Astromets Mind: January 2016

Friday, January 29, 2016

Mets Minor League Stadium Effects

            Astromets Mind now has a Minor League Guts page (there’s a permanent link at the top of the page), with linear weights and park factors from 2011-2015, presented as Fangraphs does their Guts page. Now that the numbers are up, I wanted to investigate how the Mets minor league parks played in 2015, and look for trends from the past 5 seasons. If you want a better idea of league context for the numbers below, I looked at the 2015 scoring environments of all affiliated leagues in my last post.
Park factors can give us a good idea of how a stadium played relative to the league, but it’s still more intuitive to look at rate stats, so I’ve included a Tableau worksheet with rate stats for each home park in the Mets system over the past 5 seasons (note, these are home park splits, not home team splits). I’m not including GCL data here, and I’ve included 2011-12 data for the Las Vegas 51s instead of the Buffalo Bisons because the Buffalo information would not really be relevant. I’ve also included a worksheet that allows you to look at wOBA by batted ball type for each of the Mets minor league stadiums over the past 5 seasons.

Tuesday, January 19, 2016

Comparing Run Environments From the Majors to Rookie Ball

Average batters age by league for the 2015 season

Before we start evaluating prospects, we should get a sense of the offensive environments in which they played.

            Thanks to Fangraphs, we never have to guess how much a player’s performance is influenced by the league’s run environment; we just have to compare their wRC+ or ERA- (or other +/- stat) to the league average of 100. But Fangraphs only publishes advanced pitching statistics for the major leagues, so let’s take a step back and look at the league’s overall run environment before inspecting or comparing minor league pitcher’s stat lines. We really should be investigating league and park specific run environments regardless, but it has to be the starting point when there are no league-adjusted stats readily available.

Saturday, January 9, 2016

Scouting Thor Through Pitch Sequences

How often did Thor go to that grip in 2015, and what factors influenced his decision to do so? (Image source)

              Since we're already well into the offseason, I'm sure you've already seen a lot of analysis looking back at the 2015 Mets, but my goal here has always been to try and bring you something you won't see elsewhere, so let's dig into some pitch sequencing data. I’m looking at Noah Syndergaard today, and I’ll work my way through rotation over the next few weeks. There’s a lot of information about a pitcher that can be obtained by looking at pitch sequences, and I won’t be reviewing every detail from the tables and graphs posted below, so you might notice something I haven’t, or think of a question I haven’t addressed, in which case share it with me in the comments or on twitter. If you want to get your own PitchFx data, you can download it from Baseball Heat Maps.
It took a little longer than some people predicted, but Noah Syndergaard made an amazing major league debut in 2015: 166 K: 31 BB in 150 IP with an 88 ERA-/84 FIP-/75 xFIP-. His 97.1 MPH fastball was the fastest of any pitcher who threw at least 150 innings in 2015, and his curveball whiff/swing rate was 2 standard deviations above league average, per Brooks Baseball. As if that wasn't nasty enough, he also threw a high 80's changeup that produced whiff/swing and GB/BIP rates more than a standard deviation above league average, and started mixing in a nasty slider at the end of the season. Brooks can give you tons of information about Syndergaard's pitch usage and outcomes from the 2015 season, but I haven't seen pitch-sequencing data available anywhere yet. Also, while I've seen some great discussions on this topic around the web (The Hardball Times is a great place to start on the subject), most have had a league-wide focus, and I'm interested in looking at the individual level here. Below I show how and when Syndergaard mixed his pitches, and then investigate how well they played off of each other.
Syndergaard threw 2,380 pitches to 603 batters in 2015 with two intentional walks issued, which leaves us with 1,769 pitch sequences worth investigating. The first graph below is the distribution of those sequences with a minimum count of 75 (I combined sinker's and fourseamer’s into one FB category for this graph), and the fill breakdown is by the ball-strike count when the second pitch was thrown. Below that I've included a table of the relative frequency of each sequence by count, which also includes the 'FB-SL' and 'SL-FB' sequences. If all sequence types were included in that table, each row would add up to 100%.

Aside from being pretty, the first graph gives us some clues about pitch usage tendencies without overloading us with numbers. For example, the CU-CU sequence to LHB's has no purple hues, which indicates that he never doubled up on curveballs to lefties if the count went to 3 balls. The same column in the RHB table suggests that he might double up on the curveball early or when ahead in the count, but was much less likely to do so later in the count. As expected, curveballs are more frequent vs. RHB’s, and changeups more frequent against LHB’s. The table below it might overwhelm with numbers at first, but the numbers give us an even better idea of Syndergaard's tendencies. First, the right-most column with the smaller numbers represents the count for each row so you can get an idea of sample size. By looking at the CH-FB column you can see that Syndergaard was more likely to throw his fastball after a changeup if that changeup put him behind in the count. Also, as expected from what we already knew, he's more likely to throw consecutive curveballs early in the count, and the fastball in any count. While the above charts give a good visual representation of how Syndergaard mixed his pitches in 2015, it doesn't consider how the change in count influenced Syndergaard's decision. Say Syndergaard just got ahead 1-2 with a fastball to a lefty, how likely was he to throw a changeup with his next pitch? That's what the tables below will tell you. We could take it further too, breaking it down by times through the order, runners on base, whether or not it was a close game, whether or not the Mets were winning... but the samples start getting small quickly, and I don't want all those tables here anyway.

Naturally, if the previous pitch was a ball, the count can't possibly be 0-1 or 0-2 (same for previous pitch strikes and 0 strike counts). From the table above, you can see that Syndergaard was more likely to throw a changeup to a lefty after a fastball for a ball than after a fastball for a strike in nearly all counts. Also, if he just got ahead 0-2 against a RHB with a fourseamer, it was a coin flip as to whether he'd double up on the fastball or use the curveball.


From the curveball table, you can see that Syndergaard often doubled up on the curveball after falling behind 1-0 against righties, or getting ahead 0-2 against lefties.

One could keep inspecting the above usage tables for trends and outliers in the data, but right now we're only looking at one dimension of the pitch, its type. A convenient metric for considering multiple dimensions of the pitch (velocity and location) is effective velocity. Effective velocity (EV) is Perry Husband's concept that the more jammed a batter is, the less reaction time he has to get good wood on it, so the pitch is effectively faster than its radar gun value. Alternatively, the farther low and outside the pitch is, the longer a batter has to wait on it, and so the pitch is effectively slower. As a result, there is an imaginary line from the batters feet to shoulder high and outside where the effective velocity of a pitch is equal to its actual velocity, and for every 6 inches away from that line on the horizontal axis, the effective velocity gains or loses 2.75 MPH. Pitchers are already looking to disrupt a batters timing by changing speeds and location pitch-to-pitch, effective velocity just gives them a blueprint for maximizing that timing disruption between pitches in a PA. (For more on the subject, check out this great SBNation piece written by Jason Turbow for a full background and better understanding than you’ll get from my 3 sentences, or this Dan Weigel piece from Beyond the Boxscore that tracks EV changes throughout a few PA's with Trevor Bauer on the mound).
Considering the speed variation in his impressive repertoire, Syndergaard was already near the top of the league in average change in velocity, but he's even better by average change in EV. I haven't seen a standard approach to calculating EV, but my numbers match up pretty well with this site, which has posted delta-EV data on all pitchers who threw at least 150 IP over the past 3 seasons (and takes a similar approach as Weigel, except with Collin McHugh as the main subject). By radar gun velocity, Syndergaard averaged a change of ~7 MPH from pitch-to-pitch, but by EV that change increases to ~11.3 MPH. Intentional or not, Thor is increasing his average delta EV with his breaking ball and changeup usage, as you can see from these Brooks Zone Profiles.

Zone Profiles of Thor's soft stuff, catchers POV (from Brooks)

Soft stuff is mostly down and outside, and it kind of looks like the strike zone is split in half diagonally across the strike zone. His fastball usage is more scattered against righties (as it should be for a pitch thrown so often), but he rarely came inside with the heat against lefties, which suggests he wasn't using the EV concept to influence his pitch decisions. Still, he's probably going to have to start coming inside more against lefties in the future, even if just as a 'show-me' pitch, because otherwise they'll be sitting on the outside corner. And if he does, it should only benefit his average EV gap.
         Let's wrap this up by comparing the results of the second pitch of a sequence given the first. Ideally we'd use linear weights/100 pitches for comparison, but the samples are too small for that here, so instead we’ll look at the swinging strike rate, batted ball profile, and wOBABIP (wOBA on balls in play). The graphs below are grouped by the first pitch of the sequence, and is looking at swinging strike rate on a per pitch (not per swing) basis. For a baseline, here are Thor's overall swinging strike rates by pitch type:

Pitch Type
Swinging Strike rate

         The pitch type above each graph represents the first pitch thrown in the sequence. From the results above, you can see that the swinging strike rate on Thor's fourseamer increases after a changeup or breaking ball and that the curveball is least likely to get a swinging strike when following a curveball. Interestingly, the changeup's swinging strike rate is best when Thor doubled up on the pitch, and the curveball has a higher whiff rate when thrown after a sinker than after a fourseamer. Here is his batted ball profile by pitch type for a baseline comparison for the following graphs:

Pitch Type

          The number above each set of bars represents the count for that sequence. Here you can see that Thor's sinker had a higher groundball rate when following a soft pitch than when following a hard pitch. The highest fly ball rate came on fastballs that followed a changeup, and it's not even close. The highest popup rate came on curveballs following a sinker, and that sequence also had a higher than average swinging strike rate. Lastly, here is Thor’s wOBABIP by pitch type for a baseline comparison for the following graphs:

Pitch Type

         The numbers represent the BIP count, and considering the small sample sizes, I almost didn’t post this graph. But for what it’s worth, these final graphs confirm that Thor’s sinker and curveball complemented each other extremely well last year.
         This is just some of the information that one can find while digging through pitch sequence data, so I think it’s easy to understand why THT is taking their time to define the pitch-sequencing question before looking for an ‘answer’. It’s only one season of data on Thor, so we have no way of knowing how reproducible these results will be for him, but we still learned a few new things about 2015 Thor. We can see exactly what his tendencies were with the ‘Probability of Next Pitch’ tables, and while a few trends stick out, it appears like he keeps a pretty consistent approach regardless of count. We saw that Syndergaard had one of the largest EV gaps from pitch-to-pitch, and that he could increase the gap by coming inside more often (especially to lefties). Finally, the last three graphs suggest that his curveball and sinker complemented each other best last season, which is interesting since it's often suggested that fourseamer’s complement the curveball better - although, again, the sample sizes got pretty small.

Tuesday, January 5, 2016

Mets Minors Transactions, September 9 – January 4

I think it's time to replace the Sand Gnats image...

Keeping track of transactions within the Mets system

            Hello all and happy 2016! It’s been far too long since my last post, for which I apologize, but I’m ready to come back from a much-needed Internet break. I’m going to get some bookkeeping details out of the way here first, but there are 42 days until the Mets report to camp, and I should have plenty to say about 2015 between now and then.

9/23 – Sign free agent Briam Campusano to a minor league contract.

10/9 – Reassigned Eric O’Flaherty, Dario Alvarez, Eric Campbell, Johnny Monell, Dilson Herrera, Bobby Parnell, Sean Gilmartin, Anthony Recker, Tim Stauffer, Carlos Torres, Juan Uribe Eric Young Jr, and Logan Verrett to the minor leagues.

10/12 – Selected Matt Reynolds from Las Vegas and ‘reassigned’ Ruben Tejada to the minor leagues. Tim Stauffer was DFA’d (he’d elect free agency two days later).

10/17 – Sean Gilmartin was activated from the minor leagues, and Erik Goeddel was sent to the minors to clear space.

10/17 – Juan Uribe was activated and Matt Reynolds was returned to the minor leagues.

11/2 –Yoenis Cespedes, Juan Uribe, Bobby Parnell, Eric O’Flaherty, Daniel Murphy, Kelly Johnson, Bartolo Colon, Tyler Clippard, Jerry Blevins elected free agency.
submit to reddit