Earlier this week, I focused on creating objective measures by which to examine and value individual prospects and farm systems. Inherent in those objective measures is the knowledge that the rankings themselves are not. Prospect writers like our own Eric Longenhagen and Kiley McDaniel combine in-person scouting with their knowledge and experience of the game, information from industry sources, and statistical data to arrive at well-informed but still subjective rankings and grades. What follows is one study attempting to determine if there has been any historical bias based on the position of a player.

As with the prior studies, I’m using the Baseball America Top 100 rankings from 1996 to 2010. To get a sense of how players were ranked by position, here are the raw numbers for the number of players listed at any given position, with multi-position players listed at both positions.

It should come as no surprise to find pitchers and outfielders first given that they have more starting positions available to them. Generally, a pitcher isn’t going to make a prospect list if the person believes he will be a reliever because the value is less. That might be changing some now, but for the vast majority of Top-100 pitching prospects, the hope is that they will be starters. If we were to divide the pitchers by the five starting rotation slots and outfielders by the three starting spots, shortstops would then have the highest representation on prospect lists. After shortstops, we have pitchers and third basemen, with outfielders slotting in ahead of first basemen and catchers with second basemen way down the list. Conventional wisdom holds that ranking so many shortstops is acceptable because many will eventually slide down the defensive spectrum, taking up slots at third base or over at second, which makes up for the lack of prospects there. Read the rest of this entry »

There are 6,000 or so minor-league baseball players at any given moment. By definition, meanwhile, there are only 100 minor-league ballplayers on any given top-100 prospect list. That means there are also around 6,000 minor leaguers not on top-100 lists — all 6,000 of them still intent on reaching the major leagues.

And many of them do reach the majors. For half-a-dozen years, Carson Cistulli has highlighted a number of prospects who failed to make a top-100 list by means of his Fringe Five series, and some of those players — like Mookie Betts and Jose Ramirez — have gone on to become stars. There should be little doubt that prospects outside the standard top-100 lists have value. Determining how much value, however, is a different and more involved question.

When I attempted to determine a value for prospects who’d appeared on top-100 lists, I was working with a relatively small pool of players. Even 15 years’ worth of lists equates to 1,500 players at most. Attempting to determine the value for every prospect, meanwhile, would appear to be a much larger task. Does one look at the roughly 90,000 minor-league seasons over the same period? That seems daunting. Looking at Baseball America’s team-level prospects lists, which feature 10 players per organization, would provide a more manageable 200 prospects per season outside the top-100 list, but that wouldn’t quite get us where we need to be, either.

And yet, as I’ve noted, these prospects have value. On THE BOARD, for example, there are currently 689 prospects with grades (a) of at least 40 but (b) less than 50 (the lowest grade earned by players on a top-100 lists, typically). It’s these prospects in whom I’m interested. What follows represents my attempt to place a value on them, as well.

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Over the years, a good deal of effort has been put into determining the value of prospects. Victor Wang, Scott McKinney (updated here), Kevin Creagh and Steve DiMiceli together, and Jeff Zimmerman have all published work on the subject, roughly in that order.

The reasoning behind such efforts is fairly obvious: teams trade prospects for proven players all the time. Finding an objective way to evaluate those trades is useful to better understanding how the sport operates. Indeed, FanGraphs has benefited from those prospect-valuation studies on multiple occasions.

With another year having passed, I’ve attempted to build on the work of others and produce updated valuations of my own. Previous efforts have been very helpful in the process, while the input of prospect analysts Eric Longenhagen and Kiley McDaniel has helped me find results that would be most useful.

In building this study, I set out with the following aims:

• To separate players into as many useful tiers as possible without creating unnecessary distinctions.
• To use as much data as possible so long as it was useful and likely still relevant today.
• To make the valuations as forward-looking as possible.
• To recognize that player development is not linear and that players appearing on prospect lists vary from major-league-ready to raw, Rookie-level talents.

To those various ends, here are some of the parameters of this study:

1. Baseball America’s top-100 lists from 1996 to 2010 serve as the foundation for prospect grades.
When I started the study, I looked at the lists dating back to 1990, separating out position players from pitchers and organizing by year. I found that the evaluations from the earlier part of the 90s — especially those for pitchers — had considerably worse outcomes than those that came after. I debated whether or not to throw out the data. Eventually, though, I decided that since 15 years of prospect numbers were showing decidedly different results, and that there was considerable turmoil occurring within the sport during that time — expansion, a strike, and a lockout — it seemed reasonable to toss the earlier years and go with the assumption that the 1996-2010 lists more accurately represented prospect evaluation today and going forward than the rankings of 25 years ago.

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The first month of the season was marked by cold weather throughout much of the country. It seemed to have an adverse affect on offense, with power numbers particularly affected. MLB players put up an isolated-power figure of .156 this March and April, which was the lowest mark since April of 2016. Rob Arthur, who has performed extensive research on the juiced ball, noticed the ball wasn’t traveling quite as far in early April even after accounting for weather — this despite a barrage of homers in the spring. Alex Chamberlain conducted some research of his own and determined hard-hit balls and barrels weren’t doing as much damage as in previous seasons, and he wondered if baseballs had been de-juiced. It’s an interesting question that deserves further research.

Chamberlain speculated that MLB had taken the juice out of the ball, potentially through the use of humidors. He found that hitters had to hit the ball harder to get it out of the park. He also observed that, when controlling for exit velocity and launch angle, batted balls weren’t quite doing the same damage as in years past. He concluded that, since we are now well past the cold-weather days of April, the change in batted balls this season is meaningful.

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Last week, I discussed the consequences of an expanded strike zone on the game, finding that it leads to more strikeouts and fewer balls in play. While some have suggested that a larger zone — by inviting more swings from batters — might actually result in an uptick in batted balls, the observed results don’t support that hypothesis. Whatever gains a larger zone creates in terms of swing rate, they’re negated by an increase both in whiff rate and called strikes, leading to more strikeouts overall.

What that post addressed was what would happen if the strike zone got bigger. This post attempts to answer a similar question — namely, what would happen if the strike zone got smaller?

In order to test the effects of a shrinking strike zone, it’s necessary first to identify an actual instance in which the strike zone has gotten smaller. Fortunately, such an instance exists, thanks again to the research of Jon Roegele, who produced this visual in his piece on the strike zone last year.

That’s the 2007 strike zone on the left and 2017 zone on the right. As you can see, the outside edge to lefties used to be called a lot more frequently than it is now. The bottom of the zone has gotten larger for both lefties and righties (a point addressed in my last post), and the result has been a smaller strike zone for lefties than their right-handed counterparts.

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There are a lot of strikeouts in today’s game. The most ever, in fact. If the season were to end today, the league’s 22.4% strikeout rate would represent an all-time high, eclipsing the record set in 2017. That record from 2017 surpassed the one set in 2016, which itself surpassed the one set in 2015, which surpassed the one set in 2014. Ever since 2008, actually, baseball has produced a new strikeout record, and there doesn’t seem to be an obvious end in sight.

With all those strikeouts come a lot of opinions on how to reduce strikeouts. The latest set of proposals come from Tom Veducci at Sports Illustrated. Verducci correctly places blame/credit for the strikeouts with the pitchers, where it belongs, and he suggests a few solutions: lowering the mound, limiting the number of pitchers on an active roster, and introducing a pitch clock.

I find it curious that Verducci omits any mention of the strike zone itself. I have previously proposed raising the bottom of the strike zone to put more balls in play, but there are others — including at least one MLB manager — who believe that a larger strike zone might increase the number of balls in play.

The possibility of this effect is one I’ve heard mentioned on broadcasts before, so it isn’t without precedent. The theory goes like this: an expanded strike zone will force batters to exercise less patience and, as a result, swing at more pitches. More swings, and perhaps more emphasis on contact, means more balls in play.

Fortuitously, this is a theory we can test, because the strike zone actually hasn’t remained static in recent years. In fact, thanks to great research by Jon Roegele, we know exactly where the strike zone has gotten bigger. The very bottom of the strike zone has increased considerably over the last decade, and although it got slightly smaller the last couple seasons, the trend has reversed itself this year. Even if there wasn’t an increase this year, the strike zone would still be substantially larger at the bottom of the zone than it was a decade ago.

There is considerable teeth-gnashing going on around the game due to a lack of action on the field. Those criticisms are not unfounded. All things being equal, the game is better with more and not less action. A walk might be nearly as good as a hit when it comes to scoring runs, but it is considerably less exciting. A strikeout does have some excitement of its own, but on a large field that ranges out to 400 feet in most parks, concentrating much of the action to the first 60 feet has some drawbacks when it comes to demanding and retaining the attention of fans.

In any given confrontation, both the pitcher and batter exert considerable influence over the outcome of an at-bat. Because of that, it might seem reasonable to place equal blame on the hitters and pitchers for the increase in strikeouts. In an era defined by greater velocity and more frequent shifts, one argument goes, batters are failing to adjust. If they would just take the ball the other way, they might strike out less, get more hits, etc.

That might be true. It is also possible, however, that changing their approaches might lead hitters to produce less valuable outcomes or, worse, abandon the very strengths that allowed them to become major leaguers in the first place. That isn’t fair to hitters. What I’d like to posit here is a much simpler explanation for the rise in strikeouts — namely, that pitchers are too good.

Fastball velocity has increased at a steady rate, some of that due to the rise of relief innings around the league and some of it probably to dramatic improvements in training and development. That’s not really the point of this post, though. The point of this post is to discuss one particular cause of the increase in strikeouts that likely has little to do with launch angle or players trying to hit home runs, but rather the talent level of the pitchers and a change in philosophy.

Below is a scatter plot of MLB strikeout percentage and average fastball velocity.

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Andrelton Simmons draws comparisons to Ozzie Smith for his defensive prowess. Both players are recognized as once-in-a-generation all-time greats at their positions, though Simmons has yet rival Smith’s Hall of Fame career.

Apart from the defensive skills, similarities have emerged between Smith and Simmons offensively, as well. Consider that, through the 2016 season, Simmons had taken roughly 2,500 plate appearances and put up a weak 85 wRC+. Compare that to Smith’s first seven seasons, through 1983, when he put up an even worse 74 wRC+ in more than 3,500 plate appearances.

Smith eventually turned his career around offensively, however, putting up a 103 wRC+ from 1984 through 1992 while producing 37 runs by means of the stolen base, a total which might even understate his total offensive value. Smith was bad on offense for quite some time, then he improved and was a good offensive player for a decent portion of his career. It’s possible we are seeing the same type of transformation from Simmons. The Angels shortstop put a 103 wRC+ last season at 27 years old; thus far this season, he’s doing considerably better, with a 143 wRC+ on the strength of his .331/.402/.466 batting line. Most remarkable about Simmons’ hitting numbers are the strikeouts — or lack thereof, rather — as Simmons has struck out in just 10 of his 200 plate appearances.

In 1998, Tony Gwynn stepped up to bat 505 times and struck out on just 18 occasions. The league-average strikeout rate of 17% at that point was nearly five times Gwynn’s 3.6% mark. Preston Wilson made his debut that season and struck out more times than Gwynn despite receiving only 60 plate appearances. Gwynn’s 3.6% strikeout rate isn’t the greatest of all-time. Joe Sewell struck out in under 1% of his plate appearances five times, while 68 players between 1919 and 1951 had qualified seasons with rates lower than 2%. There were 413 seasons during that time where a player’s strikeout rate was lower than Gwynn’s in that 1998 campaign. Gwynn himself even had four seasons with a lower strikeout rate than 1998, but when considering the overall context of strikeouts in the game, Gwynn’s 1998 season is probably the best of all-time. If Andrelton Simmons can keep this up, his season is going to be better.

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If you’ve spent any time observing some of the nerdier battles over baseball statistics in the last decade or two, you’re probably familiar with the arguments made for and against certain metrics. Beginning with the relatively simple matter of batting average versus on-base percentage, these debates tend generally to take the same shape. And generally, one recurring blind spot of such debates is that they tend to dwell on what certain statistics don’t do instead of best identifying what they do do.*

*Author’s note: /Nailed It

The last few years has seen the release, by MLB Advanced Media (MLBAM), of a flurry of new data and statistics, generally referred to as “Statcast data.” We’ve also seen advances in the measurement of catcher-framing by the people at Baseball Prospectus, who have also continued making improvements in the evaluation of pitchers in the form of Deserved Run Average (DRA). When new data and metrics emerge, there is inevitably a period of uncertainty that follows. What does this stat mean? What’s the best way to use this data set? Equally inevitable is the misapplication of new statistics. That aspect of potential statistical innovation is not really new.

Today, what is new is xwOBA — and, in part due to the wide proliferation of Statcast data by means of telecasts and MLB itself, more fans are finding and using stats like xwOBA than might have been in previous generations. As with other new metrics, we are still attempting to identify how xwOBA might best be used.

One such study into the potential utility of xwOBA was recently published by Jonathan Judge at Baseball Prospectus. The study is a good one, with Judge focusing on xwOBA against pitchers. While not ultimately his point, Judge does, along the way, object to the “x” in xwOBA, as he feels that “expected” implies predictive power. While I have always interpreted the “expected” to mean “what might have been expected to happen given neutral park and defense” — that is, without assuming a predictive measure — it does appear that reasonable people can disagree on that interpretation.

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At a basic level, nearly every piece at FanGraphs represents an attempt to answer a question. What is the value of an opt-out in a contract? Why do the Brewers continue to fare so poorly in the projected standings? How do people behave in the eighth inning of a spring-training game? Those were the questions asked, either explicitly or implicitly, by Jeff Sullivan, Jay Jaffe, and Meg Rowley just yesterday.

This piece also begins with question — probably one that has occurred to a number of readers. It concerns how we evaluate pitchers and how best to evaluate pitchers. I’ll present the question momentarily. First, a bit of background.

Fielding Independent Pitching, or FIP, is a well-known tool for estimating ERA. FIP attempts to isolate a pitcher’s contribution to run-prevention. It also serves as a better predictor of future ERA than ERA itself. The formula for FIP is elegant, including just three variables: strikeouts, walks, and homers. It does not include balls in play. That said, one would be mistaken for assuming that FIP excludes any kind of measurement for what happens when the bat hits the ball. Let this be a gentle reminder that home runs both (a) are a type of batted ball and (b) represent a major component of FIP. There is, in other words, some consideration of contact quality in FIP.

Expected wOBA, or xwOBA, is a newer metric, the product of Statcast data. xwOBA is calculated with run-value estimates derived from exit velocity and launch angle. Basically, xwOBA calculates the average run value of every batted ball for a hitter (or allowed by a pitcher), adds in the defense-independent numbers, and arrives as a wOBA-like figure. The advantage of xwOBA is that it removes the variance of batted-ball results and uses a “Platonic” value instead.

The introduction of Statcast’s batted-ball data is exciting and seems like it might help to better isolate a pitcher’s contributions. But does it? This is where I was compelled to ask my own, relatively simple question — namely, is xwOBA better for assessing pitcher performance than the more traditional FIP? What I found, however, is that the answer isn’t so simple.

The differences between FIP and xwOBA, as well as the similarities, deserve some exploration.

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