About That Pitch to Justin Turner
We all know that John Lackey’s 1-0 sinker down and in to Justin Turner last night was deposited by the Dodgers third baseman into the outfield seats, resulting in a walk-off win for the home team in Game 2 of National League Championship Series. It’s possible, however, that the Cubs actually lost the game a moment before that — not when Turner’s fly ball cleared the outfield fence, but when Lackey and catcher Willson Contreras agreed on that particular pitch. Because, as good as Justin Turner was in 2016, he was better in 2017 and all the improvements he made in between helped prepare him for Lackey’s 1-0 offering.
Trying to decipher the source of Turner’s superior 2017 campaign isn’t easy. He hit more fly balls this year, yes. Looking through the swing charts, though, it’s not easy to pinpoint one cause for that development. His overall swing and reach rates were down a little, perhaps, but mostly consistent with his career rates.
Two things might jump off the page. First: Turner got better at fastballs. His production versus four-seamers and sinkers, by pitch-type values, improved more than against any other offering. Turner also avoided the strikeout more often.
To record a lower strikeout rate while producing swing and contact rates roughly commensurate with his career averages, it’s likely that Turner somehow altered his sequencing — that is, the order in which he swung at pitches.
Let’s look at 1-0 counts, since that was the situation in which Turner hit his homer last night. In 2016, Turner swung at 39.6% of the pitches he faced in those counts. This past season, Turner swung at 40.6% of 1-0 offering. There seems to be little difference there, until you look at which 1-0 pitches he swung.
| Pitch | Count ’16 | Swing ’16 | Count ’17 | Swing ’17 | Difference |
|---|---|---|---|---|---|
| Change | 32 | 25.0% | 25 | 56.0% | 31.0% |
| Curve | 11 | 9.1% | 18 | 27.8% | 18.7% |
| Sinker | 51 | 29.4% | 68 | 39.7% | 10.3% |
| Slider | 41 | 36.6% | 41 | 43.9% | 7.3% |
| Cutter | 20 | 50.0% | 11 | 45.5% | -4.6% |
| Four-Seam | 102 | 52.0% | 81 | 37.0% | -14.9% |
Turner saw fewer four-seamers in 1-0 counts this year, so he’s adapted. He’s now attacking sinkers in 1-0 counts with greater frequency, even if it means he’s a bit more vulnerable to changeups down and in. Don’t see all of that here? Look at his swing rates on changeups last year and this year, and you do see a few more swings at changeups down and in.
But for that possible added weakness — against a pitch that’s rarely thrown, the inside changeup — Turner has turned up the volume on his work against sinkers. Look at his isolated-slugging heat map on sinkers in 2016 first.
And now in 2017.
See that red spot located middle-in in the latter of those charts? Well, that location should look familiar.
After the game, there was some focus on Turner’s famous changes in approach. He was asked about Chris Taylor’s new swing with a focus on “taking low pitches or pitches that were around the knees and hit them in the air with impact.” He didn’t quite take the bait, saying about Taylor: “The adjustments he made, he did them on his own this offseason.”
But maybe Turner’s “go get the ball” approach has helped him hit sinkers in general. He’s a strong producer against the pitch, top 15 this year, and has been better at two-seamers his whole career.
But last year, Turner was 113th against the sinker by pitch-type values. His production on the pitch took a small hit. It looks like, instead of shrinking from the challenge that the sinker was offering, Turner decided to go get it, particularly in 1-0 counts, where the location of the pitch might be to his advantage.
Being aggressive turned Justin Turner’s career around the first time. Being aggressive against sinkers in 1-0 counts might have been a big part of his resurgence this year. He had a 1.428 OPS against those pitches, anyway. Oh, and one sweet walk-off three-run homer in the playoffs that might have been a long time coming.
With a phone full of pictures of pitchers' fingers, strange beers, and his two toddler sons, Eno Sarris can be found at the ballpark or a brewery most days. Read him here, writing about the A's or Giants at The Athletic, or about beer at October. Follow him on Twitter @enosarris if you can handle the sandwiches and inanity.
Yes, yes it was.
Ummm, huh? The first pitch to Turner was a slider that bounced in the left-handed batter’s box. It was not a strike, hence the count being 1-0 and not 0-1.
My fault! Eno had originally spelled out the count and I transposed the numbers when I changed the words to numerals. Fixed now!
I’m about to add about zero value to this thread, but I have to say, I’ve never liked Lackey much. He’s the most constantly angry player I have seen since the days of Roger Clemens chucking bat shards at people. It’s just not fun to watch.
As such, I have taken to calling him by his lesser known nickname of Grumplestiltskin
That was such a beautiful swing.
Given Turner’s excellence vs sinkers and sinkers’ generally extreme pro-platoon splits, might this provide a substantive explanation for Turner’s career reverse-platoon splits?
Looking at the stats in 2017 only further gives credit to the “reverse-splits aren’t a thing” theory. His wRC+ skyrocketed against lefties this year while his wRC+ dropped slightly against righties.
That being said, it’s hard to ignore the stark reverse-split he’s had throughout his career up until this year. Maybe he’s seen fewer sinkers this year.
‘the “reverse-splits aren’t a thing” theory’…to say this is to say splits aren’t a thing….(hand slowly drawing back from temple with fingers extending wider)
Remember that every single situation calls for a pitch frequency matrix and one pitch is chosen randomly from that matrix according to the “correct” frequencies. The “correct” frequencies are those which result in the exact same “result” (where result is measured in the win expectancy impact of all the possible outcomes combined).
Now, obviously, most pitchers “think” they’re choosing one specific pitch for some specific reason, but in reality since the batter doesn’t know the pitcher’s reasoning, it is essentially a random selection as far as he is concerned.
The pitch selection frequency matrix is dependent on the pitcher, the batter, the count and the game situation.
So in that situation in that count, it might be something like 50% 4-seam 20% sinker, 20% slider 10% cutter. Numbers are irrelevant.
Then a random pitch is selected according to those frequencies, where the 4 seamer is chosen twice as often as the sinker and slider, the sinker and slider twice as often as the cutter, etc.
Obviously doing that even close to accurately is impossible but that’s essentially what happens and what is supposed to happen, and miraculously, pitchers and catchers do a pretty good job (really you just have to have a pretty good idea as to what pitches to throw adjusted a little for the batter).
The more a batter likes (or dislikes) a certain pitch (in that count or overall) the less that that pitch is thrown, so, for example, if Taylor is particularly good at a sinker, for a example, it might be thrown 10% rather than 20% of the time. The same is true for locations of course, which makes everything quite complex (although many of the pitches are thrown in the same location most of the time).
Remember that you can NOT tell what types and locations of pitches a batter likes or dislikes in a certain count and game situation from the results! This is a very important concept to understand. The results of every pitch type and location in each count, game situation, and pitcher (you would have to do a “delta method” to figure this) is and should be exactly the same! Any differences you see are noise – random differences. We can easily prove this.
Imagine that in all 1-0 counts, early in a game with no runners on base and 0 outs, we see that Turner gets a FB 80% of the time and a slider 20% of the time (again numbers are irrelevant). And we see that Turner’s results (we have to add up the run or win value of all the results – strike, ball, batted ball out, single, double, etc. to determine the “result”) are much better against those 80% FB than the 20% SL. Can we determine that Turner is better against the FB in that situation? No! Why? Because if we did, we would HAVE TO conclude that the pitchers were throwing him too many FB, right? So pitchers would NOT throw 80% FB. They would reduce the number. Obviously why throw a pitch 80% of the time that the batter is better against? You would obviously throw it less than 80%. How much less? Well, say you throw it 79% and the slider 31%. You have to be better off since the slider is the better pitch, as we just said for this thought exercise. So what if the FB still yields better results for Turner (and it’s not just noise – he’s still better at the DB when he knows it’s coming 79% of the time)? Well, again obviously, we should throw the slider more and the FB less. We, as a pitcher, will increase our effectiveness since he clearly still likes the FB better.
Where does this end? Every time we decrease the frequency of the FB, the batter gets worse at it since it’s more of a surprise. At the same time, he gets better and better at the slider since we throw it more and more frequently. It ends at the point in which the results of both pitches are exactly equal. It HAS to. If it “ends” anywhere else, the pitcher will continue to make adjustments. This is what you MUST understand that “in theory” any differences you see in either a batter’s results across different pitches, as well as a pitcher’s, MUST be noise – an artifact of random chance. Also keep in mind that that is only true for each subset of identical circumstances – the same opponent, count, and game situation (even umpire, weather, park, etc.).
Is it possible that there are some externalities that influence this equilibrium? Sure. Perhaps a pitcher must throw more FB than off-speed in order to prevent injury. That might cause his numbers for the FB to be slightly worse than other pitches. Or the slider is particularly risky, injury-wise, such that they throw it less than GTO (game theory optimally) which results in a result better (from the pitcher’s standpoint) than the other pitches.
Any other deviations you see among pitch types and locations, by definition, must be random noise, or, exploitable mistakes by either batters or pitchers. It would be difficult to distinguish the two without some statistical analysis of some large samples of pitches (and then we would only have limited certainty as to our conclusions).
So, given that all that is true, which it is (more or less), how can we criticize a particular pitch that a pitcher throws in one particular situation? We can’t. We absolutely CAN NOT. We can’t say that one pitch is “wrong” and one pitch is “right” in ANY particular situation. That’s impossible to do. We cannot evaluate the “correctness” of a pitcher’s single pitch. There is an exception.
The only time we can say that a particular pitch is “wrong” is when that pitch is not part of the frequency matrix – i.e., it has a 0% chance of being thrown. That rarely occurs. About the only time that occurs is on 3-0 counts where a fastball might be the only pitch thrown (for example, 3-0 count with a 5 run lead, or even a 3-1 or 2-0 with a big lead late in the game).
Now that being said, let’s say that Lackey is supposed to throw his cutter away only 5% of the time there. If we observe only that one pitch and it is a cutter, Bayes tells is that there is an inference that Lackey was intending to throw that pitch MORE than 5% of the time and we can indeed say with some small level of certainty that he “threw the wrong pitch.” We don’t really mean he “threw the wrong pitch.” We mean that we think (with some low certainty) he had the wrong frequency matrix in his head to some significant degree (maybe he intended to throw that pitch 10% or 20% rather than 5%).*
* Technically, we can only say something like, “We are 10% sure he was thinking 5%, 12% sure he was thinking 7%, 13% sure he was thinking 8%, etc.” – numbers for illustration purposes only.
Weird, I was just thinking the same thing
LOL
One major quibble though – “random noise” is redundant; you missed an opportunity to significantly shorten the article there
I’m looking forward to the identical article tomorrow about how Harris shook off Gattis calling for a curveball 3 times until he got a fastball that Judge promptly hit out of the park.
It’s like the guy lit the scouting report on fire and just said F*** IT!
How many games do the Cubs have to win for this to not be the last pitch Lackey throws as a professional?
Why would Lackey be out? Innings are valuable and just one year ago he was good. Any team could use him at the back of their rotation.
Isn’t any analysis of performance vs sinkers really dependent on pitch classification of sinkers? Sinkers seem like the kind of pitch that are interchangeable with a 2-seamer from many pitchers – good ones are called sinkers. I am sure that some FB are classified as sinkers, and visa versa. In other words, sinker is a pretty arbitrary classification of a pitch, which makes me really skeptical of any meaningful analysis. If it was a change-up or even better, breaking balls, then that would be the kind of thing that I would trust more. In any case, a good year is going to coincide with some improved splits. How does he compare with 2014? That was his other career year. Its not like this performance cropped up out of nowhere. 2016 is his worst year as a Dodger and not really a baseline performance. There is also the reality of this season, where he was an MVP front-runner in the first half, but not the second. To be clear, I have no answers, only questions.