When you think of Anthony Rendon, you probably think of consistency. He’s good every year, in roughly similar ways: he doesn’t strike out much, walks a good deal, hits his fair share of homers and doubles, and plays good defense. He’s been worth more than 4 WAR in every one of his full seasons. He’s a line drive hitter, a batting average machine. If anything, he’s become more consistent over time: in each of the last three years, he’s been worth between 6 and 7 WAR and struck out between 13% and 14% of the time.

I don’t buy it, though. Rendon might seem consistent on the surface, but under the hood, he’s completely revamped his game to unlock progressively more offensive potential. In fact, I can retell the Anthony Rendon story as a progressive improvement over time. Let’s try that now.

In 2015, the first year for which we have Statcast data, Rendon was hurt. He sprained his MCL in spring training, sprained his oblique while rehabbing the MCL, and somehow got forced off of third base — a year after a 6 WAR season — by Yunel Escobar. He played the majority of the season at second and scuffled.

That’s a low baseline, which makes any tale of improvement easier to tell. But let’s start there anyway. Rendon didn’t hit the ball with much authority that year — oblique strains aren’t good for power. When he put the ball in play, he generated a .343 wOBA, significantly below the league average of .361. Those numbers don’t really mean much out of context, so think of it this way: in 2019, Luis Arraez and Kolten Wong were below league average by roughly that amount. Read the rest of this entry »

I’d like to show you a graph. It’s not a surprising graph, nor a shocking one. Here’s the production on batted balls across all hitters in 2019, grouped by launch angle:

It’s not exactly rocket science. Hitting the ball straight down is death, hitting the ball straight up is just as bad, and most of the juice comes in line drives and fly balls that don’t approach popup status. There’s even a cute little dimple right around 15 degrees, where the ball has too much loft to be a flare but not enough that you’re all that likely to hit a home run. That all seems to make sense.

Next, let’s complicate it slightly. Here’s the same graph, only with batted balls hit less than 95 mph excluded: Read the rest of this entry »

Read the rest of this entry »

Last week, when giving our playoff odds a quick once-over, I stumbled across something interesting. In translating from player statistics to our projections, we strip out the impact of reliever leverage. That seems intuitively weird, so I wanted to delve into the thinking behind it and see if I could find a workaround.

First, a quick recap of the issue. When we calculate WAR for relievers, we include the impact of leverage. This makes sense — the last reliever off the bench is mostly pitching in blowouts, so their contribution, good or bad, is less important than the closer’s. If you used a dominant reliever in a mop-up role, they’d be far less valuable than if they got to pitch in games where the outcome was uncertain.

How do we adjust for leverage? It’s reasonably straightforward. Take a reliever’s gmLI, which you can find in the Win Probability section. Kirby Yates, for example, had a gmLI of 2.16 last year. gmLI is the average leverage index when a pitcher enters the game. You can find a recap of leverage index here, but it’s essentially a measure of how important a given plate appearance is. A leverage index of 1 means that the situation is exactly as important as the average plate appearance, 2 means the situation is twice as important, and so on. Read the rest of this entry »

With the release of full ZiPS projections, our playoff odds are up and running. For the most part that means putting a number to things that we already know. The Dodgers are 97.7% likely to make the playoffs, which sounds about right. The NL Central is a four-way tossup with the Cubs out in the lead. The NL East has three teams each with around a one-in-three chance at it. That all tracks with intuition.

Indeed, for the most part, the standings are self-explanatory. That doesn’t mean that everything is obvious and intuitive, however. Let’s take a quick look at a few of the cases where a deeper dive is necessary.

It’s tempting to think of a team’s expected win total as just a sum of their WAR. After all, the W is right there in the acronym! As Dan notes every year, however, adding up WAR totals on a depth chart isn’t a great way to go about things. Rather than just do that blindly, however, we can look at teams whose projected wins diverge the most from their WAR.

To do that, we’ll need each team’s projected WAR totals. Thankfully, there’s a handy page that shows all that data. The Dodgers have the most projected WAR and the Orioles have the least.

With that data in hand, we can work out what win totals every team would have if you could perfectly project WAR onto wins. First, let’s figure out replacement level. There are 1120 projected wins across all the teams and 2,430 total wins available in a season. This leaves 1,310 wins as the amount that replacement level is worth. Spread that across the 30 teams, and that’s 43.66 wins per team. Read the rest of this entry »

This is the last of a set of articles I’ve written over the past few weeks. Each one tries to determine what’s real and what’s noise when it comes to the outcome of a plate appearance. For the batted ball articles, the conclusions generally tracked. Variations in home run rate are largely due to the batter. Pitchers and batters both show skill in groundball rate. And line drives and popups are somewhere in between — batters exhibit a little more persistence in variation than pitchers, though neither does so strongly.

Strikeouts and walks are a different beast. It’s pretty clear that pitchers and batters can be good or bad at them. No one looks at Chris Davis or Tyler O’Neill and thinks “eh, that’s pretty unlucky to have all those strikeouts, I bet they’re average at it overall.” Likewise, Josh Hader isn’t just preternaturally lucky — he’s good at striking batters out.

So rather than attempt to prove that pitchers can be good or bad at striking out batters and vice versa, I’m interested in whether one side has the upper hand. I’m adapting a method laid out by Tom Tango here, but I’ll also repeat the same methodology I used in the previous pieces in this series. Read the rest of this entry »

Earlier this offseason, I wrote a few articles about whether pitchers or batters had more influence over different events. There’s nothing groundbreaking about my conclusions — in fact, they specifically reinforce prior studies. Despite that, however, I think there’s value in these refreshers.

Concepts like “batters control home runs” and “pitcher groundball rate matters” are implicit in many of the statistics that you see on this site and certainly in many of the articles that you read here. When we cite xFIP or talk about what a pitcher can do to control his groundball rate, we’re drawing on these concepts.

You don’t need to know these basic concepts to accept the conclusions, but it certainly helps. Appealing to authority (hey, these stats are good because smart people made them) is a pretty bad way to convince someone, and understanding the reason behind a metric is the quickest way to accept its conclusions.

In that spirit, I thought I’d round out the series by looking at a few more common events and working out whether pitchers or batters do more to influence them. Today I’ll be looking at line drive rate and also popup rate, the percentage of fly balls that become harmless popups. Later this week, I’ll cover walks and strikeouts. Then we can move on to more pressing matters, like I don’t know, José Altuve tattoo investigations or what would happen if Mike Trout knew what was coming.

Before looking at line drive rate, I had a rough idea of what to expect. There are plenty of hitters I think of as line drive machines — peak Joey Votto, Miguel Cabrera, even Nick Castellanos. I had trouble placing a pitcher in the same category, unless you count “your favorite team’s fifth starter.” Read the rest of this entry »

Even if the team isn’t quite a contender, there are plenty of reasons to follow the 2020 Pittsburgh Pirates. Bryan Reynolds and Josh Bell are interesting hitters, though there’s a decent chance that neither ever replicates their 2019 success. Chris Archer is a fun puzzle; can he regain the scintillating form he flashed at times on the Rays, or will he be more 2019 Chris Archer, all homers and walks? Mitch Keller is awesome, except when he’s terrible. Those are all storylines you can follow as a Pirates fan. Me? I’m going to be watching Joe Musgrove.

Musgrove put together a nice season in 2019, his second straight year of more than 100 innings and more than 2 WAR. That sounds great, but it’s a little less impressive under the hood. His RA9-WAR has been significantly lower, and if you’re more of an underlying skill person than a runs allowed type, his above-average FIP’s have been misleading; they’re largely down to his suppression of home runs, and if that skill fades, his results might start to look more like his xFIP:

I’ll admit I’m not doing a good job of explaining my fascination with Musgrove so far. Even if you dig into the component parts of his game, nothing jumps off the page. He strikes out fewer batters than average but makes up for it by walking even fewer. He allows a roughly average number of grounders, gives up hard contact at a roughly average rate, and overall blends into the background. Read the rest of this entry »

Read the rest of this entry »

The Astros cheated. That’s not in dispute. The search for just how much the banging scheme helped the team, however, is ongoing. Rob Arthur got the party started. Tony Adams chronicled the bangs. Here at FanGraphs, Jake Mailhot examined how much the Astros benefited, which players were helped most, and even how the banging scheme performed in clutch situations. In a recent press conference, owner Jim Crane downplayed the benefit, saying “It’s hard to determine how it impacted the game, if it impacted the game, and that’s where we’re going to leave it.” It’s a rich literature, and not just because it’s fun to write “banging scheme” — but I didn’t want to leave it there.

I thought I’d take a different tack. All of these studies are based on reality, and reality has one huge problem: it’s so maddeningly imprecise. You can’t know if we captured all the right bangs. You can’t know if the system changed, or if it had details or mechanisms we didn’t quite understand or know about. And even when everything is captured right, those sample sizes, those damn sample sizes, are never quite what you need to feel confident in their results.

If we simply ignore what actually happened and create our own world, we can skip all that grubby, confusing reality. Imagine, if you will, a player who makes perfectly average swing decisions and achieves perfectly average results on those decisions.

Let’s further stipulate, while we’re far off into imaginary land, that pitchers attack our perfectly average batter in a perfectly average way. For each count, they’ll throw a league average number of fastballs, and those fastballs will be in the strike zone at — you guessed it — a league average rate. The same is true for all other pitches — with cut fastballs included in “all other pitches” in this analysis. Read the rest of this entry »