Devon Travis is having a rough start to the 2017 season. As I’m writing this, he has “hit” .148/.207/.222, good for a wRC+ of 16 and WAR of -0.5. Fans are openly wondering if he should be sent back to triple-A. But all is not lost! If you look past the surface stats, there is hope for the young Blue Jay. Let’s explore.
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Out of the many differentiae that make up José Carlos Altuve’s thumbprint on baseball, from his 5’ 6” stature, to getting cut from his first tryout with the Astros but showing up the next day anyway, his groundball percentage would likely not rank toward the top for most fans of the Venezuelan. However, most of said fans have not seen this graph.

As you can see, after decreasing his rate consistently for four seasons, Altuve is hitting more groundballs and hitting more balls toward right field in 2017 than at any other point in his career. Although this is in a sample of just 88 plate appearances, and may be a statistical blip, I think that with a hitter like Altuve it is worth investigating.

Altuve’s BABIP is also a high, even for him, .393. Apart from being the most satisfying stat to say out loud, BABIP is also the one thing that alters early-season statistics more than any other, but .393 for Altuve isn’t like the clearly unsustainable .455 that Steven Souza Jr. is currently running. It’s just .044 points higher than his last season rate, so while it’s probably not sustainable (DJ LeMahieu led the league in BABIP last year at .388) it’s not lifting him to his 134 wRC+ by itself.

So what’s the reason for this? Is it a change in approach? A reaction to what other teams are doing? Teams don’t appear to be shifting Altuve, so it doesn’t look like he’s trying to beat them by hitting grounders through an open hole. It does, however, look like maybe teams are attacking him down and away slightly more than in the past. Here are Altuve’s heat maps the last two seasons.

2016

2017

There does appear to be a slight uptick in balls in the bottom corner of the zone, but it’s hard to call it a novel trend when the basic strategy against Altuve since he came into the league remains basically the same: down and away.

But the story doesn’t end there! There are two things that do appear to be starting a trend.

First, his Zone% (meaning the percent of the time that opposing pitchers throw him a strike) has been consistently trending downward since he came into the league. It’s currently at 46% according to Trackman, which is the lowest mark of his career.

Second, his Fastball%, according to Baseball Info Solutions, is at 49.7%, which is also a career low, and actually over 8 percentage points lower than the rate he’s seen in his career.

Now, this all makes intuitive sense. Altuve’s power has risen in recent seasons, and it looks like pitchers have adjusted accordingly; no surprise there. What I thinks is worthwhile in all this is that Altuve is adjusting right back. Before this past weekend in Tampa Bay, Altuve had no home runs on the year. He’s been going with what pitchers have been throwing him. They want to throw him offspeed down and away, and he’s been going with it, exchanging some of the power he took last year to keep his overall offensive profile as one of the league’s elite hitters.

Introduction

One of the most-discussed issues in Major League Baseball is the consistency of the strike zone. The rule-book strike zone states “The STRIKE ZONE is that area over home plate the upper limit of which is a horizontal line at the midpoint between the top of the shoulders and the top of the uniform pants, and the lower level is a line at the hollow beneath the kneecap. The Strike Zone shall be determined from the batter’s stance as the batter is prepared to swing at a pitched ball.” After watching games throughout the regular season and playoffs, it is easy to realize this is not the strike zone that is called. Each umpire has tendencies and dictates his own strike zone and how he will call a game. With the rise of PITCHf/x and Trackman in the last few years, umpires have been increasingly monitored and judged for their accuracy and impartiality. For this reason, umpires are criticized for incorrect calls more than ever before and I believe are now trending towards enforcing the rule-book strike zone more than in years past.

The purpose of this research will be to do two things. First, I will focus on identifying overarching themes where I look at finding how umpires are adjusting to modern technology but also how the rule-book strike zone is not the strike zone we know. After this, I will dive into a few umpire-specific tendencies. The latter would be helpful to teams in preparing their advance reports by knowing how certain umpires call “their” strike zone dictated by situations in a game.

Analysis

Using PITCHf/x downloaded through Baseball Savant, I have looked at major-league umpires since 2012 in regards to their accuracy in correctly labeling pitches, primarily strikes, and their tendencies dictated by specific situations. While the height of the strike zone is often influenced by the height of the batter, there are other factors to take into account such as the how the batter readies himself to swing at a pitch. Unfortunately, the information publicly available to conduct this research does not include the batter handedness, pitcher name, or measurements of individual strike-zone limits. For this reason, a stagnant strike zone serves our needs best. The height of the strike zone shall be known as 1.5 feet from the ground to 3.6 feet from the ground. This is the given strike zone of a batter while using the pitchRx package through RStudio when individual batter height is not included.

All PITCHf/x data is from the Catcher/Umpire perspective, having negative horizontal location to the left and positive to the right. The width of home plate is 17 inches, 8.5 inches to both sides where the middle of the plate represents 0 inches. After calculating the average diameter of a baseball at 2.91 inches, we add this to the width of the plate. Therefore our strike-zone width will be 17 + 5.82, or 22.82 inches. The limits we will then set are going to be -.951 to .951 feet (or 11.41/12 inches). Throughout the paper I will be referring to pitches that fall within the boundaries of our zone as “Actual Strikes” and pitches correctly identified as strikes within this zone as “Correctly Called Strikes.”

Called Strike Accuracy By Year

As Table 1 shows, correctly identifying strikes that fall in the parameters of the rule-book strike zone has risen substantially. While 2015 has a higher percentage of correctly called strikes, 2016 PITCHf/x data from Baseball Savant was incomplete, with 28 days’ worth of games unavailable at the time of this research. A rise of 5.90 percent correctly called strikes from 2012 to 2015 shows the rule-book strike zone is being more strictly enforced.

While this provides some information, we can also look into where strikes are correctly being called using binned zones. Understanding that the evolution of umpires over the last five years is taking place and trending toward correctly identifying strikes more today than in years past, we can analyze where, in the strike zone, strikes have been correctly labeled.

Called Strike Accuracy by Pitch Location

In Table 2, we can see a tendency among umpires. Strikes are called strikes more routinely over the middle of the plate and to the left (from umpire perspective). As I have mentioned before, the publicly available PITCHf/x data I used did not include batter handedness and I am unable to determine who is receiving the benefit or disadvantage of these calls. Presumably from previous research on the subject, lefties are having the away strike called more than their right-handed counterparts, explaining the separation between correctly identifying strikes in zones 11 and 13 versus 12 and 14.

Binned Strike Zone

While one may argue that there should not be strikes in these bordering zones, we consider any pitch that crosses any portion of the plate a strike. Due to our zone including the diameter of the baseball on both sides of the plate, the outer portion of the plate includes pitches where the majority of the ball is located in one of these zones.

Called Strike Accuracy by Individual Umpire

When gauging an umpire’s ability to correctly identify a rule-book strike, an 85.67% success rate sets the mark with Bill Miller, while Tim Tschida ranks at the bottom of this list, only calling 71.57% correctly. We can infer from Tables Three and Four along with Table One, that while umpires are calling strikes within the strike zone more often, they are still missing over 17% of these pitches. It is important to note that this information does not take into account incorrectly identifying pitches outside the rule-book strike zone as strikes, which when considering an umpire’s overall accuracy, should absolutely be taken into account.

Called Strike and Ball Accuracy by Count

One of the most influential factors in whether a taken pitch is called a strike or a ball is the count of the at-bat. We have all seen pitches in a 3-0 count substantially off of the plate called a strike, just as we have seen 0-2 pitches over the plate ruled balls. Table Five shows the correct percentage of strikes and balls by pitch count. While this shows that umpires are overwhelmingly more accurate at identifying strikes as strikes in a 3-0 count (91.06%) as compared to an 0-2 count (56.66%), we must acknowledge this only paints part of the picture. Umpires are conversely most likely to correctly labels balls in 0-2 (98.73%) counts and misidentify balls in 3-0 (90.32%) counts. I included their accuracy of correctly identifying both strikes and balls here as opposed to throughout the entire paper because we can clearly tell through this information that umpires are giving hitters the benefit of the doubt over pitchers. Umpires are far more likely overall to correctly identify a ball than a strike, as evidenced by the fact that there are no counts during which umpires correctly call less than 90% of balls.

The data in Table Five is corroborated by the visualizations in Figure One and Figure Two. These visualizations of the strike zone include pitches off of the plate and we can see that in a 3-0 count, a more substantial portion of the rule-book strike zone is called strikes while also incorrectly identifying balls as strikes. While in a 0-2 count, a smaller shaded area of the rule-book strike zone works with our findings that less strikes are identified correctly but more balls are correctly called.

Called Strike Accuracy by Pitch Type

The next area I looked at was whether pitch type significantly altered the accuracy of umpires. In order to do this, I grouped all variations of fastballs into “Fastball” and all other pitches into “Offspeed”, while omitting pitch outs and intentional balls. I was able to see how umpires fared in correctly identifying strikes by pitch type in Table Six.

Not surprisingly, we see Bill Miller near the top of the list with both Offspeed and Fastball accuracy. For umpires as a whole, the difference in accuracy between the two is not large (79.05% Offspeed accuracy vs. 78.91% Fastball strike accuracy). On the other hand, what may come as a surprise is the fact that eight of the top ten highest accuracies were for Offspeed pitches.

Called Strike Accuracy for Home and Away

One of the most-mentioned tendencies of referees or umpires in any sport is home-team favoritism. Whether a foul or no-foul call in basketball, in or out-of-bounds call in football, or a strike or ball ruling in baseball, many think that the home team receives more of an advantage than their visiting counterparts. Looking at top and bottom half of innings, away and home team respectively, we can identify trends and favoritism in major-league umpire strike zones.

While a difference of .62% accuracy may seem like a lot, especially in a sample size of over 650,000 total pitches, we can look at this on a game-by-game level to see the actual discrepancies. For simplicity’s sake, we can assume 162 games a season, making for roughly 11780 games played in our data set (this subtracts all games from the unavailable 2016 data). This leaves us with 23.03 Correctly Called Strikes out of 29.05 Actual Strikes for away teams per game, meaning that 6.02 strikes were not called. As for home teams, we have 22.04 Correctly Called Strikes a game with 28.02 as the Actual Strikes, averaging 5.98 missed strikes a game. By this measurement we can see that more hitter leniency was given to the away team than the home team.

During this time frame, while a higher percentage of strikes were judged correctly, hitters were given more leniency as the away team than the home team on a game-by-game basis.

Called Strike Likeliness in Specific Game Situation

Included in Table Eight are the three most and least likely umpires to call any non-fastball a strike below the vertical midpoint of our zone. I split the strike zone at 2.55 vertical feet and looked at any pitch (not necessarily within the zone) below that height. Here, we are not judging an umpire’s accuracy of correctly identifying pitches, but rather looking at where a certain umpire may call specific pitches. We can see that Doug Eddings is 5.34% more likely to call a strike on a non-fastball as compared to Carlos Torres.

While this does not paint the entire picture, we are able to see how their tendencies can play an important role in the game. Information like this may be valuable to a team in deciding how to pitch a specific batter, which reliever to bring into a game, or factor into being more patient or aggressive while at the plate.

Conclusion

External pressures and increased standards are undoubtable effects on umpire strike zones. As evidenced throughout this paper, strike zones are called smaller than the rule-book strike zone specifies. And while umpires are trending toward correctly identifying strikes, situations such as count and pitch type can affect their judgment.

While the system in place is not 100%, we must understand that these umpires are judging the fastest and most visually-deceptive pitches in the world and are the best at what they do. Major League Baseball must use modern technology to their advantage and provide the best training for umpires to achieve the goal of calling the rule-book strike zone. Another option, while more drastic and difficult to implement, may include adapting the definition of the rule-book strike zone, something that has not been changed since 1996.

The Phillies are a remarkable 9-9 in what’s now 18 games in to the 2017 season.  Why is that remarkable?  Because of what they’re doing on both sides of the ball in the late innings.

The team, as a whole, has an ERA in the ninth inning of 7.36.  Batters they’re facing in the ninth are OPS’ing a ridiculous .910 in the inning and the team has given up six home runs, nearly one in every ten plate appearances.  By almost every metric, the ninth inning has been the worst for Phillies pitching.  Only two teams have a worse ERA in the ninth and only three have a higher OPS.  Of the two teams with a higher ERA, the Rangers gave up half of their ninth inning runs in two games, and no teams have given up as many home runs.

With those kinds of ninth-inning numbers you’d expect that the Phillies would have a high amount of losses attributed to blown saves.  Of their 18 games so far, eight have come down to save situations, and they’ve given up runs in six of those games and blown four of them.  What’s remarkable, though, is that they’ve only lost two of those games, and both games were where the Phillies didn’t get a chance to bat following the blown save.  In the other two they’ve managed to come back and win.

So, how is it that they’ve managed to be at .500 over the first 18 games to start the season?  Well, there’s some interesting anomalies in the late innings on the offensive side as well.

To counter the poor pitching in the 9th inning, the Phillies batters are excelling at hitting in the late innings.  Through innings 7-9 as a team the Phillies are hitting .267/.336/.497 for an OPS of .834, which puts them at the best in baseball.  To put that in perspective, that’s extremely close to what Edwin Encarnacion hit all of last year, .263/.357/.529 for an .886 OPS.  Encarnacion finished 14^th in MVP voting.

What can be interpreted from this is that the Phillies are doing something all good teams seem to do – take advantage of relief pitching.  Indeed, their line against relievers so far this year is incredible at .282/.355/.531 for an OPS of .878, which is again the best in baseball.  Mets slugger Yoenis Cespedes earned himself a contract with an annual average value of $27.5M over the next four years by hitting similarly to what the Phillies are doing to relief pitchers early on this year.  He had a slash line of .280/.354/.530 for an OPS of .884.

For a team like the Phillies have been so far, every run seems to matter.  This isn’t a situation where they’re scoring extra runs or giving up meaningless runs in blowouts.  Over their 18 games they’re carrying a run differential of +7 runs.  To drive home how important every run is in a typical Phillies game, 13 of their 18 games have been decided by less than two runs, and nine have been one-run games.

So to say that the late innings for the Phillies have been adventurous is a bit of an understatement.  They’re giving up runs, but they’re scoring runs as well.  Surely this is a somewhat unsustainable balancing act, but due to the fact that it’s happening on both sides of the ball, when it does in fact even itself out, the end results aren’t likely to be much different.

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the teams with the biggest single-season difference in the WAR and Win Shares for the “Original” vs. “Actual” rosters for every Major League organization. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

AWAR – Wins Above Replacement for players on “actual” teams

AWS – Win Shares for players on “actual” teams

APW% – Pythagorean Won-Loss record for the “actual” teams

Assessment

The 2003 Cleveland Indians 

OWAR: 41.6     OWS: 262     OPW%: .500     (81-81)

AWAR: 26.7      AWS: 204     APW%: .420     (68-94)

WARdiff: 14.9                        WSdiff: 58  

The “Original” 2003 Indians came within one game of the American League Central Division title as the White Sox held off the Tribe and the Twins. Jim Thome launched a League-leading 47 moon-shots and drove in a career-best 131 baserunners. He scored 111 runs, drew 111 bases on balls and earned his highest finish in the MVP balloting (fourth). Manny Ramirez scorched the opposition with a .325 BA, 37 wallops, 104 ribbies, 117 runs scored and a League-best OBP of .427. Richie Sexson (.272/45/124) matched his career-best in home runs and fell one short of his top RBI mark. Brian S. Giles suffered a drop in production from his previous four campaigns but still managed to belt 20 long balls while posting a .299 BA.  “The Mayor” Sean Casey hit at a .291 clip but otherwise failed to deliver the power output expected from a first baseman. The lineup for the “Actual” 2003 Indians featured Milton Bradley (.321/10/56) and rookie outfielder Jody Gerut (.279/22/75).

Omar Vizquel (61^st-SS) and Ellis Burks (77^th-CF) placed in the top 100 player rankings according to “The New Bill James Historical Baseball Abstract among members of the “Actuals” roster.

  Original 2003 Indians                               Actual 2003 Indians

Bartolo Colon (15-13, 3.87) fashioned a WHIP of 1.198 and topped the American League with 9 complete games. Six-time All-Star lefthander C.C. Sabathia (13-9, 3.60) appeared in his first Mid-Summer Classic. David Riske notched 8 saves and a 0.964 WHIP along with a personal-best 2.29 ERA. Danys Baez (3.81, 25 SV) and Julian Tavarez (3.60, 11 SV) bolstered the relief corps.

  Original 2003 Indians                            Actual 2003 Indians  

Notable Transactions

Jim Thome 

October 28, 2002: Granted Free Agency.

December 6, 2002: Signed as a Free Agent with the Philadelphia Phillies. 

Manny Ramirez

October 27, 2000: Granted Free Agency.

December 19, 2000: Signed as a Free Agent with the Boston Red Sox.

Richie Sexson

July 28, 2000: Traded by the Cleveland Indians with a player to be named later, Kane Davis and Paul Rigdon to the Milwaukee Brewers for Jason Bere, Bob Wickman and Steve Woodard. The Cleveland Indians sent Marco Scutaro (August 30, 2000) to the Milwaukee Brewers to complete the trade. 

Brian S. Giles

November 18, 1998: Traded by the Cleveland Indians to the Pittsburgh Pirates for Ricardo Rincon.

Bartolo Colon 

June 27, 2002: Traded by the Cleveland Indians with Tim Drew to the Montreal Expos for Cliff Lee, Brandon Phillips, Grady Sizemore and Lee Stevens. 

Sean Casey 

March 30, 1998: Traded by the Cleveland Indians to the Cincinnati Reds for Dave Burba.

Honorable Mention

The 1941 Cleveland Indians 

OWAR: 43.0     OWS: 267     OPW%: .545     (84-70)

AWAR: 34.9      AWS: 225     APW%: .487     (75-79)

WARdiff: 8.1                        WSdiff: 42  

Engaged in heated combat with the Red Sox and Yankees down the stretch in ’41, the Tribe emerged in third place, four games behind Boston. Thornton Lee (22-11, 2.37) topped the Junior Circuit in ERA, WHIP (1.165) and complete games (30) to merit his lone All-Star invitation. Bob Feller (25-13, 3.15) led the League in victories, starts (40), shutouts (6) and innings pitched (343). “Rapid Robert” paced the AL in strikeouts for the fourth consecutive season and placed third in the MVP voting. Jeff Heath (.340/24/123) established career-highs in base hits (199), triples (20), RBI and stolen bases (18) while making his first All-Star appearance. “Old Reliable” Tommy Henrich clubbed a career-best 31 round-trippers and registered 106 tallies. Ken Keltner rapped 31 doubles, 13 triples and 23 circuit clouts. “Old Shufflefoot” Lou Boudreau socked 45 two-baggers and scored 95 runs.

On Deck

What Might Have Been – The “Original” 2010 Orioles

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “www.retrosheet.org”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

On Wednesday, November 30, 2016 the Twins announced the signing of free-agent catcher, Jason Castro to a 3-year, $24.5MM contract, a move that was widely attributed to the Twins’ new front-office comfort with advanced analytics. Jason Castro is widely regarded as very good defensive catcher, due in large part to his ability to frame pitches and steal strikes for his pitchers. In 2016, Castro ranked third in all of baseball in Baseball Prospectus’ Framing Runs statistic, with 16.3. Kurt Suzuki, the Twins primary catcher in 2016, ranked 92^nd at -6.8. Suzuki’s main backup, Juan Centeno, ranked 97^th with -9.7.

Castro is a roughly average offensive catcher. He put together a 88 wRC+ in 2016, which ranked 17^th among catchers with at least 250 PAs, via FanGraphs. For reference, the league-average wRC+ for catchers in 2016 was 87. But, he got a $24.5MM contract primarily because of his framing and the Twins are expecting him to make an impact on their pitching staff.

So where might the Twins pitchers benefit from better framing? Let’s look at the Twins pitchers (that are still with the organization in 2017) that threw at least 50 innings in 2016, sorted by innings pitched:

Using this list of pitchers, we can utilize FanGraphs’ excellent heatmaps tool to explore each pitcher’s distribution of pitches around the strike zone. For example, here is Kyle Gibson’s 2016 pitch% heatmap, which displays the percentage of pitches thrown to each particular segment in and around the strike zone (from the pitcher’s perspective). The rulebook-defined strike zone is outlined in black.

There are not many surprises here, as we can see Gibson most often pitches down in the zone, and to his arm side, which is likely driven in large part to the high number of 2-seam sinking fastballs he throws (27.2% of total pitches in 2016, per PITCHf/x data available on FanGraphs).

What this data also lets us do, is explore each pitcher’s propensity for pitching to the edges of the strike zone. Let’s assume much of the benefit of pitch framing occurs at the edges of the strike zone, where pitches are less definitively a ball or a strike to the eyes of the umpire. By focusing on the edges of the zone we can identify which Twins pitchers might benefit most from better framing.

For this analysis, I focused explicitly on the strike-zone segments just inside and just outside the rulebook strike zone, which are the areas between the gold lines in the graphic below:

Using the pitch data in these sections, I calculated a metric for each Twins pitcher, Total Edge%. These data points are summarized in the table below and show us the percentage of pitches thrown on the edge, or just off the edge of the strike zone, by each Twins pitcher:

What we can see is the Twins’ starting pitchers seemed to pitch toward the edges of the strike zone more than the league average and more than their reliever teammates in 2016, with the exception of Brandon Kintzler. Ervin Santana is approximately at league average, which was 44.7%. Kyle Gibson is significantly above, at almost 49%. Jose Berrios, Phil Hughes, and Hector Santiago are all up around 47%.  So, as a starting point, we can assert that Gibson, Berrios, Hughes, and Santiago are the primary candidates to benefit from better framing.

But how do they fare in getting called strikes around the edges of the zone?

Using the same heatmaps tool, we are also able to visualize each pitcher’s called strike percentage (cStrike%), in each segment of the strike zone. Here is Gibson’s for 2016:

As we would expect, pitches located in the middle of the zone are nearly always called a strike, evidenced by the bright red boxes and rates at or near 100%.

Our interest is just on and just off the edge of the strike zone, which I again outline in gold. Here, we see more variation, with the called strike percentage ranging from as high as 88% in the zone to Gibson’s arm side, to as low as 27% inside the zone up and to his glove side. We also see, pitches just off the strike zone are called strikes at a much lower percentage than pitches just in the zone, as you would expect. We need a reference point. How do the Twins compare against the rest of baseball?

Using this data, I calculated two additional metrics, In-Zone Edge cStrike% and Out-Zone Edge cStrike%, which delineate the called strike percentage on the edge and in the zone, and on the edge and out of the zone. Focusing on these strike zone segments, I calculated the called strike percentage for each Twins pitcher. Also included are the MLB averages for each metric.

What we see above is that six of the 10 Twins pitchers to throw 50 innings last season had a lower than league-average called strike rate on pitches on the edge and inside the legal strike zone. Ryan Pressly and Jose Berrios appear to be the most impacted, with called strike rates of significantly less than the league average of 64.9%, at 52.8% and 57.5% respectively.

But what about just off the edge?

When we focus on the segments just off the strike zone, we see this same trend play out, but even more significantly. The visual above shows that eight of the 10 Twins hurlers had lower than league-average called strike rates on pitches just off the strike zone. This indicates that they were not getting many strikes stolen in their favor. In most cases for the Twins, the difference from league average is quite significant. Berrios, Michael Tonkin, Pressly, Taylor Rogers, and Santiago each have rates right around half the league average of 10.4%. The net result, when we add up the In-Zone and Out-Zone Edge cStrike% for Total Edge cStrike%, is that seven of the 10 Twins pitchers studied had called strikes rates around the edges of the strike zone that were decidedly less than league average.

Now, this probably isn’t all that surprising intuitively. We know the Twins as a whole did not pitch well last year (29^th in ERA, 27^th in FIP, per FanGraphs), and we know the Twins catchers did not rate well as pitch-framers. Kurt Suzuki and Juan Centeno combined to catch nearly 86% of the Twins’ defensive innings last season. But for as bad as the team pitched, it is also clear the pitchers were not getting much help from their catchers.

But how many pitches are we talking about here? If we assume a league average called strike rate on the edges of the strike zone (which was 36.1% in 2016) for the Twins, we can estimate an additional number of pitches that would be called strikes. This is what we find:

By this analysis, it seems that Jose Berrios, Ryan Pressly, and Ervin Santana would benefit the most from better pitch-framing, with each gaining roughly 20 additional called strikes over the course of the season.

But how much does a pitch being called a ball, instead of a strike, matter?

Let’s look at the major-league batting average by count in a plate appearance. The data in the table below is from a 2014 Grantland article written by Joe Lemire, and calculates the batting average for plate appearances ending on specific counts. For example, the batting average on plate appearances ending on the 0-1 pitch is .321. The data fluctuates slightly year to year, but in any given season, you’ll find a table that generally looks like this:

By this measure, the value of a strike, depending on the count is quite significant. In a 1-1 count, for example, if the next pitch is called a strike, making the count 1-2, the batter’s expected batting average drops from .319 to .164. Similarly, if the pitch is a ball, making the count 2-1, the batter’s expected average increases to .327. That’s a .163 swing in expected batting average.

Others have approached this differently by trying to calculating the expected outcomes by the result of the at bat that reaches each count. So for example, what is the expected outcome for all plate appearances that reached an 0-1 count, regardless of whether it was the 0-1 pitch that the outcome of the plate appearance was created. Nonetheless, we find a similar result. This is a revisit of the idea by Matt Hartzell published on RO Baseball in 2016:

While the differences here are not quite as steep as before, we still see the swings matter. Batting average after a 1-2 count is .178, where after a 2-1 count it is .247. That’s still a .069 swing in batting average. We also have added on-base percentage, and the trend holds. OBP after a 2-1 count in 2016 was .383, versus just .229 after a 1-2 count.

So, all of this helps us show the Twins have a pitch-framing problem and pitch-framing matters because getting more pitches called strikes leads to fewer runners on base.

But can Jason Castro fix it?

To try to find out, let’s look at the Houston Astros, Castro’s former employer. Using the same methodology as with the Twins pitchers, I again calculated the cStrike% on the edges of the strike zone for the all Astros pitchers that threw more than 50 IP in 2016.  What we find is pretty telling:

Of the 12 Astros to throw more than 50 IP, only one, Michael Feliz, had a lower than league-average called strike rate on the edge. But even he was roughly league average at 36.06%, compared to league average of 36.11%. The rest of the pitchers studied were above league average, and in most cases, quite comfortably so. Six of them are clustered close together right around 41.0%.

Now, to be fair, not all of this is directly attributable to Castro. These are different, and arguably, better pitchers. And Castro didn’t catch every pitch thrown (he caught 61.9% of the Astros’ defensive innings in 2016). But, the difference is stark and by this rough measure, it seems Jason Castro will make a positive impact for the Twins pitchers.

To the Twins’ credit, they recognized they had a weakness, and they used the free-agent market to acquire a player they hope can help address it.

Last week, the Cardinals announced that Kolten Wong would be part of a platoon with Jedd Gyorko. As Mark Saxon noted, Wong did not react well to the news (although he later clarified that he would prefer to stay in St. Louis). Kolten and the Redbirds agreed to a five-year, $25.5-million contract last spring, but what might have served as a confidence booster for the young second baseman resulted in a slash line of .240/.327/.355 over 121 games.

The reason for this platoon is Gyorko’s bat. He did lead the Cardinals with 30 home runs in 2016, a number that had him tied for 11th in the National League. But is Gyorko that much better offensively to offset Wong’s defensive attributes?

Let’s look at this from two different perspectives: in the field and at the plate.

In the Field

Using data from 2015 and 2016, Wong and Gyorko played over 1000 innings at second base — a large enough sample size to use in an analysis. Examining the Ultimate Zone Rating per 150 games (UZR/150), we see that Kolten and Jedd have scores of 3.1 and 4.2, respectively. So, while Gyorko seems to have an advantage here, over the course of a 162-game season, this is a relatively insignificant difference.

Looking at Def, which measures the number of runs above or below average a player is worth, we see that Wong scores 8.2, while Gyorko scores 5.3. According to FanGraphs Rules of Thumb for interpreting this statistic, both players are between “above average” and “great defenders.” Wong’s advantage here equates to about 1/3 of a win. Again, no significant difference in their fielding abilities.

If we look at the Inside Edge Fielding statistics from FanGraphs, we see, as a whole, that Kolten makes more difficult plays, but Jedd makes the easier play a greater percentage of the time. For instance, look at the percentage of “unlikely” plays that each player made. An “unlikely” play is a play that is made 10-40% of the time. Kolten made 27% of these plays, while Jedd did not make a single one. At the same time, looking at plays that are “likely,” Kolten made 73% of them, while Jedd made significantly more (88%).

An analysis of these statistics shows us that, in the field, Kolten may make more web gems, but Jedd is the more consistent everyday second baseman. Nevertheless, there is not much separating these two on the defensive end.

At the Plate

At first, this part of the debate seems relatively simple. Gyorko led the team in HRs last year, he is clearly the much better hitter, right? Let’s take a look. At first glance, the players look very similar, with Jedd posting a line of .245/.301/.445 and Kolten producing a line of .254/.323/.375.

One key aspect of a platoon is starting the right-handed hitter against southpaws and vice versa. So let’s look at the zone profiles.

Kolten vs. Righties

Jedd vs. Lefties

Kolten is, far and away, the better hitter in this platoon in terms of average. But Gyorko’s greatest success was his power, right? So let’s look at the slugging zones.

Kolten slugging vs. Righties

Jedd slugging vs. Lefties

Although there are places where Jedd has the higher slugging percentage, Kolten has slightly lower, but similar zone ratings over a longer period of time. Even with advanced statistics, these two players are very difficult to separate.

By the eye test, Kolten seems to have the advantage in the field, but the statistics tell us that these two players are actually very similar. In addition, Jedd seems like the better hitter, but the statistics tell us that, again, they are very similar. Perhaps there is one thing that we can glean from this analysis: Kolten should be put in a place where he can reach base in front of players who drive the ball and Jedd should be placed where he can drive runners in.

To respond to the question asked at the beginning, should this platoon continue? The statistics tell us yes. As a younger player who just signed a large extension, Kolten has more upside. However, if we are to make a decision for this year, not the future, the numbers tell us that the platoon should continue because neither player has separated himself from the other.

Runs Created was one the first sabermetric statistics I took it upon myself to learn about.  After all, it was one of the first statistics developed by Bill James himself.  I am also pretty sure RC is the formula written on a whiteboard in Moneyball (the most influential Brad Pitt movie I have ever seen).  Anyways, Runs Created is not discussed much because there are other, more sophisticated alternatives – wRC, wRC+, etc.  I still appreciate RC because of its simplicity, and it is can still be used as an effective tool for measuring the efficiency of offensive production.

That is precisely what I set out to do.  The question I sought to answer with this study is, “which teams were the most efficient in scoring runs?”  A pretty basic question — which I decided to complicate.  Using team statistics from last year, I calculated the Runs Created for each team’s offense.  The largest separation between Runs Created and actual runs scored came from the San Diego Padres, who scored 686 times, despite “creating” only 621.38 runs.

While ranking in 19th in total runs, the Padres were actually incredibly efficient. I discovered this after trying to develop a way to measure offensive efficiency.  To do so, I created the Runs Conversion Rate (RCR).  While relatively rudimentary, this ratio between runs scored and Runs Created provides, in my mind, a good measurement for the efficiency of offenses.

Run Conversion Rate = Runs Scored / Runs Created

The purpose of this, again, is to gauge the overall efficiency of offenses.  All I really did was give a fancy name to the margin of error of Runs Created.  However, what I sought to do was use this statistic in a different way — to examine which teams made the most of what they produced (efficiency), and which did not.  Think of this article as a new way of looking at an old statistic, not me trying “discover” a new stat.  Below is a table, sorted by runs scored (i.e. from most productive offenses to least productive).  Green values represent teams in the top 10 of a category, and red the bottom 10.

After looking at the table, I noted a few observations to be made: teams ranked top 10 in scoring and top 10 RCR last year were, for the most part, the best teams in the league, the two highest-scoring teams did not score as many runs as they could have, and some teams capped out their production, albeit not a high level of scoring.

First, let’s look at the teams who ranked top 10 in scoring and top 10 in RCR in 2016: the World Champion Chicago Cubs, the American League Champion Cleveland Indians, the Seattle Mariners (second in AL West), the Texas Rangers (AL West Champs), the Washington Nationals (NL East Champs), and the Toronto Blue Jays (AL Wild Card).  All these teams were both productive and efficient.  Both are key indicators of good ball clubs.  They created an equal balance of the two, and, outside of the Mariners, played postseason baseball.

While the last paragraph was basically a no-brainer, this is where the study got interesting.  The Boston Red Sox scored 878 runs last year — short of their roughly 905 “created” runs.  According to their RCR, they were only 97% efficient.  So, what does this mean? The Red Sox, while more productive than anyone else, did not hit their ceiling.  They came close (RCR of 0.970), but still only ranked in the middle third of offensive efficiency.  What if the post-Ortiz Red Sox put up around the same numbers they did last year, but became more efficient in doing so?  In my opinion, the AL East should be scared.  Other teams falling into the top 10 scoring, middle 10 RCR category are the Colorado Rockies, St. Louis Cardinals, and Arizona Diamondbacks.  The Rockies certainly receive a boost in production because they played 81 games in Coors Field.  The Cardinals and Diamondbacks, like the Red Sox, scored often, but not as often as they could have.  So maybe their problem is not a low ceiling, but rather getting away from their floor troubles them.

Our third group of relatively important teams in this study are those who ranked in the middle 10 in scoring and top 10 in RCR: the Pittsburgh Pirates, Los Angeles Dodgers, the Los Angeles Angels, and the San Diego Padres.  Essentially, these offenses were middle of the road in terms of productivity, but scored as many runs as possible given their level of production.  The Angels, ranked in the bottom 10 in Runs Created by their offense in 2016, but were second in RCR, scoring 2.4% more runs than they “created.”  The only team ahead them were the lowly San Diego Padres, who turned in 10.4% more runs.  The Dodgers, who won 91 games in a comparatively weak NL West division, were middle-of-the-road in terms of offensive production, and came in third in terms of RCR.  These teams were ruthlessly efficient, milking the most out of what their offense provided.

I do not know what qualities are common in high-RCR teams.  Maybe a high average with runners in position, a low number of runners left on base, or maybe just plain luck.  That could be the topic of an entirely different study, perhaps.

To sum things up, a high RCR was a common denominator in the teams who saw great success in 2016, and I would like to think it is useful in measuring the efficiency of teams’ offenses.  It will be exciting to see who will rise in 2017 as the most potent offense.  For me, it will be just as exciting to see who is the most efficient.

FanGraphs and Baseball-Reference.com were instrumental in the production of this article.  Theodore Hooper is an undergraduate student at the University of Tennessee in Knoxville.  He can be found on LinkedIn at https://www.linkedin.com/in/theodore-hooper/ or on Twitter at @_superhooper_

Pitch counts have become an integral part of the game of baseball, so much so that it’s impossible to find a TV telecast that doesn’t display the pitch count side-by-side with the score and the inning. Yet pitch counts continue to be maybe the most annoyingly simple and arbitrary metric used to craft crucial in-game strategy. 99 mph fastball down the middle: +1 pitch. 76 mph curveball in the dirt: +1 pitch. Intentional ball: +1 pitch. Dirty ball tossed to the umpire: +0 pitches. Pitchout +1 pitch. Warmup pitches: +0 pitches. My goal here is not to fix this problem — just explore some interesting data that I believe should eventually be used to bring pitch count into the modern era.

Right now, I’m just going to look at 4-seam fastballs and how hard they’re thrown. All data comes from the 2016 regular season. Thank you Baseball Savant. The question I set out to answer is simple: When a pitcher needs to make a pitch, does he try harder? Common sense says yes, of course this is what happens. Relievers throw harder than starters in general because they don’t have to worry about throwing more quality pitches in later innings. But the data shows that pitchers change their effort levels within innings as well, especially when they have two strikes and/or runners in scoring position. Eventually, we should be able to use this knowledge to craft a better pitch count that takes this extra effort into account. Read the rest of this entry »

Spending money is an interesting avenue to build a pitching staff. Many of the deals are conventional; a superstar pitcher around 30 years old gets a contract in the neighborhood of at least 7/$175M. But something unconventional is the nature of the contract that Zack Greinke signed with Arizona; 6/$206M. We have seen pitching contracts at or exceeding $175M several times in recent years; they have all been at least seven years in length. Never before has a contract in Major League Baseball history paid so much money in so little time. In fact, Greinke’s $34.5M take-home in 2016 was the highest single-season pay in Major League Baseball history. Now, with stricter luxury taxes in place, the higher average annual value (AAV) is certainly a unique burden on Arizona, but what about the burden of the seventh, eighth, or even ninth year of a deal for every other team? Arizona’s braintrust decided that, rather than having Greinke hamstring their payroll for seven or eight years, he will only do so for six, albeit at a slightly higher rate. I think they are onto something.

Here’s a look at every major pitching contract signed from the 2000-2011 seasons worth at least five years. Compare the values produced in the first four years of those deals to the value of the whole contract, and look at the following years as well.

Pitching Contracts and Subsequent Performance in $, 2000-2011 Seasons

*all contract data via Baseball Reference, all valuations via FanGraphs by conversion of (fWar)($/fWAR)

** these seasons will be played out in 2017

Of course, there are some contracts in here that went south from the start. Mike Hampton, Barry Zito, and John Danks are the culprits here. You probably notice that in most cases, years one through four go completely according to plan! Some of the exceptions are due to injury, and those are Johan Santana and John Lackey. But even other injury victims, such as Yu Darvish and Chris Carpenter, were so valuable in two or three years that they held up their end of the bargain.

However, the second thing you’ll notice is how quickly values go down on this list after year four. Of the 17 samples we have here, there are only seven success stories in year five (Oswalt, Carpenter, Burnett, Sabathia, Lackey, Gonzalez, and Darvish). Two of those cases are unique, as A.J. Burnett experienced a career revitalization in Pittsburgh under Ray Searage, and Darvish was a young international free agent. Overall, the success rate isn’t encouraging. The real black marks are the years following that. We have 11 samples on hand, and aside from modest renaissances from Mike Mussina and C.C. Sabathia, you get some really ugly numbers.

With this chart now in context, it brings us to wonder why any pitcher is even offered a deal in excess of four years. It is just not worth having so much dead payroll for one to five years. In fact, looking at how successful the first four years are, the values already come pretty close to the original contract anyways. Did the Phillies or Cliff Lee ever consider a four-year contract in that same $120M range? Probably not, but Lee would have taken it, and the Phillies would have been better off. I’m sure C.C. Sabathia never received a 4/$120M offer from the Yankees, but it would have let him hit the market again to potentially cash in one more time, and New York would have still recouped 75% of the value they ultimately got from him in nine years.

Thinking in present times, here’s a chart in a similar vein, but examining pitching contracts in the length of at least more than four years signed from just the 2012 season alone. Remember, these players have pitched the first four years of these contracts…

Pitching Contracts and Subsequent Performance in $, 2012-Present Seasons

*all contract data via Baseball Reference, all valuations via FanGraphs by conversion of (fWar)($/fWAR)

…and we see more of the same. Year five for these eight pitchers is 2017, and how many are a good bet to produce? Verlander, Hamels, most likely Hernandez, and…possibly Wainwright? Matt Harrison’s career is already over due to injury. Lengthy DL stints have ruined Ryu and Cain. Wainwright and Hernandez have also dealt with injury woes. Anibal Sanchez hasn’t been an effective pitcher since 2014. And yet, years one through four look beautiful for everybody but our two outliers.  

The same unorthodox contracts could apply to these guys. Anibal Sanchez is on the 2017 payroll for $16.8M, but what if Detroit had signed him for 4/$80M? Equal value would have been produced, and he wouldn’t be an albatross in 2017. 4/$100M for Adam Wainwright? That is similar to our previous Cliff Lee scenario. If Seattle had offered King Felix 4/$120M, he would have taken it in the hopes of cashing in one more time, and the Mariners would have received good value, similar to the Yankees and Sabathia.

Let’s condense all of the data from both charts and see what averages we get.

Examining the averages, what if the average pitching contract shifted from nearly 6/$96.68M to 4/$73.14? The players would lose $23.54M on average over the length of the contract, but gain close to two free-agency years. Presumably, two free-agent years would be worth more than that, making for a worthy trade-off. As for the teams, they would pay $1.27M more per year in AAV, but eliminate two years of dead payroll (for those of you calculating that at home, that’s [17.02×1.68] – [1.27×4] for an average gain of $23.51M). That is a worthy trade-off for them as well. In other words, teams save millions, and players make more millions.

These condensed contracts have virtually no true precedent, but the 6/$206M deal that Greinke signed is closer to them than the current industry standard. Of course, pitching deals signed around the same time as Greinke are completely in tradition with this century (Max Scherzer, Jordan Zimmermann, David Price, Jeff Samardzija, Johnny Cueto, Mike Leake, Wei-Yin Chen, Ian Kennedy, and Stephen Strasburg), but that makes this one contract so potentially revolutionary among its contemporaries.

If you are thinking of the player and team who may follow these footsteps, I would imagine the perfect test case to be Matt Harvey. The Mets pitcher proved that he is an All-Star-level hurler in his comeback 2015 season from Tommy John surgery, but was hampered again in 2016 and diagnosed with thoracic outlet syndrome. All of this speculation is for naught if Harvey’s career is going to fizzle out or if he will need to be relegated to the bullpen, but let’s say the next two years are a comeback for him in the mold of 2015. After 2018, he would hit free agency going into his age-29 season. He would theoretically be in line for a five- to seven-year deal, but I don’t think someone with “Tommy John surgery” and “thoracic outlet syndrome” on his resume is a wise investment for that long. What if instead of something in the 6/$150M range, it’s a deal for 3/$100M? 4/$130M? If his production is equal to that type of contract, he would still hit free agency at age 33 or 34 and be in demand; it’s quid pro quo.             

Only time will tell if front offices of the future will adopt this strategy, and the harsher luxury-tax penalties surely dampen the idea. However, a team with cash to spend is always a team in need of pitching; perhaps we will see their contracts truly begin to condense.