Black swan events are a defining feature of each baseball season. Like any good sport, the contours of the game elicit a comfortable and familiar warmth. But also like any good sport, the details that make up the fabric of a particular contest or campaign are essentially unpredictable. It’s the round ball, round bat game: Weird stuff happens all the time.

Once they happen though, unexpected events have a way of enmeshing themselves in the game’s broader narrative as if they were just another ad on the outfield wall. Our brains struggle to handle surprises, and so we rationalize them. For a time, it was very weird that Lucas Giolito suddenly looked like one of the best pitchers in baseball; by the time the Cy Young ballots were tallied, his breakout season was just another event from 2019, a feel-good moment and a developmental win but no longer a curiosity. Lucas Giolito is now good and we accept this for what it is.

But there’s so much more fun to be had with unexpected events. They’re worth celebrating on their own merits. In one form or another, they happen every day and to every team and we should remember the most notable of those surprises. More to the point, one of these is coming for your club in 2020. Like a birthday present waiting to be unwrapped, each team is just a month or so away from discovering something weird about itself. Today we’re going to use recent history as a guide to imagining what that will look like. Read the rest of this entry »

Naturally, once the ZiPS elves have finished baking the ZiPS, the first thing I want to do — at least after actually getting some sleep — is to crank out some ZiPS projected standings. So let’s wrap up ZiPS Week (I’m possibly the only person calling it this) by doing the first run of the ZiPS projected standings for the 2020 season.

The methodology I use is not identical to the one we use in our Standings, so there will naturally be some important differences in the results. So how does ZiPS calculate the season?

Stored within ZiPS is the first through 99th percentile projections for each player it projects. I start by making a generalized depth chart, using our depth charts as an initial starting point. Since these are my curated projections, I then make changes based on my personal feelings on who will receive playing time, as filtered by arbitrary whimsy my logic and reasoning. ZiPS then generates a million versions of each team in Monte Carlo fashion. The computational algorithms, that is, not dressing up in a tuxedo and playing baccarat like James Bond.

After this is done, then ZiPS applies another set of algorithms with a generalized distribution of injury risk, which changes the baseline PAs/IPs selected for each player. Of note is that higher-percentile projections already have more playing time than lower-percentile projections before this step. It then automatically “fills in” playing time from the next players on the list (proportionally) to get to 700 plate appearances for each position and 1458 innings. 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 »

Assuming he doesn’t get traded, Kris Bryant appears to be David Ross‘ choice as leadoff hitter this season. It’s not a secret that the Cubs have struggled to find a leadoff man since they let Dexter Fowler walk in free agency after their 2016 championship season. Last year, the Cubs’ .294 on-base percentage and 77 wRC+ from the leadoff spot were the worst in baseball.

Over the last three seasons, nine players have taken at least 50 plate appearances from the leadoff spot.

Some of these are small samples, and while we know Jason Heyward isn’t a player who would put up a 44 wRC+ with more playing time, we also know he probably isn’t going to be much more than average with the bat. Given the importance of the leadoff spot, average shouldn’t be good enough for a contending team. Ian Happ was a little above average, but his .319 OBP leaves something to be desired. Even the .333 OBP he put up in limited time overall last year isn’t great. Daniel Murphy was only with the club for a few months. Kyle Schwarber’s career .339 OBP screams pretty good but not start-the-game-off great, and being below-average against lefties means he couldn’t do it every day. Read the rest of this entry »

Read the rest of this entry »

Yesterday I took a look at team payrolls, offseason spending, and the outlook for MLB spending on players as a whole compared to the last few years. Today we’ll take a look at one portion of team payrolls most teams would rather avoid. No organization wants to be paying players to play for other teams or to sit in the minors or to simply be out of the game, at least in the abstract. At some point though, teams will kick in money for a trade because the overall savings can be utilized elsewhere, the prospect return is slightly better, or because there is better use of a roster spot. Those payments become dead money.

As in past years, I’ve defined dead money as generally any money a team is paying out to a player who no longer appears on their 40-man roster. There are three types of dead money:

1. Money paid to players who have been released. Those players are free to sign with other teams, but the team releasing the player still owes the money remaining on the contract.
2. Money paid to other teams as compensation for players who have been traded. Generally, we see teams cover a portion of a contract to receive a better return in trade.
3. Money paid to players who are still in the organization but who have been removed from the 40-man roster. Any team could have claimed these players if they were willing to take on the contract, and the player probably could have elected fee agency, but then he would forfeit his right to the guaranteed money.

Here are the teams with the most money on their current payrolls going somewhere other than their roster. 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 »