The issue that plagues the valuation of late-inning relievers is the generalized treatment of runs.

WAR is the most accepted player evaluation metric and wins are determined by run value. Run value is determined in a generalized sense; it’s too perilous and unwieldy to predict, or evaluate performance, based upon the sequencing of events.

However, late-inning relievers do not pitch in a general situation. Unlike many other players we know when they will perform. They are unique; they pitch in particular situations: the late innings of a baseball game.

They are not vulnerable to give up a home run in a large range of innings like a starting pitcher. They are vulnerable to giving up runs in the innings their role demands them to appear in; most notably the 7th, 8th, and 9th innings.

Therefore, reliever value should be measured by a more specific run value. This run value, and ultimately win value, cannot be measured in a general sense. Their valuation must account for the specific times they appear in a game.

I set out to do this with those principles in mind.

First, I used Baseball Reference’s Play Index to determine the amount of runs scored in between the 7th and 9th innings of all games in 2015. There were 13,448 7th, 8th, and 9th innings played last year. That is the equivalent of 1,494 full 9 inning baseball games. In sum, 5,968 runs were scored in the 7th, 8th, and 9th innings of baseball games in 2015. On average, that is 3.99 runs per “game”, where “game” signifies 9 innings of 7th-9th inning performance.

Second, also using Baseball Reference’s Play Index, I looked at the 300 pitchers with the most appearances in the 7th, 8th, and 9th innings. This does not represent every pitcher that pitched in the 7th, 8th, and 9th inning, but it gets us to Trevor Cahill, who pitched 16 innings in the 7th inning or later.

I then split this list of pitchers into two groups. Theoretically, the 90 best relievers in the league would be pitching in the 7th, 8th, and 9th innings (30 teams; 3 relievers each). Therefore, the first group is the first 91 pitchers with the most appearance (Tony Sipp and Blaine Boyer each appeared in 43 innings between the 7th and 9th innings, so there is one more than 90 in this case). The other 209 pitchers represent the “replacement” pool.

The average performance of the “replacement” pool was taken to determine the performance of a replacement player. Here is what that looks like:

This week the strike zone has been getting a lot of attention. If you’ve been paying any attention to baseball (and I’m sure you have since fantasy baseball leagues are starting to open up) there have been a few articles/releases suggesting that MLB may be considering raising the strike zone from the hollow beneath the kneecap to the top of the kneecap. It seems like a good idea since strikeout rates are on the rise, but was this a result of (1) pitchers getting better or (2) hitters getting worse or (3) have strikes been getting called differently? I’ll give you a hint; it’s neither of the first two suggestions, at least not directly. No, instead let’s focus on the strike zone and more specifically two things: (1) visualizing the strike zone from 2008 to 2015 and (2) using a standardized set of pitches look at how those pitches have been called over time.

Let’s go through the methods I used before we get to the plots. I used the pitchRx package in R to gather and store the data and used many of the functions included in the package. Next I went through the data and subset the PITCHf/x data by year since I was interested in looking at annual changes. Now due to a combination of time restraints and lack of computing power I didn’t run all of the pitches thrown in each year so I did some subsetting instead. I downloaded a CSV from the FanGraphs leaderboards of all qualified pitchers from 2008 to 2015. In each year I randomly selected 20 pitchers from the list of qualified starters to represent how the strike zone was called for that given year. Finally I ran the data through a general additive model (seen here) which was used to create the “heat maps” for the probability of called strikes in the plots below. I also tested the probability of five standard pitches being called strikes, but that is addressed a bit more later one so I won’t bore you with the details twice. Added note: if anyone actually wants a copy of the R code leave a comment below and I’ll get in contact with you.

Below I’ve included a GIF of the strike zone from 2008 to 2015 . If you watch it a few times you’ll begin to notice the gradual changes to the bottom of the strike zone, plus when it flips from 2015 to 2008 you can really notice the difference. It’s not surprising that there are inter-annual differences between the zones since I’m sure MLB makes a few minor tweaks every off-season and maybe there is a changing of the guard over time for the umps. I also need to apologize about the 2010 plot, the left (L) and right (R) are reversed and I can’t seem to switch them. We will just have to deal with that one plot being different. In all plots the label “L” refers to left-handed batters and “R” to right handed batters.

Now I wanted to find a way to quantify changes to how pitches were being called and I decided on using a set of standardized pitches. Below is a plot showing the locations I chose for my test pitches. I went with five different locations. The pitch right down the middle was my control of sorts, just to make sure things were getting called consistently over time. The remaining locations were the ones I was really interested about; three of those pitches were all located on the lower edge of the strike zone and the final pitch was located 0.2 feet or 2.4″ (the metric system would be more useful here, just sayin’) below the bottom edge of the strike zone. When I initially began this simulation I expected that the lowest pitch would be a second control pitch that would consistently be called a ball, but the results were pretty surprising. Also, I’d like to include that the strike zone to lefties is slightly shifted so that more outside pitches are called strikes.

OK so we are almost at the exciting conclusion. Using those standardized pitches from the plot above I used the general additive model to predict the probability of that pitch being called a strike in a given year. The results are summarized in the plot below. We can see that the pitch being thrown at coordinates 0, 2.5 (the one down the middle) the probability of being called a strike is basically 100% every year. Well that’s a good thing at least that call is consistent. The low pitch thrown down the middle on the bottom edge of the strike zone, coordinates 0, 1.7 (green line), has increasingly been called strike since 2008 to both right- and left-handed batters. Pitches down and in to righties increased pretty significantly this past season where the probability crept above 50%; to lefties that pitch is down and away and it’s been called pretty consistently since 2011 (red lines). Pitches thrown down and away to righties or down and in to lefties (coordinates 1, 1.7 — purple lines) haven’t changed all that much over the time period.

Now we get to what I think is the most interesting pitch. The low fastball down the middle (coordinates 0, 1.5) the one that should be out of the strike zone. This pitch is represented by the gold/yellow lines on the plots. In 2008 these pitches had a chance of being called a strike ~10% of the time to both righties and lefties. Over the last eight seasons that number has trended upwards and in the 2015 season settles in somewhere around 36-40%, which is not an insignificant proportion.

Based on this data it certainly appears as though MLB is justified into looking at raising the strike zone. Pitchers that live down in the zone have been given an increasing advantage in a relatively short amount of time. Hopefully this sheds some light onto the debate on whether or not to raise the strike zone in the coming seasons or maybe the umps will be able to make some adjustments for the upcoming season.

Being a Rockies fan for most of my life, I’ve had my fair share of discussions about how a ballpark can affect not only the performance of the home team, but also that of the visiting team. At this point, I don’t think anyone has any doubt that Coors Field is a hitter’s park. However, there are a couple of questions regarding this park I’d like to address. First of all, is Coors Field alone in its capacity of enhancing offense, or is it comparable to other parks around the league? And secondly, is this effect stronger among Rockies’ hitters than it is for hitters from other teams?

To answer the first question, let’s compare offensive production at home versus on the road for each team, so we can see where the Rockies stand among the rest of the league in this regard. I selected a time frame from 1995 to 2015, simply because it is the same time frame that Coors Field has been hosting baseball games. For teams that moved to a new park during that time, we’ll consider only the seasons played in the newest stadium. The comparing stat we’ll use is OPS. I chose OPS instead of runs scored (which many park factors out there use) to take sequencing out of the equation. The order in which individual events occur in baseball can depend on things like lineup construction or managerial in-game decisions, but mostly it’s just random chance. I could have chosen a sounder, more sophisticated stat like wOBA, but OPS is more readily available, and a wider array of audiences are familiar with it.

After constructing a table for each team, consisting of year by year home and away OPS, I calculated the percent change of the two means, using the away value as the base. But simply comparing means can be very misleading. Randomness will always create a difference between two means, even if there is no actual effect causing it. In order to have some confidence that the differences we observe are statistically significant, I ran a Student’s t-test to each set of data (i.e. yearly home and away OPS for each team). The threshold of significance was set at 0.10, which means that there would be a 10% chance of seeing these differences if there were no real effect.  Anything above that value was considered not significant.

The following table contains the percent change for every team, along with its p-value. Red values don’t satisfy the significance criterion.

According to these numbers, 19 out of 30 ballparks have a statistically-significant positive effect on the home team’s offense, while 10 of them can be considered “neutral” due to the non-significant nature of the data, and just one (San Diego) has a significant negative effect on the home team’s offense.

At first glance, Coors Field seems to be in a league of its own when it comes to enhancing the home team’s offensive production. A common rule of thumb is that in a normally distributed data set, 99.7% of its values fall within three standard deviations of the mean. Any value outside of that range is considered an outlier. In this case, that range goes from -12.43% to 20.96%. Colorado, with its variation of 27.01%, falls way outside these limits, making it the only outlier of the group. This answers our first question, confirming that there’s no park that increases offense for the home team quite like Coors does. Which takes us to the second question: does it have a similar effect on visiting teams? Let’s crunch some numbers and see what they tell us.

The idea is to repeat the same process we used for answering the first question, only this time we’re going to use opponents OPS or OPS against, instead of the team’s own OPS. Basically, what we’re trying to do is compare how opponents’ offenses as a whole, change when they visit a particular park. In other words, and using Colorado as an example, we want to know how the league’s OPS against the Rockies is affected by playing at Coors Field as opposed to anywhere else.

Using the same methodology, here’s the opponents OPS change by park:

There are a couple of things to digest from of this table. First off, the fact that Colorado has the only park in which visiting hitters significantly increase their offensive production is pretty mind-blowing. It seems to me that we’ve been using the term “hitter’s park” way too lightly. Out of the 30 ballparks actively housing an MLB team, 19 have a statistically-significant negative effect on the visiting team’s offense. Just like in our first analysis, 10 of them can be considered “neutral”, with p-values above 0.10, and just one (of course, Coors Field) has a positive effect with a good degree of significance.

This seems to contradict the numbers showed in our first table. In fact, out of the 19 parks that enhanced offensive performance for the home team, 10 of them also have a negative effect on visiting hitters. How can this apparent contradiction be explained? Well, it probably has a lot to do with the all-encompassing concept that is Home Field Advantage. For whatever combination of reasons (familiarity with the park, sleeping in their own beds, having dinner with their families), playing at home seems to get the best out of most players. If you think of the visiting teams’ OPS as a pitching stat for the home team (which it is), then you can interpret the numbers in the second table as having 19 out of 30 parks with a positive effect on the home-team pitching staff, 10 being neutral, while just one of them having a negative effect. Coincidentally, that’s precisely a mirror image of the results we got when analyzing the first table.

Going back to the second question, does Coors Field have a greater impact on Rockies’ hitters than on the rest of the teams? The short answer is yes. The variation in OPS for Colorado players is 27.01%, while the equivalent for non-Rockies players is “just” 9.00%. So by just comparing these two values, it seems evident that the effect is in fact greater among Rockies’ hitters. The explanation could be again simply Home Field Advantage, but the difference is just too big. If we merge both tables in one, and consider the visiting hitters as a control group, then a simple subtraction should give us a rough estimate of the net effect of Home Field Advantage on home-team hitters.

Here’s that table. Red values were not considered in the subtraction since they were deemed non-significant.

Coors Field sits comfortably at the top, way ahead of Minute Maid, the second park on the list. Applying the same criteria for outliers we used before, Colorado’s Net Effect of 18.01% is not within the range of three standard deviations around the mean (-1.60% , 16.74%), once again being the lone outlier. It doesn’t look like that this is simply a result of Home Field Advantage; it seems there’s something else. This brings up a new question, one for which I’m not sure I have a definite answer: Does Coors Field undermine the Rockies’ ability to have a healthy offense on the road?

Let’s go back for a moment to the 27% increase in OPS for Rockies’ hitters at home. That number could mean a huge spike in offensive production when they play at Coors Field or a massive collapse when they hit the road; it depends on how you see it. Colorado ranks dead last in the majors in OPS away from home in the same time span we’re studying, so either they have been the worse offensive team in two decades (which is certainly an option) or something is causing them to consistently under-perform on the road. Of course, it doesn’t help that almost half of their games away from Denver are played in places like San Diego, Los Angeles, and San Francisco. In fact, according to the numbers in the second table presented in this piece, Colorado’s division rivals have the toughest combination of parks for visiting hitters. The average drop-off in opponents OPS in NL West parks (excluding Coors Field) is -7.15%. The following table shows that value for every team in the majors (for the purpose of this exercise, Houston was considered an NL Central team).

This definitely helps explain, at least partially, the abnormal home/away splits that Rockies’ hitters have had historically. Not only do they play their home games in the biggest, if not the only true hitter’s park in the game, but they also play a big chunk of their road games in three of the toughest pitcher’s parks in MLB.

The last question remains unanswered; the thesis of a Coors Field Hangover effect is largely unproven. Still, there’s a good amount of circumstantial evidence that points to the existence of something like it.

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. Consequently, Giancarlo Stanton is listed on the Marlins roster for the duration of his career while the Mets declare Ken Singleton and the Expos / Nationals claim Tim Raines. 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 finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “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

Assessment

The 1939 New York Yankees          OWAR: 60.8     OWS: 345     OPW%: .607

Based on the revised standings the “Original” 1939 Yankees registered 94 victories and outlasted the Indians to secure the pennant by a 7-game margin. New York paced the American League in OWS and OWAR. GM Ed Barrow acquired all of the ballplayers on the 1939 Yankees roster.

“Joltin’” Joe DiMaggio claimed his first batting title and the 1939 American League MVP Award. “The Yankee Clipper” produced a .381 BA with 30 four-baggers, 126 ribbies and 108 runs scored. Red Rolfe (.329/14/80) topped the leader boards with 213 safeties, 139 aces and 46 two-base knocks. Fellow third-sacker Billy Werber registered 115 runs scored and drilled 35 doubles. Bill Dickey belted 24 round-trippers and tallied 105 RBI along with a .302 BA. George “Twinkletoes” Selkirk (.306/21/101) posted career-bests in homers, runs scored (103) and bases on balls (103). Joe Gordon smashed 28 long balls and drove in 111 baserunners during his sophomore season. Charlie “King Kong” Keller supplied a .334 BA in his inaugural campaign.

Lou Gehrig is listed as the top ballplayer in the All-Time First Baseman rankings according to Bill James in “The New Bill James Historical Baseball Abstract.” Teammates listed in the “NBJHBA” top 100 rankings include DiMaggio (5^th-CF), Dickey (7^th-C), Gordon (16^th-2B), Keller (17^th-LF), Tony Lazzeri (19^th-2B), Dixie Walker (30^th-RF), Rolfe (44^th-3B), Ben Chapman (55^th-CF), Frankie Crosetti (67^th-SS), Lefty Gomez (67^th-P), Werber (78^th-3B) and Lyn Lary (80^th-SS).

Lefty Gomez (12-8, 3.41) earned his seventh All-Star nomination. Atley Donald furnished a 13-3 mark with a 3.71 ERA. Marius Russo contributed an 8-3 record with a 2.41 ERA and a 1.095 WHIP in his freshman year.

The “Original” 1939 New York Yankees roster

Honorable Mention

The “Original” 1932 Yankees            OWAR: 52.6     OWS: 336     OPW%: .588

The Philadelphia Athletics ended the season in a virtual tie with the Bronx Bombers. The A’s edged the Yankees by a few percentage points to take the pennant while New York led the Junior Circuit in OWAR and OWS. Lou Gehrig pummeled opposition hurlers, belting 42 doubles and 34 round-trippers. “The Iron Horse” registered 138 tallies, 208 base knocks and 151 ribbies along with a .349 BA. Lefty O’Doul (.368/21/90) collected his second batting title and topped the 200-hit mark for the third time in four campaigns. Tony “Poosh ‘Em Up” Lazzeri supplied a .300 BA with 15 dingers and 113 RBI. Earle Combs aka “The Kentucky Colonel” scored 143 runs and posted a .321 BA as the Yankees’ primary leadoff hitter. Ben Chapman rapped 41 doubles, swiped a League-leading 38 bases and topped the century mark in runs scored (101) and RBI (107).  Bill Dickey (.310/15/84) and Kiddo Davis (.309/5/57) bolstered the prolific lineup. Lefty Gomez (24-7, 4.21) anchored the starting rotation and finished fifth in the 1932 A.L. MVP balloting in spite of his high ERA and walk totals. Johnny Allen fashioned a 17-4 record with a 3.70 ERA in his rookie year.

On Deck

The “Original” 1906 Cubs

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

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

Last year, many Indians fans anguished over the so-called “SI Cover” curse. The prediction that the Cleveland Indians would win the 2015 World Series, however, was not their downfall. The downfall of the Indians once again was highlighted by mediocre offense, and the unfortunate decline of their biggest free-agent acquisitions in over a decade, Nick Swisher and Michael Bourn. Swisher and Bourn ate up over 1/4 of the Indians’ total payroll and their sedentary production was an absolute killer in their lineup. Things improved offensively with the promotion of Rookie of the Year runner-up and budding star shortstop Francisco Lindor. Lindor produced way above what was expected of him and figures to be a key piece of the puzzle in 2016. Also the trading of both Bourn and Swisher to the Atlanta Braves was the equivalent of a one-thousand pound anchor being lifted from the Tribe’s lineup. Ultimately, the Tribe finished with a respectable, yet disappointing (based upon previous predictions) 81-80 record. However, there are five key things that will be the difference-makers in 2016 and will lead the Tribe to winning their first AL Central title since 2007.

1. Corey Kluber, Carlos Carrasco and Danny Salazar – The top of the Tribe rotation is arguably the best in all of baseball. According to early projections, the trio all look to post ERAs under 3.35, have more than 200 strikeouts each, and have no more than 50 walks each. Respectively, they project to have WARs of 6.0, 5.8, and 3.5. No top of the rotation in all of baseball projects higher. Carrasco and Kluber each should easily contend for an AL Cy Young Award, and Salazar looks to break through in a big way as well.
2. Terry Francona – Since Francona took over the Tribe they haven’t had a losing season. He’s compiled an overall record of 258-229 since 2013 and, of course, had a very storied career in Boston, winning two World Series in his tenure there. Francona is arguably the best manager in baseball, and would love nothing more than to add another World Series title to his name. He’s worked particularly well with the Tribe’s young roster and was reported to be a key reason behind the front office not moving one of their top-of-the-rotation starters for a bat. With a strong rotation and bullpen to work with (the Tribe had the Majors’ 4th-lowest bullpen ERA at 3.12 last season), Francona’s biggest task lies with getting his lineup in a situation to produce as many runs as possible to support their outstanding pitching. Luckily for him they’re most likely not going to have to score that much.
3. The Middle Infielders – Both Francisco Lindor and Jason Kipnis are among the very best in the league at their positions. They hit 1 and 2 in the Tribe lineup, and they also will be quite possibly the biggest factors to helping the Indians win several games in 2016. Both infielders were top 2 in WAR at their position last season (Kipnis 5.2, 1st in MLB at 2B, Lindor 4.6, 2nd in MLB at SS). Both infielders also were 2nd at their position in both BABIP and wOBA (minimum 400 PA). On top of all this, both players are plus defenders, and remind some fans of the dynamic duo of Omar Vizquel and Carlos Baerga that dominated the middle of the field throughout the 90s for the Tribe.
4. A Healthy Yan Gomes – Okay, so Yan Gomes was pretty bad last year…his lack of production no doubt had a big effect on the Indians lineup, and not in a good way. Gomes had a miserable slash line of .231/.267/.391 and hit only 12 homers. In 2014 we all saw a very different Yan Gomes, as he had a respectable slash line of .278/.313/.472 along with 21 homers. He led all AL catchers in WAR (4.5) and Slugging percentage (.472) (minimum 400 PA). With Gomes returning to full health now in 2016 he should return to form and be a big producer in the middle of the Tribe lineup.
5. Michael Brantley – Losing Brantley for the first month of the season is really going to hamper the Tribe, but if he can return to full health, you’d be hard-pressed to find a more productive player in all of baseball. Brantley is the Tribe’s X-factor; over the last two seasons he has hit 35 homers and 90 doubles, he’s had batting averages of .327 and .310 respectively, and he’s had OPS’s of .890 and .859 respectively. Most impressively has been his ability to hit with runners in scoring position — over the last two seasons combined he’s owned a .351/.437/.507 slash line. When healthy, the hope is that he can return to this form once again. Brantley proved resilient last season, putting up big numbers despite dealing with back issues throughout his 2015 campaign.

So there it is, the keys to the Tribe winning a 2016 division title. Obviously on top of all this, several other things need to go right for the Tribe. But these five factors alone will be among the leading reasons why the Indians win their division.

All stats referenced, or used for statistical analysis for this article are courtesy of mlb.com, baseball-reference.com, and fangraphs.com.

What’s the single most important offensive statistic? I imagine most of us who have bookmarked FanGraphs would not say batting average or RBIs. A lot of us would name wOBA or wRC+. But neither of those are the types of things you can calculate in your head. If I go to a game, and a batter goes 1-for-4 with a double and a walk, I know that he batted .250 with a .400 on-base percentage and a .500 slugging percentage. I can do that in my head.

So of the easily calculated numbers — the ones you might see on a TV broadcast, or on your local Jumbotron — what’s the best? I’d guess that if you polled a bunch of knowledgeable fans, on-base percentage would get a plurality of the votes. There’d be some support for OPS too, I imagine, though OPS is on the brink of can’t-do-it-in-your-head. Slugging percentage would be in the mix, too. Batting average would be pretty far down the list.

I think there are two reasons for on-base percentage’s popularity. First, of course, is Moneyball. Michael Lewis demonstrated how there was a market inefficiency in valuing players with good on-base skills in 2002. The second reason is that it makes intuitive sense. You got on base, you mess with the pitcher’s windup and the fielders’ alignment, and good things can happen, scoring-wise.

To check, I looked at every team from 1914 through 2015 — the entire Retrosheet era, encompassing 2,198 team-seasons. I calculated the correlation coefficient between a team’s on-base percentage and its runs per game. And, it turns out, it’s pretty high — 0.890. That means, roughly, that you can explain nearly 80% of a team’s scoring by looking at its on-base percentage. Slugging percentage is close behind, at 0.867. Batting average, unsurprisingly, is worse (0.812), while OPS, also unsurprisingly, is better (0.944).

But that difference doesn’t mean that OBP>SLG is an iron rule. Take 2015, for example. The correlation coefficient between on-base percentage and runs per game for the 30 teams last year was just 0.644, compared to 0.875 for slugging percentage. Slugging won in 2014 too, 0.857-0.797. And 2013, 0.896-0.894. And 2012, and 2011, and 2010, and 2009, and every single year starting in the Moneyball season of 2002. Slugging percentage, not on-base percentage, is on a 14-year run as the best predictor of offense.

And it turns out that the choice of endpoints matter. On-base percentage has a higher correlation coefficient to scoring than slugging percentage for the period 1914-2015. But slugging percentage explains scoring better in the period 1939-2015 and every subsequent span ending in the present. Slugging percentage, not on-base percentage, is most closely linked to run scoring in modern baseball.

Let me show that graphically. I calculated the correlation coefficient between slugging percentage and scoring, minus the correlation coefficient between on-base percentage and scoring. A positive number means that slugging percentage did a better job of explaining scoring, and a negative number means that on-base percentage did better. I looked at three-year periods (to smooth out the data) from 1914 to 2015, so on the graph below, the label 1916 represents the years 1914-1916.

A few obvious observations:

• The Deadball years were extreme outliers. There were dilution-of-talent issues through 1915, when the Federal League operated. World War I shortened the season in 1918 and 1919. And nobody hit home runs back then. The Giants led the majors with 39 home runs in 1917. Three Blue Jays matched or beat that number last year.
• Since World War II, slugging percentage has been, pretty clearly, the more important driver of offense. Beginning with 1946-1948, there have been 68 three-year spans, and in only 19 of them (28%) did on-base percentage do a better job of explaining run scoring than slugging percentage.
• The one notable exception: the years 1995-1997 through 2000-2002, during which on-base percentage ruled. Ol’ Billy Beane, he knew what he was doing. (You probably already knew that.)

This raises two obvious questions. The first one is: Why? The graph isn’t random; there are somewhat distinct periods during which either on-base percentage or slugging percentage is better correlated to scoring. What’s going on in those periods?

To try to answer that question, I ran another set of correlations, comparing the slugging percentage minus on-base percentage correlations to various per-game measures: runs, hits, home runs, doubles, triples, etc. Nothing really correlates all that well. I tossed out the four clear outliers on the left side of the graph (1914-16, 1915-17, 1916-18, 1917-19), and the best correlations I got were still less than 0.40. Here’s runs per game, with a correlation coefficient of -0.35. The negative number means that the more runs scored per game, the more on-base percentage, rather than slugging percentage, correlates to scoring.

That makes intuitive sense, in a way. When there are a lot runs being scored — the 1930s, the Steroid Era — all you need to do is get guys on base, because the batters behind them stand a good chance of driving them in. When runs are harder to come by — Deadball II, or the current game — it’s harder to bring around a runner to score without the longball. Again, this isn’t a really strong relationship, but you can kind of see it.

The second question is, what does this mean? Well, I suppose we shouldn’t look at on-base percentage in a vacuum, because OBP alone isn’t the best descriptor of scoring. A player with good on-base skills but limited power works at the top or bottom of a lineup, but if you want to score runs in today’s game, you need guys who can slug.

Taking that a step further, if Beane exploited a market inefficiency in on-base percentage at the beginning of the century, might there be a market inefficiency in slugging percentage today? It doesn’t seem that way. First, there’s obviously an overlap between slugging percentage and on-base percentage (i.e., hits), and just hitting the ball hard on contact doesn’t fill the bill if you don’t make enough contact. Recall the correlation coefficient between run-scoring and on-base percentage is 0.89 and between runs and slugging is 0.87. The correlation between run-scoring and pure power, as measured by isolated slugging, is just 0.66. That’s considerably lower than batting average (0.81). ISO alone doesn’t drive scoring.

The second reason there probably isn’t a market inefficiency in slugging percentage is that inefficiencies, by definition, assume that the market as a whole is missing something. In the Moneyball example, other clubs didn’t see the value in Scott Hatteberg and his ilk. It’s harder to believe, fifteen years later, with teams employing directors of baseball systems development and posting for quantitative analysts, that all 30 teams are missing the boat on players who slug but don’t contribute a lot otherwise. Or, put another way, there’s a reason Pedro Alvarez and Chris Carter were non-tendered, and it’s not market inefficiency.

Justin Upton’s recent $132.75M/6-year contract with the Tigers does not seem, on the surface, like an outrageous contract. And right now it isn’t; at age 28, Justin should be hitting his prime. Since breaking in with the Diamondbacks, he has been a consistent power threat in a league where consistent power bats are few and far between. To pay $22 million for an outfielder that the Tigers control for two years, potentially six years (Upton has an opt-out clause after two seasons), does not sound extreme when you consider other contracts signed by young, dynamic outfielders; in fact the contract came in below MLB Trade Rumors’ projection of a 7-year/$147 million deal[1]. So why anyone would be concerned about Justin Upton’s deal? Maybe it’s the fact that it took a while for his market to develop this offseason, or maybe it is because he shares the same bloodline as Melvin (formerly known as B.J.) Upton whose production went in the tank after his age-28 season? I get the feeling that Justin could end up as a bad investment for the Tigers. Here’s why.

Exit Speed and Park Factors

Fortunately for Justin, he is getting out of the notorious pitcher’s kingdom that is Petco Park. Unfortunately for Justin, he is moving to another pitcher’s park, Comerica Park. Poor guy can’t catch a break. One concern that I noticed about Upton’s metrics was his exit speed on home runs. According to the ESPN Home Run Tracker, Upton had an average home-run exit speed of 105.2 mph. The concern here lies when you compare the average exit speed versus his prior years. Take a look at the chart below which compares his FB/HR%, HR totals, and average home-run exit speed.

The numbers here do not look all that out of line, other than his 2012 season where his HR/FB% was off from the average. Upton usually sits around the high 20’s in terms of total home runs, being pretty consistent except for the outlier 2012 season. But the home-run exit speeds have decreased each of the last five seasons — some seasons the decrease was more than others, but still they have decreased nonetheless. Another aspect of Upton’s stats to look at is his 2015 home-run landing spots overlaid with an outline of Comerica’s dimensions.

The graphs show the “True” Landing spots according to the ESPN Home Run tracker for the 2015 season. Notice that roughly eight of Upton’s 2015 home runs would not have made it out of Comerica. Only one would have stayed inside Petco, Upton’s 2015 home field. If we used the Comerica park numbers, Upton would have hit 26-8, so 18 home runs. This creates a reason to be concerned, especially since most of Upton’s value is supplied by his ability to drive the ball out of the park, and not his ability to hit for average.

So a value trap you say?

Yes, a value trap. Considering that Upton is 28, paying $22 million a year seems pretty reasonable. In fact, some baseball commentators saw it as a solid investment (and it may turn out to be such). But the caveat is Upton’s opt–out option after two years, similar to the deal Jason Heyward has. If Upton is able to continue to produce nearly 30 home runs a year, he could easily opt out and test the free-agent market again. But if an underlying metric like home-run exit speeds continues to dip and the power numbers take off downhill with it, there is no rational reason for him to opt out and test the market again when he has a $22 million/year deal locked up for four more years.

Therein lies the trap: In an effort to win now by the Tigers, they will either lose Upton after two seasons or they will get trapped by a contract that could eat $22 million of payroll a year, for four years, for a player whose power numbers have dropped and will struggle to provide value in other areas. Is it a great deal for Upton? Of course. Is it good for the Tigers? Short-term, yes. Long-term, there are very few scenarios where they emerge as a winner in the deal. Either they have to pay for Upton again after the 2017 season, or they get stuck with a player who isn’t as good as he once was. Maybe it’s just a hunch but I think the Tigers may be getting the shaft.

[1] www.mlbtraderumors.com/2015/10/justin-upton-mlb-free-agent.html

2015 was the year of Bryce Harper. He led qualified hitters with a 197 wRC+, the highest since the turn of the century among players not named Barry Bonds. This was a vast improvement on his already-impressive 2014 season, in which he totaled a 115 wRC+.

Depending on how you look at things, you could say Bryce Harper was the most improved batter in 2015. I choose not to for two reasons: 1) it’s too easy, and 2) it makes this article more fun. There’s also another more objective reason: with only 395 plate appearances in 2014, Harper didn’t qualify for the batting title.

This poses a question: what minimum do we set to determine who improved the most between 2014 and 2015? If we say that the player needed to qualify for the batting title each year, we get Chris Davis as the most improved batter, who increased his wRC+ from 94 in 2014 to 147 in 2015. If we set no minimum, our wonder-boy is none other than notorious slugger Carlos Torres, the Mets pitcher who upped his wRC+ from -100 to 491.

Clearly, there needs to be some minimum. For the purpose of the article, I’ve decided to set it at 100 PA. This seems a reasonably small enough number to include a wide array of players, but large enough to get rid of anomalies (I’m looking at you Carlos). When we set this minimum, we discover that the batter whose wRC+ increased the most between 2014 and 2015 is… Ryan Raburn. However, since Jeff Sullivan already talked about Raburn, I decided to go with the next name on the list: J.B. Shuck.

If you don’t know who that is, I don’t blame you. I didn’t until I started this research. If you do know him, I’m going to guess that you’re either a White Sox, Indians, or Angels fan. Either that, or you have more time to watch baseball than a college student taking a full course-load of credits. Who’s to say?

The reason the casual fan might not know Shuck is because, well, he’s not exactly a star player. Here are the players with the lowest wRC+ in 2014 of those with at least 100 PAs:

That’s right, he was literally the worst batter that year. Almost as bad as if I were to join the majors. It should be no surprise, then, that he was able to improve so much — he had the lowest starting point. Even so, he still had needed to improve quite drastically in order to surpass Harper’s wRC+ improvement. And that’s exactly what he did:

In 2015, Shuck improved so much that he almost managed to be an average player. But how did he manage to do it? Was it a matter of luck, or did he actually get better?

The number that stands out the most in Shuck’s 2014 season is his .146 BABIP (batting average on balls in play). For those of you that don’t know, that number is quite bad. Like, less than half of what it should be. His BABIP in other seasons is right around league average, so something must have gone amiss last year. Looking at the underlying numbers, some things showed up:

So. His FB% and Pull% numbers were way up as compared to other years. For some context, the league-average FB% has been approximately 34% the past two years, while Pull% has been approximately 40%. These numbers suggest that Shuck spent too much time trying to pull the ball over the fence two years ago, and the video suggests the same thing. Here’s an example of him trying to do just this to a pitch on the outside corner, but instead weakly grounding to first. You can see how he opens his hips before he even starts his swing, forcing him to simply slap at the ball if he wants to make any contact:

And here he is in 2015, driving a similar pitch into left field:

The cause of his change in approach is hard to say. He did get a new hitting coach to start off the year, switching from Jim Eppard to Don Baylor. From 2013 to 2014, the Angels as a team increased their FB% from 33% to 34% and their Pull% from 37% to 42%, so that argument does have some merit. Regardless of the reason, it’s clear that it had an effect. Here’s Shuck’s ISO by zone:

As can be seen on the left, Shuck had trouble hitting anything not on the inside edge of the plate in 2014. This past year, he learned to control more of the strike zone, and even though there’s less red than there was in 2014, there’s also a lot less dark blue. Shuck drove the ball from all parts of the zone to all parts of the field, and his numbers improved because of it.

While Shuck may not be an All-Star anytime soon, his year-to-year improvement is truly remarkable. If he can go from being the worst hitter in baseball to an average one, anyone can. And if that doesn’t inspire the Brendan Ryans of the world, I don’t know what will.

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. Consequently, Frankie Frisch is listed on the Giants roster for the duration of his career while the Indians declare Rocky Colavito and the Mariners claim David Ortiz. 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 finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “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

Assessment

The 1969 Cincinnati Reds          OWAR: 58.1     OWS: 362     OPW%: .619

Based on the revised standings the “Original” 1969 Reds recorded 100 victories and claimed the National League Western Division by 14 games over the Giants. Cincinnati topped the circuit in OWS and OWAR. GM Gabe Paul acquired 27 of the 40 ballplayers (68%) on the 1969 Reds roster.

Pete Rose (.348/16/82) notched his second straight batting title and paced the League with 120 runs scored. “Charlie Hustle” rapped 218 base knocks including 33 doubles and 11 triples while establishing personal-bests in OBP (.428) and SLG (.512). Jim Wynn aka the “Toy Cannon” unleashed 33 bombs, nabbed 23 bags, tallied 113 runs and topped the circuit with 148 bases on balls. Frank “The Judge” Robinson (.308/32/100) registered 111 aces and finished third in the MVP balloting. Third-sacker Tony “Big Dog” Perez belted 37 round-trippers, knocked in 122 runs and merited his third consecutive All-Star invite. “The Little General” Johnny Bench swatted 26 big-flies and drove in 90 runs during his sophomore season. Lee “Big Bopper” May crushed 38 moon-shots and plated 110 baserunners to earn his first appearance in the Mid-Summer Classic.

Johnny Bench places runner-up to Yogi Berra in the All-Time Catcher rankings according to Bill James in “The New Bill James Historical Baseball Abstract.” Teammates listed in the “NBJHBA” top 100 rankings include Robinson (3^rd-RF), Rose (5^th-RF), Wynn (10^th-CF), Perez (13^th-1B), Vada Pinson (18^th-CF), Curt Flood (36^th-CF), May (47^th-1B), Leo Cardenas (50^th-SS), Johnny Edwards (53^rd-C), Tommy Harper (56^th-LF), Cookie Rojas (69^th-2B), Cesar Tovar (79^th-CF), Tony Gonzalez (82^nd-CF) and Tommy Helms (99^th-2B).

Mike Cuellar (23-11, 2.38) earned the Cy Young Award while fashioning the lowest WHIP (1.005) of his career. Claude Osteen (20-15, 2.66) delivered career-bests in victories, innings pitched (321), strikeouts (183) and WHIP (1.143). Jim Maloney contributed a 12-5 record with a 2.77 ERA and Casey Cox (12-7, 2.78) furnished strikingly similar statistics. Diego Segui anchored the bullpen with 12 wins, 12 saves and a 3.35 ERA.

The “Original” 1969 Cincinnati Reds roster

Honorable Mention

The “Original” 1974 Reds                 OWAR: 52.6     OWS: 336     OPW%: .557

Cincinnati scrapped with Atlanta in the final weeks of the season. The Braves emerged with the division crown by two games while the Reds paced the National League in OWAR and OWS. Johnny Bench (.280/33/129) scored a career-high 108 runs and topped the RBI charts. Jim Wynn walloped 32 circuit clouts, drove in 108 baserunners and amassed 104 tallies. Pete Rose’s batting average dipped below .300 for the first time in ten years. All the same, “Charlie Hustle” paced the circuit with 45 doubles and 110 runs scored. Dave Concepcion earned his first of five Gold Glove Awards and contributed a .281 BA with 14 wallops and 41 steals. Hal McRae (.310/15/88) responded with 36 doubles after earning a full-time role. Ross “Scuz” Grimsley furnished an 18-13 record with a 3.07 ERA.

On Deck

The “Original” 1939 Yankees

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

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

A question that tends to pop up around this time of year: “When does fantasy baseball season start?” Of course, we all know that fantasy-baseball season never ends, especially for those of us in keeper and dynasty leagues. To wit, Brad Johnson’s “Keeper Questions” thread posted just the other day is now sitting at 350 comments and growing. As we all collectively count the days ‘til spring training and opening day, one of the most oft-discussed and most subjectively-answered topics is “Who do I keep?” Fantasy baseball players intuitively understand the idea of aging, at least qualitatively. Older players are less valuable, given that their performance is more likely to decrease due to both injury and ineffectiveness. But how much is age worth, really?

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