Archive for PitchFX

As I noted here on August 15, major league batters are being hit by pitches at rates not seen in over a century (measured by HBP/game). I offered data illustrating this but was at a loss explaining it. Fortunately, I spent the following weekend at the Saber Seminar in Boston, surrounded by a bunch of really smart people, so I posed the question to them.

To be clear, everyone was surprised by the conclusion. Through Sunday’s games, there were 67.4 hit batters per 100 games in 2014. That’s the highest rate since 2001-2008 which, in turn, is the highest since the two leagues were formed in 1901. (Note that these numbers are different from the ones in my original post. When I downloaded league totals from FanGraphs, I hadn’t realized that Games referred to player games, not team games. So I was using a denominator that was too large. The conclusions still hold. I’ve updated the figures in a comment to the August 15 post.) If you didn’t notice this spike in hit batters, join the club. This appears to be an entirely under-the-radar trend.

Asking around, I got several possible explanations. Dave Cameron, FanGraphs managing editor, suggested that since PITCHf/x has clearly demonstrated that left-handed hitters suffer from an elongated strike zone on the outside part of the plate, lefties might be setting up closer to home in order to reach those outside “strikes.” That would make them more likely to be hit by inside pitches.

To test this, I looked at data from 2010-13, when there were 63.8 hit batters per 100 games, and compared them to 1980-83, when there were just 32.6. Switching from HBP/100 games to HBP/1000 plate appearances (since games contain a combination of left- and right-handed batters), the hit batter rate went from 4.3 in 1980-83 to 8.3 in 2010-2013. Right handed hitters got hit at a rate of 4.6 per 1000 plate appearances in 1980-83 and 9.0 in 2010-13, an increase of 95%. For lefties, the HBP rate went from 3.8 to 7.3, and increase of…95%. Exactly the same. Handedness hasn’t been an issue.

Former major league pitcher Brian Bannister suggested that I correlate HBP rates to measures of power. He noted that he didn’t like to come inside on sluggers, for fear that they’d pull the pitch down the line and into the bleachers. I thought this was a sharp, counter-intuitive insight: A rash of longballs makes pitchers work away rather than come inside. With offense in retreat in recent years, pitchers are more willing to pitch inside, and when they miss, the batter gets hit.

I looked at three measures of power: home runs per plate appearance, slugging percentage, and isolated power. I correlated these metrics to hit batters per game for the period 1980-2013. If Brian’s hypothesis is correct, there should be a negative correlation–as power increases, hit batters decrease. However, the opposite was true: 0.84 correlation coefficient between hit batters per game and homers per plate appearance, 0.55 for slugging percentage, 0.65 for isolated power. Maybe my endpoints were wrong? I checked 1970-2013 and got pretty much the same results: Correlation coefficients of 0.84 for HR/PA, 0.66 for SLG, 0.73 for ISO. I was ready to think that maybe hit batters are a result of more power, not less, but then I picked 1990-2013. At least during those 14 years the correlations were weaker, coming in at 0.82 for HR/PA but 0.25 for SLG and 0.43 for ISO. That’s still consistent with the observation that hit batters have remained high in the post-PED era. I don’t see a strong case for fingering the long ball as a cause for hit batsmen, one way or the other.

SiriusXM radio host Mike Ferrin thought we may be seeing a cultural shift of sorts. In college ball, he pointed out, batters view getting hit as an on-base weapon. Might an influx of college players be driving up HBP rates?

Unfortunately, neither the FanGraphs Leaders board nor the Baseball Reference Play Index have college vs. non-college splits, so I looked at the most-plunked batters in 2013 and 1983. In 1983, players with four or more HBP represented the top 53 overall and collectively comprised 274 of 717 HBP that year, or 38%. Of those 53, 30 (56%) attended a secondary school in the US. (I am going to use “attended college” instead of “attended secondary school” going forward, but I mean players who went on for any schooling, including junior college, following high school in the US.) They were hit 10.2 times per 1,000 plate appearances. Players who didn’t go to college were hit 10.4 times per 1,000 plate appearances. That’s our baseline: No evidence of college kids leaning into pitches the year “Every Breath You Take” and “Billie Jean” were top hits.

Now, 2013. There were 15% more teams than in 1983. As it happens, there were 61 hitters with seven or more HBPs in 2013, and 61/53 = 1.15, so 61 is the appropriate sample size for consistency. Those 61 batters were hit 587 times, 38% of all HBP, just like our sample for 1983. Here are the relevant metrics:

• Percentage of most-hit players who attended college: 57% (30 of 53) in 1983, 49% (30 of 61) in 2013
• Percentage of HBPs by most-hit players that were players who attended college: 56% (167 of 298) in 1983, 47% (275 of 587) in 2013
• HBP per 1,000 plate appearances, all most-hit players: 10.4 in 1983, 18.2 in 2013
• HBP per 1,000 plate appearances, most-hit players who attended college: 10.2 in 1983, 18.5 in 2013
• HBP per 1,000 plate appearances, most-hit players who didn’t attend college: 10.8 in 1983, 17.9 in 2013

Mike has a point. College players appear to be getting hit more, relative to other hitters, than they were in the past. The rate of HBP per 1,000 plate appearances increased 82% over 30 years for batters who went to college. However, the HBP rate for batters who didn’t go to college was up 65%, which is also pretty dramatic. And the list of HBP leaders has more players who didn’t go to college than in 1983. So while college kids may be bringing a lean-into-the-pitch ethic to the plate, there is still strong evidence that players who didn’t attend college are getting hit more, and the limited data I used don’t indicate that college kids are comprising a growing percentage of plate appearances.

Some of the commenters on my post from the 15th suggested that maybe HBPs are up because pitchers are throwing harder, giving batters less of an opportunity to get out of the way of an errant delivery. Per FanGraph’s PITCHf/x data, average fastball velocity has climbed from around 91 mph in 2007-9 to 92 mph in 2013-14. That’s a pretty tiny difference from a hitter’s perspective (about five milliseconds, or 0.005 seconds, over 60.5 feet), but it’s something. I’m not ruling it out.

Last Wednesday on the Effectively Wild podcast, Baseball Prospectus’s Sam Miller noted that the rate of batters reaching base via catcher’s interference is near all-time highs. (And you thought hit batsmen per 1000 plate appearances was obscure…) He hypothesized that one of the reasons is that batters are standing further back in the batter’s box in order to get extra time — maybe like five milliseconds? — to identify and swing at an incoming pitch. By setting up deeper in the box, batters increase the possibility that their bat may hit the catcher’s glove at the end of their swing, drawing the catcher’s interference call. If that’s correct, wouldn’t moving back also give pitches that break horizontally — two-seam fastballs, sliders, cut fastballs, some changeups — more time to drift into the hitter? It makes sense!

Unfortunately, the numbers don’t back this up. The correlation coefficient between catcher’s interference and hit by pitches is 0.00 since 1962 (expansion in both leagues), -0.10 since 1969 (divisional play), and 0.07 since 1994 (three divisions per league). That doesn’t necessarily mean that the increase in hit batsmen isn’t caused by batters positioning themselves toward the back of the batter’s box, but it does say that whatever’s driving catcher’s interference isn’t the same thing that’s driving hit batters.

So basically I’m back to where I was going into the Saber Seminar. We’re seeing batters hit by pitches at rates not seen in a century. This change has not been widely reported, and I haven’t identified an obvious underlying cause. After talking to people at the Seminar, I still don’t have a great explanation. It could have a little bit to do with fastball velocity, or batter positioning, or players who went to college being willing to get plunked. But I haven’t identified a clear reason thus far. Even with smart guys helping me.

The MLB is not facing a crisis yet, but it may be soon. In an age of instant gratification and the desire to see the biggest, loudest, and longest of highlights, baseball is getting slower and lower scoring. Although picking up the pace would be a simple task for the Commissioner’s Office, picking up the scoring would be much, much more difficult. The reason for the decline in runs per game is not obvious at first glance. But, like all things in the MLB these days, the key lies in the data.

At the turn of the century, the Steroid Era was going on strong. Even when league-wide PED testing was implemented in 2003, runs per game increased from 2003 (4.73) to 2006 (4.86). Since then, runs have dropped significantly, hovering just above four. Rather than looking to possible reasons, such as PED use, the real proof lies in observation. The major change from 2006 to now is the use of PitchF/x data. In 2006, PitchF/x became a staple in every MLB ballpark. The applications for the system are endless, but the focus for scouting hitters is Hot Zones.

Nearly every hitter has a “hole” in their swing. Even Mike Trout struggles hitting balls up in the zone. Miguel Cabrera has (some) trouble with balls on the outer edge, although limited. Pitchers meanwhile dictate the zone. Although they may prefer to throw to one side of the plate or a certain elevation, elite pitchers have no problem working the ball to all parts of the zone and outside it. The game’s most dominant pitcher this year (not up for argument) has scattered pitches everywhere, especially to lefties. For Kershaw of course, the Heat Map does little justice to his ability to locate the ball. Most hitters have a similar hole, so he is more likely to throw it there than he is all over the heat map. It does show his ability to pitch the ball to a spot better than a hitter can make good contact on a pitch in a certain spot. Let’s take a peek at an example.

Paul Goldschmidt is a really, really good hitter of white balls with red laces. If you don’t believe me, ask Tim Lincecum. First, let’s take a look at Goldschmidt’s Heat Map over his career. Nothing too surprising, he likes his baseballs on the inner half of the zone. Once you get out of the zone on the inside though, he becomes not-so-amazing. Now if we take a peek at DJ Pauly G (I will never call him this to his face because I like my current face structure) vs Kershaw, you can see that Kershaw has been pretty good at targeting his cooler zones. The result of this has been a batting average of just over the Mendoza Line. When you look at him against Lincecum, you see something a lot different. This is probably why Lincecum typically has a sore neck the day after he faces the Diamondbacks. While Kershaw has been able to get it out of the zone low and in, Lincecum has tended to leave them over the plate, resulting in the ball coming to rest in the stands.

At first glance, it may be a pretty simple difference that one pitcher is hitting his spots and one is not. At second glance, it might still look the same. If you really squint though, you can see that conventional wisdom would say very rarely throw it inside to Goldschmidt. Goldy would have been pitched around 10 years ago, and almost all the balls would have been dotting the lefty batter’s box. Prior to the installation of PitchF/x, pitchers would likely have been scared to throw it inside to the slugger. Advanced data available via Heat Maps can show something different, which Kershaw has capitalized on.

From a hitter’s perspective, you probably have a decent idea of what you can and cannot do at the plate. Prior to Pitch F/x, hitters kind of knew what to expect. There was once a hitter that pitchers really didn’t know what to do when they faced, so they walked him. His name was Barry, and a large part of why he couldn’t be pitched to was because pitchers had no idea what to do when he came to the plate. In a 2001 USA Today article, it got to the point where the question was asked “How do you pitch to Bonds?” Bonds had no holes, or so it was thought. I would venture to guess that Bonds, and other greats, would have hit far fewer home runs in an age where pitchers knew the specific places hitters could and could not put the ball over the wall.

Now, hitters are faced with more of a dilemma due to the hyper-advanced scouting. Back when it was a simple “he likes to chase sliders outside the zone late in the count”, hitters had some expectations of what they would likely face. Now, their approach has changed to, “I better look for the low and away slider, but he might try to get me with the high heat since I have a high whiff rate there. Or maybe he’ll go for the change since I have trouble when I am behind in the count and I have fouled off two pitches after seeing one or more sinkers on the outer half of the zone during night games played on the West Coast.” The moral is, pitchers have so much data they can know a hitter better than he can know himself. A hitter’s guess on what he may face is much less educated than it was prior to PitchF/x, making it a lot harder to put the barrel on the ball.

Although there are surely outside causes, PitchF/x is a large part of the reason that runs are on the decline. Pitchers have control on where the ball will end up 60’6” later, and if they are able to put it in a place where the hitter is poor, there will be fewer runs. The new data available has helped pitchers much more than hitters thus far, and until something changes in hitters’ approaches or new data comes along favoring batters, we can expect more of the same. Unfortunately for fans like myself who loved watching Barry knock them into the bay in high scoring affairs, it looks like the Steroid Era’s high scoring affairs are long gone. Low scoring baseball is here to stay.

Before his June 6th appearance, Dellin Betances had thrown his knuckle curve 255 times, and it had amassed a value of 8 runs above average(according to FanGraphs), but that is not the point of this post. Betances throws the knuckle curve a lot (48% of the time), batters can’t hit it (74% zone contact, 20% out of zone contact!, for a total contact rate of 42%), and when they do it’s very weakly (15% line drives, 55% ground balls, 10% popups, 0 home runs). It’s impressive  but not what I’m interested in.

Here’s a hint, in gif form

Batters take the pitch for a called strike all the time. They swing at the curve in the strike zone a measly 29.3% of the time. This is where it gets really crazy, they swing at it out of the strike zone 36% of the time! I’ll let that sink in. This may sound hyperbolic (it’s actually hypergeometric) but a literal blind person would be expected to do better than these pros have.  There is an 83.96% chance swinging at random would beat current major league performance.

For a little math aside, you can think of this like one of those marble problems. You have a jar filled with 116 red marbles (pitches in the strike zone) and 139 green marbles (pitches outside the zone), and you pick 84 (swing at) at random. What are the chances that out of the 84 marble you chose more than 34 are red (in the strike zone)?  You can determine the probability of picking more than 34 red marbles using a hypergeometric distribution.

How is it even possible to make major league players look so confounded (see gif above)?

The worst approach at the plate (other than sabotaging yourself) is just swinging at random.  There is an 84% chance that the approach of  these players is worse than random. A possible explanation is hitters are actually trying to swing at more of the pitches outside the strike zone. This sounds like a really stupid strategy, because it is. The only reason hitters should do this is if they were able to crush the knuckle curve when it’s outside the strike zone. Hitters haven’t crushed any of the knuckle curves (an anemic .029 ISO), and they are barely ever hitting it when it’s outside the zone. It makes you wonder if Betances is using Jedi mind tricks.

Assuming that Betances is not a Jedi (if he was wouldn’t he use his powers on his fastball as well?), then something else has to be going on. From the batter’s reaction you can tell that the batter thought the pitch was going to hit him. So, maybe the batters are just so worried about the 95MPH heater that they are getting surprised by the knuckle curve? Still Betances threw the pitch 48% of the time; it’s not a surprise pitch.  Whatever it Betances is doing is definitely making hitters look dumbfounded. I don’t know of any other pitch that gets a higher swing rate out of the zone than in it (if you can think of a pitch that gets more swings out of the zone than in leave it in the comments).

Thanks to Pitcher Gifs for this great gif.

Also and unrelated useless fact, hitter have exactly a .000 wOBA on plate appearances ending with DB’s knuckle curve.

This is definitely something to keep an eye on and look into further.  What makes a pitch look like a ball to the batter when its in the strike zone and look like its going to be a strike when it is out of the zone. This is the only pitch I know of that can do both.

I challenge any reader to find a pitch thrown more than 200 times that has a higher O-Swing% than Z-swing%, and leave the name of the pitcher and the pitch in the comments.

All stats are from FanGraphs PITCHf/x

This article was originally posted at GWRamblings.

As we start a new baseball season, I start a new season of my own. This is my first – of many I hope – analysis and write-up on baseball that I am submitting. I am an avid fan, a numbers geek, an aspiring writer and lastly a bored software engineer. I am also very fortunate. I have a close connection with a former major league player and the ability to leverage his vast experience and knowledge of the game. Hopefully, I can parlay the knowledge I have learned from many years of observation along with the knowledge I have gleaned from my connection to realize my goal as a contributor to the sabermetric community and to the enjoyment of baseball fans everywhere. Here we go!

Question

Is the effectiveness of a sinker dependent on from which side of the rubber the pitcher throws?

I was in Florida in mid March for spring training, talking with a minor league coach when he mentioned that he and a former all star pitcher were in a disagreement about how to throw a sinker. Their debate centers on where a pitcher should stand on the rubber to throw a sinker most effectively. We all understand that a pitcher should not move all over the rubber to become more effective on a single pitch. This would obviously tip off the hitters as to what type of pitch might be coming. But for argument’s sake, a team might have some newly transformed position players learning to throw different pitches. Wouldn’t a team want to know if, for some pitches, it was more beneficial to stand on one side of the rubber than another?

I consider myself a pretty observant guy, but I will have to admit that I never really paid much attention to where a pitcher stood on the rubber. To me the juicy part is watching the ball just after it is released. The dance, dip, duck and dive a pitcher is able to command of the ball is where the action is as far as I am concerned. So watching what a pitcher does before he even starts his motion was asking a little much. Nonetheless, I was certain that with so many pitchers in the majors, that a breakdown of data would show that there was not a singular starting point on the rubber. Every pitcher is different, right?

Setup

I started my analysis by downloading the last 4 years (2009-2012) of PitchFx data. Most of us know this already but by using PitchFx data there are some limitations to analysis. Unlike Trackman, PitchFx initially records each pitch at 50’ from home plate, not the actual release point of the pitch. For PitchFx this data point is called “x0”, and for all intents and purposes this is pretty good data, as for most pitchers their strides are approximately 5 to 6’ from the rubber, and with arms length added in we are talking about a difference of a couple of percentage points from being the same as the release point metric from Trackman. But full disclosure, it is not exactly the release point. Another factor that I didn’t measure is a pitcher’s motion to the plate. Some pitchers throw “across” their bodies and not down a straight line, and even fewer open up their body to the batter (stepping to stride leg’s baseline). Also, there is probably a bit to glean from going between the stretch and wind-up, but again without doing a very in-depth study I assume no factor in the analysis. Lastly, arm length is an unmeasured factor. For example, I didn’t check to see if there were any right-handed pitchers with extra long arms standing on the first-base side of the rubber distorting the data.

I started by combining the PitchFx Sinker (SI) and Two-seam fastball (FT) data into a single database. The reason to combine the data is due to the fact that the grips for each pitch are the same, combine this with a two-seam fastball can and a sinker break the same way (down and in to a RH batter from a RH pitcher), and lastly they are also somewhat synonymous in major league vernacular. Maybe somewhere along the line the pitch was invented twice (north or south), the name given is based on region like when asking for a Coke… it’s a “soda”, a “pop”, or a “tonic” depending on where you are in the states. Maybe in the South it was labeled a sinker and the North it was taught as a “two-seamer”? Either way it’s the same pitch as far as I am concerned, and the etymology of pitch naming is a different topic for a different time.

Back to the question above about every pitcher being different, I was wrong. Using the 2012 data I created a frequency distribution for right-handed pitchers (figure 1), and as you can see there is definite focal area at around -2’ point from the centerline of the pitching rubber (and home plate).

Figure 1 – Right-handed pitchers in 2012

This shows that most pitchers start from about the same side; which I determined to be the right side of the rubber (3^rd base side). I determined this by adding 9” to one-half the length of the pitching rubber (24”) which comes to 21” (9”+12”). Add in arm length and you can see that using an x0 that is less than or equal to 2’ (remember we are using negatives here) should prove that the pitcher is throwing from the right side.  I would like to add that the 9” used above is based on the shoulder width of an average man, which is around 18”. This metric is based on studies on the “biacromial diameter” of male shoulders in 1970 (pg. 28 Vital and Health Statistics – Data from the National Health Survey). I think we can all agree that the 18” is probably conservative by today’s growth standards. I mentioned in the limitations of the analysis written above, I don’t account for arm length or pitcher motion. Therefore I needed to make sure that there are right-handed pitchers who are throwing from the left hand side of the rubber; just not a bunch of super long-armed, cross bodied throwers.  With the data in hand I was able to identify which pitchers had thrown the ball closer to centerline of the rubber and therefore would be good candidates for standing on the left side of the rubber. The first pitcher who had a higher (>-2) x0 value was Yovani Gallardo of the Milwaukee Brewers. Without knowing Gallardo’s motion I needed to go to the video. From the video, you can clearly see that Gallardo starts on the left side of the rubber and throws fairly conventionally, straight down the line to the batter.

I wanted to keep this as simple as possible, breaking up the pitchers in two categories – Left side or Right side. Without looking at video for each pitcher I had to come up with a tipping point for classifying the side based on the x0 data I had available. If we simply take what we determined above and correlate it to the left hand side we will come up with 1 (starting on left side of rubber) and an x0 of 0. But it isn’t quite that simple. The frequency chart shows that there are less than 1000 balls thrown in 2012 with an x0 greater than or equal to 0. Gallardo threw 504 pitches himself in 2012. So we have to increase the scope a bit. By arranging the x0 data into quartiles we see that upper or lower quartile – depending on handedness – is around -1 or 1 (remember we are using negatives) so for a right handed pitcher the x0 splits are:

For left handers:

As I am trying to stay conservative, and the fact that these are not release point numbers I use 1 and -1 as the cut off for classification based on the handedness of the pitcher. Using these numbers provided a pretty clean break in the distributions (90-10%).

Findings

So who was right, the all star pitcher or the minor league pitching coach? Is there an advantage depending on where the pitcher stands on the rubber? Neither – both of them. It’s a tie.

What can I say; my initial analysis is a bit anticlimactic, but not because of lack of effort.  To denote the labels below:

• LH or RH (Handedness)
• RR or LR (Right or Left Rubber)
• B – Balls
• K – Strikes
• P – In play (No Outs)
• O – In play (Outs)
• BackK – Called Strikes
• FT – Two seam fastballs
• SI – Sinkers
• Efficiency – O/(P+O)
• XSide – Cross Side (i.e. RH-LR or LH-RR)
• Same side – LH-LR or RH-RR

The efficiency is so very close. Twelve-hundredths (.12) of a percent is not a lot – 169 outs out of 140678 – but give any Chicago Cub fan five of those outs in 2003 and Mr. Bartman would be an afterthought. Which, I am sure is the way he and all Cub fans around the world would like it. The efficiency is the same, no other way to put it which is the beauty of statistics and sabermetrics. Numbers can say so much, even when they are the equal.

But the analysis wasn’t all for naught, there are some nuggets to glean from the numbers above. As a segue, I am currently watching Derek Lowe of the Texas Rangers pitch on opening night and from the left side of the rubber he throws a sinker and it dips back over the rear part of the plate for a called strike. With all of the similarities within my analysis the most striking observation is the difference in called strikes depending on the side of the rubber. If a pitcher, coach or manager could get a strike or a strike out without the fear of having a batter get a hit or moving a runner forward they would do it every time. With a five percent difference in getting a strike and not having the worry of the ball being put into play would be an interesting thing to know in some tight situations with runners on base. My thought on the difference revolves around the back door being open a little wider when it comes to getting called strikes. With a pitcher throwing X-side you can definitely see a pattern of called strikes on the same side of the plate from which the pitcher throws from. Positive numbers in figures below indicate right side of plate (1^st base side)

With today’s specialization where pitchers are matched up to batters based on handedness, the ability for a pitcher to throw a strike as it tails back over the plate or close to the plate (or maybe not even close for some of the pitches above ) is essential. It appears that umpires are a little more flexible with their perception of the strike zone for these pitchers as well.

Closing

I didn’t get the results that I anticipated when I started this analysis, and that is great! As a society we are determined to have a winner! Just as there is “no crying in baseball”, there are no ties in baseball. Even when there is a tie; like on a close play at first – it proverbially goes to the runner. We can’t settle for a tie…. hockey reduced ties by adding a shootout after overtime.  College football removed the tie by introducing sudden death (hopefully the bowl playoff with help eliminate the subjective BCS tie). With no clear cut advantage (read – TIE) identified in my analysis means that a more in depth analysis could/should be performed to validate. Maybe expanding the percentage of X-side pitchers to 15-20, or identifying when pitchers are throwing from the stretch and removing those instances would alter the results and provide a much needed winner? If after all analytical statistical avenues have been exhausted there’s still not a proven advantage, we can always resort to having the coach and player settle it with a coin flip?