 with two walks and a home run. He was released in May after hitting a very underwhelming .178/.288/.355 in 30 games for the Salt Lake City Bees.
What fascinates me is how his value is being perceived by Major League clubs. Clearly, they are not very high on this guy. He had a solid minor league track record for the Blue Jays, yet they never promoted him to the big leagues. It took a monstrous season for the El Paso Chihuahuas for the Padres to finally give him his chance at age 28. He’s been thrown around in multiple trades, and each time he’s essentially been traded for peanuts or just straight up released.
Do teams not realize just how valuable Schimpf is and can be? As mentioned earlier, he accrued 2.5 fWAR in just 330 plate appearances. His career batting line through 534 games is .195/.318/.496. Yes, the average is low. But the OBP, aided by a 13.3% walk rate, is acceptable, along with a massive .496 SLG.
In fact, among all hitters in MLB history with at least 500 plate appearances, Schimpf’s .300 ISO is the foruth best of all time, behind only Babe Ruth, Mark McGwire, and Barry Bonds.
Think about that. He’s hit for more power on a rate basis than sluggers such as David Ortiz, Alex Rodriguez, and Mickey Mantle, just to name a few. He may not put the ball in play often, but man when he does, he hits it really, really hard. Hey, and you know who’s fifth on that list? Joey Gallo, of course.
Despite his obvious flaws, Schimpf is a valuable hitter. The walks and power make up for the low average and high strikeout rate, just like in the case of Gallo. Gallo’s career wRC+ is 109, and the Rangers have granted him a starting role over the last two seasons. In each of those seasons, he’s hit for a 121 and 110 wRC+, respectively, with exactly 2.8 fWAR in each year. Schimpf’s wRC+ is 114, and he even has more defensive value being a second and third baseman as opposed to Gallo, who the Rangers announced will no longer be playing third base and will stick to first base going forward. Yet Schimpf can’t find a Major League job, and Gallo can.
Some teams will look at the .195 batting average and shudder. But come on. We’re in 2019 now. This new age of analytics has taught us that batting average is a mostly meaningless tool in terms of evaluating a player’s actual offensive production and value. Getting on base and hitting for power, and Schimpf does both those things in spite of his low average. The Rangers have accepted and even embraced this three-true-outcomes characteristic with Gallo, and it’s about time for other teams to realize the same with Schimpf.
Now, there are some things going against Schimpf. He is already 30 years old, as opposed to Gallo who is still just 25. Schimpf took forever to receive a promotion to the Majors, and despite a solid fifth round selection, was never regarded that highly as a prospect. He’s simply someone that scouts have doubted for his entire career because of all the strikeouts and pop-ups.
And in today’s day and age, when players are hitting home runs and striking out more than ever, guys with power and not much else aren’t being valued that highly since power has become a more common skill around the league. This is especially true for the one-dimensional first base/DH types, who are struggling to even get Major League deals at this point. While Schimpf isn’t a great defender by any means, he does at least play a passable second and third base, which boosts his value a little bit. But even in this new age of analytics when people are starting to look beyond batting average to value hitters (as they should), Schimpf’s one-dimensional toolset as well as his age and lack of fanfare are hurting him.
Still, he’s a 30-year-old infielder with pop, so he definitely has some baseball left in him and I would be stunned if he didn’t pick up at least a minor league deal.
Oh, and one last fun (or depressing) tidbit about Schimpf. Schimpf is the proud owner of one of the worst Spring Training performances in MLB history. Before the 2018 season while he was playing for a spot on the Braves’ Opening Day roster, he went 0-30 with a whopping 19 strikeouts. Yep, you read that correctly. Wow. Maybe that’s why teams don’t like him.
Seriously, though, Schimpf is a baseball player with undeniable flaws in his game, but also some admirable qualities that I think are being severely undervalued by teams. I don’t think teams realize just how much power he actually has. And with his value at a low point right now, I believe that any MLB team should be willing to give him a look on a low-risk minor league contract, especially a team in need of quality infield depth.
Thanks for reading! If you enjoyed this article, you might want to check out my analysis of Austin Barnes, or if you like interviews, Mike has plenty of those. You can also follow us on Twitter and Instagram for updates on when we publish a new article or interview. You can also follow me on Twitter.
The Trouble with Tommy John
– The K Zone –
The Trouble with Tommy John, by Ian Joffe
December 24th, 2018
Up through 2015, baseball was noticing a troubling trend: Tommy John surgeries – in the major leagues, minor leagues, and even among youth – were on the rise. In more recent years, the number of torn UCL’s has started to fall back, at least among professionals, but the concern is still ever-present, especially given the 12-16 month recovery time and far-from-perfect success rate. The rise in Tommy Johns has led a lot of doctors and baseball analysts to chime in with theories on why so many more players are needing the surgery. In this article, I wanted to test a few of the leading theories on which risk factors are significant in increasing the odds of needing Tommy John.
I’ll get to my own research later, but I wanted to start with a few theories that have already been tested by others. The first have to do with pitch selection, and these theories are, to say the least, contradictory. Some hypothesize that an increase in fastballs thrown has led to the spike in Tommy Johns, but at the same time others argue that breaking ball usage ultimately does pitchers in. I had the hardest time finding research to back up the curveball theory. An entry to the American Sports Medicine Institute’s journal found that there is no correlation between throwing curves and needing Tommy John. In terms of the fastball theory, one study from the Journal of Shoulder and Elbow Surgery argues that there is a correlation between fastball usage and torn UCL risk. However, a later study (I couldn’t find the original link) from the American Sports Medicine Institute says that there is no correlation between pitch selection and Tommy John surgery. There is a potential lead here, but it’s not conclusive. High fastball selection may or may not be a Tommy John risk.
One theory that seems to have more widespread backup is that higher velocity can risk Tommy John surgery. This article, by the American Journal of Sports Medicine, suggests that higher velocity may very well lead to higher risk of elbow injury. Another piece, also from the AJSM, makes the same case, and goes as far as to say that pitch velocity is the most predictive element of Tommy Surgery, but it still limits r^2 to 0.07. Specifically, it suggests that peak pitch velocity, as opposed to mean velocity, is a risk factor. These findings are corroborated by this Fangraphs community research article, which details exactly how the data was found.
Based on all of that, it seems that while pitch selection is not a fully proven theory, there is evidence that high velocity leads to heightened Tommy John risk. That begs the question, “What can be done about it?” The obvious answer is “throw less hard,” but it’s very unlikely that pitchers will be willing to sacrifice an essential part of their game to reduce health risks. That especially goes for younger pitchers who are being judged for their tools rather than a career’s worth of stats. In today’s game, when draft signing bonuses are so large, and initial free agent contracts are even more massive, it is borderline unreasonable to ask a young pitcher to risk all their value to improve their health. Additionally, the players most at risk are high-velocity pitchers, and high-velocity pitchers are the ones who depend most on their speed (when combined with other tools), and are therefore least likely to be able to make a change without taking a potential hit to their value. In my research, I wanted to look at changes that I thought could be made in pitchers without really hurting their value.
One proposed theory for the increase in Tommy Johns is sports specialization. This theory is not only a logical causation, but is heavily respected in orthopedic circles, and seems scientifically sound. Unlike the other theories, there are pages after pages on Google that champion this one, but here are the first three. As the theory goes, high school baseball players, especially those looking for scholarships, are always looking to gain a competitive edge. So, a few decide to do baseball year-round, in order to get better. Then, to catch up, others had to do the same thing. Soon enough, every serious baseball player was practicing baseball all year in high school. I’ve seen a few different colloquial explanations as to why this is bad – “the UCL needs rest;” “an arm only has to many bullets;” “one needs to strengthen different muscles” – and I’m not sure which is the closest to the real scientific explanation, but either way the negative aspects of specialization seems like a widely accepted theory among doctors and casual baseball fans alike.
To test the theory, I grabbed a data set of pitchers who had Tommy John surgery between 2015 and 2018. I then built a Python webscraper to sort through MaxPreps data, which keeps tracks of all high school athletes and their statistics. The program searched for the player on MaxPreps, and then checked how many sports he had played in high school. Unfortunately, I was only able to get data on about a third of the players who had Tommy John surgery during the given period. Some players had unusual last name configurations (I’m talking to you, Jose de Leon), others did not go to high school in the United States, and some went to high school before MaxPreps was founded in 2002 and later popularized. The biggest issue, though, was that several high school players had the same name as those who I was looking for. I was able to further filter my search using state, but if two people had the same name and played high school baseball in the same state, which happens more often than one would imagine, I had to remove them from my data set. In total, I was left with a sample size of 28, which while small, is still reasonable enough to mean something.
Out of those 28 MLB players who had a torn UCL, 7 played multiple sports and 21 only played baseball. That’s a 25% multiple-sport rate. In a control sample of random baseball players who I could specify on the MaxPreps database, 155 out of 596 played multiple sports in high school, or 26.0%. Based on this, it is safe to conclude that I found no evidence that sports specialization is a Tommy John risk (my chi-square derived P-value was a hardy 0.904). To be clear, I was dealing with a limited sample. My research also says nothing about the very real risk of needing Tommy John surgery while in high school. But, based on that, I see little reason to believe that playing multiple sports in high school leads players to have significantly better odds of staying healthy in the majors.
The most common method of preventing injury in MLB is the pitch count. Every team practices it, and pays special care to number 100. According to common knowledge, high pitch counts risk injury, and managers will take pitchers out when the count gets high because of it. That’s not to say that injury risk is the only reason pitchers are removed in the late game; batters get better multiple times through the order, pitchers get worse as they fatigue, and relievers are often just better than starters. But, injury risk is usually part of the equation, and almost every manager would probably say that high pitch counts do risk injury. So, pitch count is the second factor that I set out to test.
For this test, I gathered data on starters alone, because they are more similar to each other in use (at least for now). Like last time, I was not able to look at every starting pitcher in my data set, so I once again ended up with a very small sample, only 13 starters. So, for the last time, I want to reiterate that because of that, my research is more of a starting point on the subject than an end. Anyways, the first look I took at pitch count had to do with the game of the injury. Here were the results:
The points seem to be scattered rather randomly across the number line. The chunking of data points is a little odd, but I would expect the gaps to fill as n increased. The overall lesson here, though, is that pitch count does not seem to contribute to torn UCL risk. A pitcher is about as likely to tear their ligament on the 40th pitch as on the 90th. In fact, one might note that there are zero data points past the 100th pitch. In a larger sample, there may have been a few, but the point is clear: removing pitchers before or around the 100th pitch does absolutely nothing to decrease injury risk within that game. Tommy John risk does not increase as the game goes on, and players should not be pulled early simply to avoid getting hurt, because that does not work.
While pitch count has no influence on injury odds within a game, it is possible that high pitch counts have a hangover effect, making a pitcher more likely to get hurt in their next start. So, looking at the same lucky 13 pitchers, I charter their pitch count from the start before the one in which they got hurt:
The average among these pitchers was 85.3, 7 pitches below the league average for a start. Even when those lower two outliers are removed, the mean only goes up to 93.5, a pitch and a half above the league average. From this data, it does not appear that lowering pitches in the previous start leads to lowered injury risk overall, for pitchers who got hurt threw about the same number of pitches in their pre-injury start as the average pitcher who will not get hurt. So, that’s one more reason that pitch count should be ignored as a factor for injury risk.
Pitch count on a start-by-start basis appears to be a complete non-factor in the Tommy John question. Still, though, I wanted to give pitch count one last chance and take a look at seasonal trends. Perhaps each individual start is insignificant, but if a player throws too many pitches in one season, they become a higher risk for the surgery. So, to test this, I found the amount of pitches thrown in the season of the injury for Tommy John recipients.
Think of the blue histogram as an extended version of the league average dot, showing how many pitches every pitcher has thrown per season from 2015-2018. The singular red dots exist only on the x-axis, and show the seasonal pitch count of injured pitchers. For the first half of the graph, the density of the red dots seem to match the density shown by the blue histogram. But then, there are no dots at all in the second half on the chart. This shows that seasonal pitch count has no effect on injury risk. Injured pitchers did not throw more pitches than other pitchers. In fact, injured pitchers are completely left out of the upper range of the graph, a range that many healthy pitchers got to. This is to say that there were no pitchers from 2015 to 2018 that got Tommy John surgery because they threw an abnormally high amount of pitches in the year of their surgery.
Single-year trends showed no evidence that season pitch count had an effect on Tommy John risk. The very last step of my study was to examine multi-year trends. First, I took a look at changes in pitches from year to year. Often, teams will say that normally they would not cap a pitcher, but because he only threw so many pitches in the previous season, that number could only increase by a certain amount next season. For example, if a pitcher throws 1500 pitches in 2017, the manager may conclude they can only throw 2000 pitches in 2018. To test the theory of pitch increase, I charted the percent change in pitches per season for injured starters. Unfortunately, I did not have access to minors pitch data, so I had to remove rookies from the set, once again shrinking the sample.
Like all graphs before, the pattern on this one shows how small the effect of pitch count is on injury risk. Most pitchers who needed Tommy John threw far fewer pitches in the season of the injury compared to the season before, not far more. Only one pitcher experienced a severe workload increase, and only two had small workload increases. Torn UCL’s had no tendency to occur more often to pitchers with heavy workload increases.
Since two-year trends seemed to have no effect on Tommy John risk, the next multi-year trend to turn to is a player’s career span. I don’t have the pitch count data for the players’ careers, and even if I did it wouldn’t mean much because it wouldn’t account for the bullets on their arm in high school, college, and perhaps most significantly MLB practice. However, I do have player ages. In the career-pitches “the arm only has so many bullets” theory, it is suggested that as players tear their UCL’s after a certain number of career pitches, thus as a pitcher’s total career pitches increases, their odds of hurting themselves increase too. Age increases at the same rate as total pitches, so it would follow that as age increases, likelihood of surgery also would increase. This is not the trend that I found in the real player ages. The average MLB player age in the seasons that I studied was 28.9. Rather tragically, the average of a Tommy John pitcher was a year younger, 27.9. This discredits the theory that injury risk increase is directly proportion, or even somewhat proportional, to career pitches thrown, as players who were younger actually turned out to have the higher injury risk in the data.
From all my research and all the research of my peers, we are left with few clues about the causes of torn UCL’s. The only useful piece of information was the study about fastball velocity, but even that just barely had any predictive power. This inability to find causes does not signify a weakness in modern research, but rather a weakness in the traditional views of health. It’s easy to look at injuries as directly caused events. Just like how I could stub my toe because I jammed it against the wall, a pitcher tears his UCL as a direct effect of more complex causes. Instead, health should be looked at as a skill, like, for example, batting. Everyone is born with some degree of batting skill, whether it be very high or very low. People can improve on that natural ability through techniques like diet and practice, and then bring their total skill level to the plate. But, once at the plate, their chance of getting a hit is rather random. A .333 hitter, for example, gets a hit one in three at bats, and it is more or less random which of those three at bats he got a hit in. Similarly, players are born with good or bad health skills, which they can work on improving through techniques like proper stretching. But, once they bring that health skill to the table, which may be represented by a probability of injury, the likelihood that an injury occurs within that probability is more or less random. It’s impossible to know if an injury is more likely on the 10th or 100th pitch of a game or season because the pitch at which the injury occurs should be viewed as randomized. This skill-based view of health is more accurate to the data, and scientifically assumes the null hypothesis. If teams thought like that, it may lead them to more successful pitcher use.
If you liked this article, follow us on Twitter and be the first to know when more original research, articles, and interviews come out on the site.
Image Attributed to Sports Illustrated
The K Zone 