Author Archive

FanGraphs Spotlight: Custom Comparable Downloads

Sam Navarro-USA TODAY Sports

FanGraphs Members can download any of our stats over any time frame. One of my favorite ways to use the download feature is to compare information over unique time frames. For today’s example, I’ll compare the first- and second-half fastball traits of a few Cardinals starting pitchers.

I’m going to show an example comparing two time frames for just one team, but I usually use multiple time frames (e.g. previous two seasons, first half, last month) for hundreds of pitchers. And while this comparison is especially helpful to fantasy players, anyone can use the procedure for their player analysis. One key in analyzing players, especially pitchers, is selecting unique time frames. Most people will compare first- and second-half stats, but I’ve found hidden gems by using three to eight week intervals. No site provides odd intervals in their default time frames, like the first four months to the last two, or the season divided into quarters. These data downloads help facilitate that sort of analysis. Again, the key is to find breakouts that others might not have identified. Read the rest of this entry »

High Fastballs and Hidden Strikeouts

Every year, I help write the Fantasy Profiles you see on FanGraphs player pages. One of my assigned players for the 2020 season was Michael Pineda. Pineda is a bit of a mystery. In 2019, his fastball was a unicorn. Nothing in his profile made sense. I decided to investigate, and tweeted out my initial findings:

Here’s a detailed breakdown of the above numbers:

Michael Pineda’s Recent Fastball Results
Season FBv Usage Spin Bauer Units GB% Zone% Total Movement SwStr%
2016 94.1 51% 2086 22.2 41% 54% 8.6 6.9%
2017 93.9 49% 2088 22.2 48% 62% 9.6 6.7%
2019 92.6 55% 1999 21.6 29% 61% 7.7 9.2%

No improved performance indicators stick out quite like higher velocity, greater spin, or a pitcher living in the strike zone more. Sometimes a pitch will improve if it’s thrown less often since batters don’t expect it, but Pineda’s fastball usage jumped. The flashing red lights are with the groundball rate; Pineda’s fastball’s groundball rate was almost halved. Maybe he was throwing higher in the strike zone. Here are his pitch location heat maps over those three seasons. Read the rest of this entry »

Team Win Projections vs. Actual Win Totals, 2007-Present

Full-season team projections cause some heated arguments. If a team finishes the year with fewer wins than expected, fans want to know why their club underperformed projections. If a team overperforms its projections, meanwhile, those same fans will insist that forecasts in subsequent years lack the ability to detect their club’s particular strengths and are thus useless.

Here at FanGraphs, we have only been doing full-season projections for a couple years, but just about every week I see a mention of the 2015 World Champion Kansas City Royals’ projected record of 79-83. If I search Google for “79-83 Royals FanGraphs,” I get over 11,000 article links. Unsurprisingly, it’s a popular topic. Rarely does a club, following a pair of World Series appearances, then proceed to fail to break even. But that’s what the numbers suggest for 2016.

While FanGraphs has produced team win projections for only a couple seasons, Replacement Level Yankee Weblog (RLYW) has been publishing win projections for years. Since 2007, to be precise. Given this larger sample, I thought that it might be worthwhile to compare the projected win values produced by RLYW to the actual final win values produced by teams. So, with the permission of RLYW editor SG, that’s what I’ve done here.

I hate to disappoint anyone, but there are actually aren’t any great findings in the plethora of graphs to follow. I did find a couple interesting artifacts of the data, but no game changers. Instead, I see the following mainly as an additional data point in many past, present, and future discussions.

To start with, here is how projected and actual values have correlated.


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MLB Farm Systems Ranked by Surplus WAR

You smell that? It’s baseball’s prospect-list season. The fresh top-100 lists — populated by new names as well as old ones — seem to be popping up each day. With the individual rankings coming out, some organization rankings are becoming available, as well. I have always regarded the organizational rankings as subjective — and, as a result, not 100% useful. Utilizing the methodology I introduced in my article on prospect evaluation from this year’s Hardball Times Annual, however, it’s possible to calculate a total value for every team’s farm system and remove the biases of subjectivity. In what follows, I’ve used that same process to rank all 30 of baseball’s farm systems by the surplus WAR they should generate.

I provide a detailed explanation of my methodology in the Annual article. To summarize it briefly, however, what I’ve done is to identify WAR equivalencies for the scouting grades produced by Baseball America in their annual Prospect Handbook. The grade-to-WAR conversion appears as follows.

Prospect Grade to WAR Conversion
Prospect Grade Total WAR Surplus WAR
80 25.0 18.5
75 18.0 13.0
70 11.0 9.0
65 8.5 6.0
60 4.7 3.0
55 2.5 1.5
50 1.1 0.5
45 0.4 0.0

To create the overall totals for this post, I used each team’s top-30 rankings per the most recent edition of Baseball America’ Prospect Handbook. Also accounting for those trades which have occurred since the BA rankings were locked down, I counted the number of 50 or higher-graded prospects (i.e. the sort which provide surplus value) in each system. The results follows.
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RIP: Kaiser Carlile

This past Saturday, I was collecting some first-hand accounts on a few players at the NBC World Series in Wichita, Kansas. The NBC World Series brings in some of the top teams from various college summer leagues for a double elimination tournament. On Friday, I was up until 2 a.m. watching the final Friday game and then at the stadium for the 8 a.m. game.

During the day’s third game (Liberal Bee Jays vs San Diego Force), a play was just finishing, when I heard the sound that resonated throughout the stadium. The on-deck batter was taking a warm-up swing and hit the team’s bat boy in the head. While I didn’t see the actual contact the Bee Jays’ player made with their bat boy’s head, I saw the last few steps the boy made in his short life. He stumbled twice and then fell to the ground. Then chaos ensued for a few minutes. The player who just hit the boy was holding the lifeless body in his arms. The boy’s mother was screaming as she jumped on the field. The home plate umpire immediately began to administer first aid. During this whole time, I never saw the little guy move.

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Batted-Ball Rates vs. Velocity Changes

Last year, I revisited Mike Fast’s “Lose a Tick, Gain a Tick” article and found how much a pitcher should expect to see his ERA, FIP and xFIP change with a velocity decline. Additionally, I found the rate of decline of strikeouts and walks. An interesting finding from the work was that FIP and ERA change by the same amount with a velocity decline while xFIP doesn’t follow the other two. I decided to examine some batted-ball stats to see which ones change when a pitcher’s velocity changes.
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Which Defenders Make the Plays They are Supposed To?

Defensive statistics have been open to debate since they were first created. This back and forth probably will continue on for years to come, even with some new technologies offering the promise of better data.  One limitation with giving individual players values for their defensive metrics is positioning. The player’s coaches may have them completely out of position for a seemingly routine play and zone based metrics are going to downgrade the player because they didn’t make the play. While it may be impossible to know the correct player position before each play, the chances of a defender making a play knowing their initial position can be estimated with Inside Edge’s fielding data. By using their Plays Made information, I will add another stat to the defensive mix: Plays Made Ratio.

The concept is fairly simple. Inside Edge provides FanGraphs with the number of plays a defender should make given a range of possible chances. Inside Edge watches each play multiple times and grades the difficulty of the play. Here is their explanation for how they collect the data.

Inside Edge’s baseball experts include many former professional and college players. Every play is carefully reviewed, often more than once. It is not uncommon for IE scouts to review certain plays together in order to reach a consensus on the defensive play rating. IE also performs a thorough post game scrubbing process before the data is made official.

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Did Bumgarner and Shields Throw Too Many Pitches?

Madison Bumgarner pitched quite a bit this past season. Including the regular and post season, he threw a total of 4,074 pitches, which wasn’t even the season’s top total; James Shields bested him by throwing six more, for a total of 4,080 pitches in 2014, not including spring training. So with all of the pitches thrown this season (and one month less of rest), how should we expect these two to produce next season? Let’s look at some comparable pitchers.

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Alex Gordon, UZR, and Bad Left Field Defense

Since Alex Gordon moved into first place in position player WAR (although he’s now second again), quite a bit of back-and-forth discussion has occurred on if he is this season’s best position player. Most of the talk revolves around how much stock  should people put into defensive statistics. Our own Dave Cameron has already taken a stab at the subject earlier in the week. Alex Gordon is getting close to two wins of value from his defense, a considerable jump from his previous seasons. After looking at the inputs used for UZR, it is not Alex Gordon’s performance going to new levels, but the lack of talented defenders in left field making him seem better.

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Component Changes in New Hitter Aging Curves

A few weeks ago I noticed hitters no longer improved, but instead their production only declined as they aged. Additionally, I answered at few questions on the first article in a second article. Today, I am going to look at the individual hitter traits to see which ones may be leading to the early decline.

First off, I will start off with some disclaimers.

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