For the last twenty years, we've been working with the Run Expectancy by the 12 ball-strike counts (RE12). I posted such a chart back in 2017 here.
Those numbers have not changed much since. Comparing to 2020-present, and every number there is within .01 runs. So, we'll just go ahead and use those numbers for this discussion.
The value of a first-pitch called ball is simply the difference between the run value at 0-0 (.000 runs naturally) and 1-0 (.040 runs). In other words, a first-pitch called ball is worth +.040 runs.
On the other hand, the value of a called ball on a 3-2 count is the difference between the run value of a walk (.330 runs) and the 3-2 count (.056) or +.274 runs.
In other words, not all called balls are worth the same. Which of course we all intuitively know. All we're doing here is quantifying this effect.
Now, we know, for that play, that not all called balls are worth the same. But, from the perspective of the TALENT of the pitcher, is that true? Do we really want to give each called ball a different run value?
There are in fact (at least) four different approaches that the pitcher follows. And when I show you that breakdown, you will immediately understand. You can of course argue for 12, and I'd agree, but that makes the analysis much more difficult.
First, there is the 3-2 approach. This is a pretty special case because it all comes to a head. It's down to one pitch. A called ball we saw above is huge (+.274 runs). A called strike is just as huge (-.326 runs). This is a very very high leverage count, with no margin of error, for the pitcher or the batter.
Then we have the X-2 approach (0-2, 1-2, 2-2). In this case, the run value of the strike far exceeds the run value of the ball. Which of course you could have guessed, since a called or swinging strike means a strikeout, while a ball still keeps the at bat alive. A pitcher will be tempted to see if a batter will chase because the downside of a called ball is so low compared to the swinging strike.
Then we have the 2-X, 3-X approach (2-0, 2-1, 3-0, 3-1). In this case, the value of the called ball is 1.5X to 2.5X that of the value of the called strike. So in this case, the pitcher is really not counting on a batter chasing and so will be targetting the strike zone. He just can't afford a called ball here.
Finally, we have the remaining counts (0-0, 0-1, 1-0, 1-1), where the run values of a called ball or strike is fairly muted, as well fairly even. In other words, at these counts, the pitcher has a much higher margin of error, and so has a more individualized approach.
I would make an exception for the 0-0 (first-pitch) as a special fifth case. In addition to the low run value for a called ball and strike, batters also swing the least in this count, compared to all other counts except 3-0. So, at 0-0, the pitcher really has little to fear, as a batter is really motivated to not swing.
What I will be doing is tracking the called ball rate in these five groups for each pitcher, and seeing how well it correlated to next season's walk rate. Ideally, what we'll see is that a pitcher having a low or high called ball rate in the X-2 approach has little impact to his walk rate. And that his called ball rate on 3-2 as well as 2-X/3-X counts is what will drive his walk rate.
We'll have of course have to take care of the frequency in which he finds himself in a high-ball count to begin with.
Players are humans. That is not only a point of fact, it is a point of fact that models should consider.
In hockey, players used to have much longer shifts than they do now. In 1925, the Montreal Canadiens played their 30 game schedule with 8-9 skaters per game and 1 goalie total. At some point, roster size doubled to 18 skaters and 2 goalies. Why? Because it's hard playing 60 minutes with that few number of players. Length of shifts on the ice naturally plummeted with the increase in the number of skaters, from well over a minute to barely over 30 seconds today. Why? Because skating without a rest is hard. When we were kids we were told to NOT drink water while playing! Humans. Players are humans. They get tired. They need rest. They need activity. They need a routine.
PINCH HITTER
The pinch hitter is one of the hardest jobs in the sports world. There is no real pattern for preparation. They get one shot in some indeterminate point in the game. If they even get that shot. How tough do they have it?
We can compare how players (humans I should say) perform as a pinch hitter, and how they perform when they are in the starting lineup. Since 1969, their wOBA is 25 points below as a PH compared to themselves in the starting lineup. This is tremendously (negatively) impactful. wOBA of 25 points is .020 runs per plate appearance (PA). With the average number of runs per plate appearance of .11, PH therefore generate almost 20% fewer runs than they would in the starting lineup. And the only thing these humans are doing differently is coming into the game as a PH.
DESIGNATED HITTER
A DH is almost like a PH, except they come to bat about four times a game. Some DH are professional DH, like David Ortiz or Edgar Martinez. Other players are DH on a rare occasion. So, again, humans. Preparation. I broke up all DH into three groups:
Experienced
Typical
Inexperienced
The determination for an Experienced DH is that they qualified under any one of these three categories:
300+ PA in that season
200+ PA in that season, plus 50%+ of their PA over a three season span as a DH
100+ PA in that season, plus 66.7%+ of their PA over a three season span as a DH
The three-season span includes the year before and the year after the season in question
An Inexperienced DH had fewer than 100 PA in that season, and less than 33.3% of their PA over a three season span as a DH.
Every other DH is considered Typical.
When we do that, what do we see?
The Inexperienced DH is a tad more effective than a PH, at .018 runs per PA worse than themselves as a starting player.
The Experienced DH was the most effective of all these part-time players, at .009 runs per PA worse than themselves as a starting player.
The Typical DH was halfway between these two at .014 runs per PA worse than themselves as a starting player.
So, not only have we shown that players are humans, but we've also been able to see a logical progression based on their day to day usage.
Creating a scale here, we can use this as a rule of thumb, per 700 PA:
-7.5 runs, Experienced DH
-10.0 runs, Typical DH
-12.5 runs, Inexperienced DH
-15.0 runs, Pinch Hitter
ASPIRING SABERIST
The next step is for the Aspring Saberist to better define our DH, perhaps a continuous sliding scale from 0% to 100% on the Experience scale rather than these three groups. And probably the 0% Experienced would match the PH adjustment of -15 runs and the 100% Experienced DH would need only a -5 run adjustment. Something like that.
I also looked at defensive subs, and how they did as batters. The results were inconclusive. Over one stretch of years, the adjustment was -.006 runs per PA, or almost -5 runs per 700 PA, pretty close to what I'd have expected given the above findings. However, over the most recent years, 2018-2024, the adjustment was a POSITIVE .006 runs per PA, meaning that it is easier to bat after coming onto the field in the middle of the game. So, certainly more work needs to be done here.
My favourite line from Moneyball is Michael Lewis describing Bill James: he prefers to leave an honest mess than a tidy lie. That is pretty much how I operate as well. What we do here is as much art as science. Trying to be totally scientific about it would essentially mean we'd rarely come to conclusions because we'd have huge uncertainty about so many variables (or, we'd lie and assume we have no other variables to consider and so have very little uncertainty with the variables we did consider). Forty years ago, Bill James said: I can't do all this by myself. Back then, he probably represented half of all the saber work being done in the public. Now, there's hundreds of us. But we can always use more dedicated, passionate folks out there. This particular topic has plenty of meat on the bones.
UPDATE: Also, the Inexperienced DH group should be split between whether they may be injured or otherwise not feeling good enough to be part of the starting lineup. While we're at it: the PH could be given similar treatment, splitting between the occasional PH (Kirk Gibson) and the ready-to-go PH (Rusty Staub) and everyone in-between.
A walk is as good as a hit, is essentially a true statement when the bases are empty. Which has been true for most of baseball history (with the exception being the extra inning placed runner, the XIPR).
In a Markov chain, the presumption is how you entered a state is immaterial. Being in a state is the information you need in order to know what's to come. So, if you have a runner on 1B with 0 outs, does it matter HOW you got there? If it doesn't, then that's your Markov state: runner on 1B, 0 outs. If it DOES matter, then your Markov state has to include how you go there, so that your actual Markov state is 1B-or-BB-or-HBP-or-Err, and the runner on 1B and 0 outs.
In an award-winning presentation at SABR52, Bailey Hall tackled that issue. The main overall point is that the number of runs that followed the runner on 1B, 0 outs state was essentially the same, regardless as to how the state was entered (0.94 to 0.93 runs following a leadoff BB or single respectively). But, Bailey did note that there may be a pitcher-by-pitcher effect, that maybe some pitchers are more affected by one or the other, and maybe even at the inning-level.
Most important to all this is that the question was asked, a solution has been offered, and the presentation is beyond outstanding (with pure baseball themes wherever you look). This is what an #AspiringSaberist should do: ask the question, roll up their sleeves, and show off the work. Because others will be watching, and they will remember any good work.
I have to write one of these blog posts every year because folks are so disbelieving. And it's not just my research. I MUCH prefer when others do this research so that there's no conflict of interest.
I'll lay out my method, and you can feel free to reproduce it however you can. There's some data you may not necessarily have, but you'd be able to estimate it.
Anyway, here we go. Again.
First the data: 2016 to present (thru Jun 4, 2024), regular season and playoffs. Only hit-into-play. We want actual wOBA and xwOBA. Minimum 500 batted balls for each batter over the entire time period. This gives me 593 batters. Hopefully you get something pretty close to that.
Next, we create a spray tendency for each batter. In the past, I would just take all their battedballs to create their spray tendency. But, inspired by the point Ben Clemens recently made (who was studying a similar issue), this time I've focused on batted balls with a launch angle of 4 to 36 degrees, for balls hit 200+ feet. This is basically line drives and flyballs, and the type of batted balls that folks talk about when they talk about pull hitters and spray hitters.
But, just for completeness, I'll also do it my usual way of looking at all batted balls to establish the spray tendency. I'll do that at the end. For now, we'll follow the Clemens-inspired approach.
For the 593 batters, I take the 10% most extreme pull hitters. There's 59 of them. Their spray tendency is a pull of 9.5 degrees. Then I take the 20% next most extreme pull hitters. That's 119 batters with a spray tendency of -7.0 degrees.
I take the 10% most extreme spray hitters. There's also 59 of them, with a spray tendency of +1.6 degrees. The next 20% are at -1.6 degrees. Finally, the middle 40%, 237 batters, have a spray tendency of -4.3 degrees.
Next, for each group, we look at their actual wOBA and their xwOBA. Now remember, the xwOBA does NOT look at a batter's spray direction, whether at a single play level, or at a player tendency level. It is simply ignored. So, if we find that there is a difference between actual wOBA and xwOBA then this is evidence that the spray variable needs to be added to the model. If they are a match, then we don't need it (or at least, we haven't found any evidence with this method that it is needed).
What do we find with the most extreme pull hitters, those at -9.5 degrees of spray? Actual wOBA of .386, xwOBA of .385. How about going the other way, the most extreme spray batters, those at +1.6 degrees of spray? They have a .362 actual wOBA... and .362 xwOBA. Identical.
How about the rest of the three bins? Bin 2 is .379 actual and .379 xwOBA. Identical. Middle bin is .370 actual and .370 xwOBA. Identical. Bin 4 is .363 actual and .365 xwOBA.
So... yeah... we don't need to consider the spray tendency of the batter to model their effectiveness.
***
I said I would rerun everything doing it my usual way of using all batted balls to establish spray tendency. The results are almost as boring, but I'll lay it out, from bin 1 (most pull tendency) to bin 5 (most spray tendency). Actual wOBA first, xwOBA second, difference third. Ready?
-12 degrees, .382, .385, -.002 (rounding)
-10 degrees, .372, .371, +.001
-8 degrees, .378, .379, -.001
-6 degrees, .364, .363, +.001
-3 degrees, .348, .345, +.003
So... yeah... as it turns out, it doesn't really matter how I establish the spray tendency. We just get similar conclusions.
***
Now... How? HOW? HOW is it possible to ignore spray tendency and still be able to get the player wOBA to match to their xwOBA? Simply put: opposing teams know the pull/spray tendency of the batters and position their fielders accordingly. How about the HR? Well, that's true, but if you miss the HR, guess what, there's an outfielder who was positioned close by to turn that almost-HR into an out.
The reality that we found in 2016, when we had so very little data, such limited data, that allowed us to ignore the spray variable is being upheld with tons of more data. And this conclusion has been reinforced by other researchers who also found the same thing.
Long story short: while you need the spray angle to describe the PLAY, you do NOT need the spray angle to describe the (effectiveness of the) player. You can use the spray angle to show the PROFILE of the player, but it won't alter our opinion as to their overall performance.
I'll see you again in six months, where I'll do similar research in different ways. Again.
According to Baseball Savant, Salvador Perez has been minus 89 runs in Catcher Framing since 2016. Fangraphs has him at a similar -80 runs. Baseball Prospectus at -82 runs. DRS has a more compressed scale, still with Salvador as 2nd worst at -46 runs (compared to the low of -48 runs).
So, it is clear, Salvador Perez is a disaster at Pitch Presentation. And his other skills, his throwing (+17 runs) and blocking (+1 run) just can't compensate for that. Overall, he's a big net negative at -72 runs. But, is that all there is to being a catcher? Surely there's more to it. The calling of the pitches, the confidence the pitcher has.
See, when we look at Pitch Presentation, that's basically a segment of the pitch. Everything else that goes into it is not considered. Maybe he is so good at everything else about being a catcher that it not only overcomes such a huge deficit, but he may in fact even be a net positive. Is that possible?
Well, let me give you a number to blow your mind. Since 2016, the Royals with Salvador Perez have given up 3079 runs while making 17,369 outs. That is a rate of 4.79 runs allowed over the equivalent of 643 9-inning games (or just about exactly 4.0 full 162 game seasons). The Royals without Salvador as catcher have played the equivalent of 3.6 full seasons, while allowing 5.28 runs per 27 outs.
In other words, the Royals, with Salvador, have given up nearly 0.5 fewer runs per 9 innings. And since we just said he played the equivalent of 643 9-inning games, that's 316 fewer runs allowed with Salvador. This stands in STARK contrast to the 72 MORE runs that Salvador allows when we consider Framing, Throwing, and Blocking. We have a 388 run gap here to bridge.
Now, you may be saying: is Salvador catching with the better pitchers maybe? Well, that's the question I ask. As you know, I pioneered the WOWY method (With or Without You), which really at its heart is a simplified mixed-effects model. WOWY has the advantage of being completely transparent, easy to explain, and with a great theme song.
Let's start with Danny Duffy. With Duffy and Salvador, the Royals gave up 152 runs on 1222 outs. With Duffy and without Salvador, the Royals gave up 178 runs on 965 outs. So, that's more runs allowed and fewer outs. This is a feather for Salvador. Pro-rating the 178 runs on 965 outs to the 1222 outs that Salvador caught, that gives us a weighted runs allowed of 225. As you can see, Duffy loves Salvador: he gave up 152 runs instead of the expected 225 runs, or 71 fewer runs.
However, repeating this process for the next most popular pitcher, Ian Kennedy, he gave up 27 more runs with Salvador than without Salvador. Brad Keller also gave up more, at 13 more runs. As did Brady Singer at 46 more runs. And Jakob Janis at 20 more runs.
The Duffy pitchers, those what gave up fewer runs with Salvador gave up a total of 721 fewer runs. The Singer/Kennedy pitchers, those that gave up more runs with Salvador than without gave up 417 more runs. Add it up, and Salvador still allowed 305 fewer runs. This is after controlling for the quality of pitchers.
Remember, we had him at 316 fewer runs allowed without any controls at all. With this level of control, the identity of the pitcher, all we did was reduce that to 305 fewer runs allowed.
Can we do more here? Yeah, we can look at it year by year. Maybe we can focus only on strikeouts and walks, or at least separate the K, BB numbers from the other numbers. We can do alot.
But, the main point here is to say that Pitch Presentation, as real as it is, and as large of an effect as it has, still may pale in comparison to everything else that a catcher does beyond Throwing and Blocking. There's Game Calling. And maybe even just an overarching skill that we can call it Presence if we want.
Whatever it is, it's important that focusing on a very specific skill in a very myopic way doesn't keep us from looking at the overall impact of the catcher.
Home win% in regulation games is 54%, but falls to 52% in extra innings for totally normal reasons, having nothing at all to do with who bats first or last.
This chart shows the win% season by season since 1969. I've included the Random Variation lines, which I have nominally set at 2 standard deviations. This implies we should expect to see 5% of these 55 data points (aka 3) to land outside these two lines. We see alot more than that. Why, I don't know. I used a flat 52% expected win%, and maybe it should be 51.5% one year and 52.5% another year. I'll leave that to the aspiring saberists. (click to embiggen)
Of course, the most striking thing in the chart is what's happened since 2020, with the extra inning placed runner (XIPR). Though inconveniently, the pattern started the season prior to that. Anyway, here's how it looks when we group in chunks of five seasons. The data point for "2010" you see at the bottom refers to seasons "2010 - 2014". And "2015" is "2015 - 2019".
We should only see, maybe, one point outside the 2SD lines, but we see three, with the one from 2020-present way outside even this standard. Something is definitely going on with how teams are approaching playing with the XIPR. I'm sure an aspiring saberist can look into this. Are there any teams that have figured it out? I'll leave that up to y'all to show.
I don't know. But to help us get us there, we can look at how often a batter has a full swing, at each plate location and ball-strike count (click to embiggen). The first set of numbers is the league average. I (for now anyway) define a full swing as follows: take a batter's 50% fastest swings, take that average, subtract 10 mph, and that's the minimum threshold of swing speed for a full swing. Anything below that is an abbreviated swing. League average is about 10%.
The second set of numbers is Spencer Torkelson, who at 95% of his swing as full swings is among the league leaders. That he is also among league leaders in strikeouts is not a coincidence. The last set of numbers is Jose Altuve, who at 80% of his swings as full swings is among the league lows. That he is among the league-lows in strikeouts is also not a coincidence. Also note that he reserves his abbreviated swings especially in 2-strike counts, to a much larger degree than league average.
Look at this chart. You will notice that batters, when a pitch is in The Heart of the Plate, have the slowest swing speed at 0-2 counts (70.1 mph) and fastest swing speed at 3-0 counts (74.4 mph). Indeed, at EVERY count, the more balls, the higher the speed, and the more strikes the lower the speed. Roughly speaking, every ball, the speed increases by 0.5 mph, and every strike, the speed decreases by 1 mph. That's for The Heart of the Plate.
This directional progression (though not the same magnitude) is maintained when the pitch is in The Shadow Zone as well as the Chase Region. It's only in the Waste Region where the ball-strike count does not matter.
While this progression makes sense in the Heart of the Plate, it makes no sense in the Chase Region. At this point, the pitch is at least a few inches off the plate. At a 3-0 count, there's no (good) reason for the swing speed to be at 71.9 mph, while it is 64.2 mph at 0-2. This is a good sign that the batter is being overly aggressive at 3-0 in the Chase Region.
We can learn more by looking at the Run Values by location and count. Focus on the Swing columns, and start with Heart of the Plate. Swinging at 0-2 is providing far more benefit than swinging at 3-0, when the pitch is the Heart of the Plate. Even though the batter is swinging less hard. Indeed, if you follow the progression, it is almost a complete reverse of the speed progression: the more strikes, the better the batter is doing on swings, while the more balls, the worse the batter is doing.
My initial guess is that swinging at 0-2 at a pitch in the Heart of the Plate has the batter with a more defensive swing, hence the lower speed. And at 3-0, the batter is more aggressive, not worrying about any swing-and-miss, since the worst case is getting them at 3-1. However, overall, this is not working out.
Naturally, not all batters are going to behave the same way. I am sure if we look at the best and smartest batters, like Juan Soto and Luis Arraez for example, we'll likely learn what the more optimal approach should be.
What I'd like to learn is if this batting approach ability is something that can be taught, or is it something that pitchers will exploit in a batter early on, and thereby doom that batter to a shorter career. So much to learn...
In a recent article, we learned that there's been some movement with sliders in terms of their efficacy and usage.
It is important when establishing the efficacy of a pitch that we look at ALL pitches, and not just the PA-ending pitch: if you only look at the last pitch of a plate appearance, you are subjecting your analysis to unnecessary selection bias. After all, you don't know it's the last pitch until AFTER the pitch is thrown and results are known. You can't select data in that manner.
Here is the run value of all pitches thrown since 2014 (click to embiggen). Remember, negative is good for pitchers and positive is good for batters.
Here we see that the slider has been an enormously effective pitch thru 2020, and it is still the most effective pitch thru to the present. That it is very effective is not a surprise, as this is something that I've brought up repeatedly on this blog over the years. And I noted that when that happens, it means that pitchers are under-throwing it. You can't leave a weapon that powerful at low usage.
So, what happened over the years? Well, exactly what we expected: sliders are being used more and more.
Given that the slider is still the most effective pitch, we should expect to see its usage continue to grow. I am including all types of sliders here (sweepers and slurves, along with the traditional gyro-slider).
We can also see that the increased usage in sliders has come mostly at the drop in usage of sinkers. Why sinkers? Well, theoretically, it's because they must have been ineffective and so in order to improve its efficacy, it has to be thrown less (aka, more of a surprise pitch). And if you look at the first chart, that's exactly what has happened. Thru to 2019, the sinker was the least effective pitch. We have gotten to the point that the sinker is now the 2nd most effective pitch, which is quite the turnaround.
So, we should expect the sinkers drop in usage to stop, and likely rise a little bit more.
What's the next pitch that will take the hit? It's the changeup (includes splitter, forkball, screwball). Since 2021, it's been the least effective pitch each and every year. So, expect a small drop in usage in the coming years.
The cutter is what you would expect: constant increase in usage from 2017 to 2023, each and every year. And the run value of the cutter has seen an almost constant rise. We've pretty much reached the point of equilibrium, and so the cutter is no longer a pitch that pitchers will continue to chase.
A small note: this is at the league level, and we really should do it at the pitcher level. If for example only good pitchers throw sliders, then we should be comparing their sliders to their own fastballs, and not all fastballs of all pitchers. In addition, we should better control for the plate count: at 3-0, you are only throwing fastballs for example. Whatever happens there is not really relevant for comparing to a slider.
If/when the ABS-challenge system comes to MLB, a new skill will be sought after: the challenge skill. If I were manager, I'd likely reserve the right to challenge to a select few batters on each team (at least through 7 or 8 innings), like Juan Soto and other genius batters who know the strike zone better than most.
If you are allowed 3 wrong challenges, players like Juan Soto should be given free reign to make those challenges, and not have the rest of his team "foul out", thereby preventing him from challenging.
We SHOULD be getting something like 70 or 80% reverse rate through seven or eight innings, before getting 30 or 40% reverse rates (use em or lose em) in the last inning or two. While the minor leagues likely has under 50% overturn rate, I'd expect the overturn rate to be over 60%, if not 70% among the better MLB clubs. We'll see how it goes if/when ABS-challenge comes.
I have previously shown that fielders that come into the middle of the game as fielding subs do in fact suffer in performance at the plate, just as DH suffer at the plate, and especially PH. This is the "cold effect", coming cold into the game. The PH are going straight from the bench to the plate, so have a "double cold" effect. The fielding subs have the advantage of first taking the field after being stuck on the bench for hours, so they have a "single cold" effect. DH end up like fielding subs, that spend so much time sitting on the bench, before batting 4 or 5 times a game. Catchers also are impacted, but in their case, it's not a "cold" effect, but rather a "burden" effect.
So, Jay asks what happens when your starting fielder stays on the field a long time (high defensive workload), and then comes to bat. And the conclusion is that, yes indeed, being on the field a long time (and/or getting alot of action on the field) has an effect at bat. Whether we want to surmise this to be a "cold" effect, or "exhausted" effect, I'm not sure we can say yet. But the impact is 10 wOBA points, which is in-line with the "cold" effects noted above.
Basically, when you start the premise that players are human, you will in fact find patterns consistent with human frailties. We can now add another one to the list.
This research certainly should be able to spawn more research along this line for the Aspiring Saberists out there.
I asked the Straight Arrow followers which they prefer to value more for a player in their personal uber-metric. And on a roughly 2:1 split, they prefer the solo HR be valued more. I wasn't surprised by the overall results, but the folks leaving comments had alot of confusion. Let me try to clear some of that up.
Let's talk about the solo HR first. We have bases empty before the batter shows up. The batter hits a HR, and scores a run. The bases are still empty for the next batter. In this case, it's unequivocal: the HR generated exactly 1 run.
Now, with the bases loaded walk, we have, well, the bases loaded to start with. This is what the batter has been GIVEN. The batter didn't earn those three runners. The batter just happens to be there for them. The batter draws a walk. A run scores. And the next batter sees the exact same situation as the batter who got the walk: the bases are loaded. So, in terms of the RUN IMPACT, the bases loaded walk generated exactly 1 run, and this is unequivocal. That there happens to be 3 runners on base (including the batter) after the walk is irrelevant, since there were 3 runners on base before the walk.
So, if you are confused on this point, you need to reread all of this. The run impact of the solo HR and the bases loaded walk are identical: exactly one run was generated.
Now, the question is how to value the PLAYER for each of these two events. A random HR is worth +1.4 runs, because sometimes there's runners on base, and sometimes there are not. On average, it's +1.4 runs. This PARTICULAR HR, the solo HR is worth exactly +1 run. This is how you can see the breakdown:
+1.4 runs for hitting a random HR
-0.4 runs for hitting a HR with the bases empty
So, combined, that's +1 run. The batter, unfortunately, happened to cash his chips early, and instead of waiting (though naturally, it doesn't work like that) for a runner, he decided to play his HR card with the bases empty. One run was the result, not 1.4. Do we credit the batter with +1.4 runs, and -0.4 runs to the rest of the team for not having the foresight to send a runner on base? Imagine if you will, it was the leadoff HR of the game. Then what? Do you still want to give the batter credit for +1.4 runs for a HR, and somehow give out -0.4 runs to... who... the manager for putting Rickey Henderson or Mookie Betts as his leadoff batter? Or, do we just measure the player exactly in context:
+1 run for hitting a HR with the bases empty
Well, you tell me.
Now, let's talk about that walk. The bases are loaded. The batter did nothing to earn that. He gets a walk. The batter totally earns that. A run scores as a DIRECT RESULT of the walk. Who earns that? And after the walk, the bases are still loaded. From a purely causative agent, the batter directly generated exactly 1 run. The bases were loaded before the batter got the walk, the bases are still loaded after the batter got the walk. The DIFFERENCE is simply this: one walk was added, and one run scored. If this was a RANDOM walk, it would add around +0.3 runs, because most of the time, the walk has no runner on first base, and so, it's really just about the batter getting on base. So, you can see it this way if you like:
+0.3 runs for getting a random walk
+0.7 runs getting a walk while the bases are loaded
So, that's +1 run. The batter waited to play his walk-card while the bases are loaded (not that it works like that). And 1 run was the result, not 0.3 runs. Do we credit the batter with +0.3 runs and the rest of the team +0.7 runs for having the foresight to get on base for that batter knowing the batter would walk? Or do we just measure the batter in context:
+1 run for drawing a walk with the bases loaded
Well, you tell me.
***
I will say this: if you measure a batter based on the ball-strike count, then you would surely treat what he does with a 3-0 pitch differently from an 0-2 pitch. The batters and pitchers are responding to the count, and they are changing their approach on the count. This is ridiculously obvious for any baseball fan to realize. Batters will rarely swing on 3-0, while they are very aggressive on 0-2. Now, naturally, it's the batter/pitcher interaction that puts them into those counts. So, looking at it from the plate appearance level, it's irrelevant if we end up with a 3-0 single or a 0-2 single. A single is a single. But if you are trying to measure each pitch in isolation, then each pitch is going to have a value dependent on the count.
When you have a runner on 1B, the pitcher will naturally pitch differently. They know that 3-0 pitch is much different in impact if there's a runner on 1B or not. And similarly, with a runner on 3B and less than 2 outs, both batter and pitcher will treat the entire plate appearance differently. The pitcher really wants a strikeout, while a batter just wants to make contact. And so, their pitch by pitch approach is dependent on the base-out situation. We can't just treat a long fly out the same, whether there's a runner on 3B and less than 2 outs or not. The entire plate appearance, every pitch, was predicated on knowing the base-out situation. The batter and pitcher both interacted exactly because of that. We can't then just look at it from a high level and say: well, that's a long fly out, just like any other long fly out. It's just an out.
That's not how baseball actually works. And when you create a model, you are trying to represent reality. And the reality is that players are humans and that has to be our starting point. And humans respond to stimulus, they change their behaviour. And a long fly out, with a runner on 3B and less than two outs, matters differently from a bases empty long fly out.
You can of course go all the way here: consider the score is tied in the bottom of the 9th, with a runner on 3B. In THAT scenario, a team might even bring in one of their outfielders. And whether the batter hits a single, double, triple, or even a HR: it makes no difference. A hit wins the game. Or imagine the bases are loaded. In this case, a walk and a HR have equal value: the runner on 3B touching home plate is what makes the team win. And this is where I lose some folks. And I lose them to the point that it unravels the entire thing, and suddenly, we are treating a bases loaded walk as if it was a random walk, and a solo HR as if sometimes there was a runner on base.
A few years ago, noted baseball thinker Meg Rowley eloquently presented the case on national TV: it all depends on what aesthetic of baseball you want.
Ok, so this is what I did. I looked at every LHH since 2020, splitting their performance between LHP and RHP. I put their 2020-22 performance in the "previous" bucket, and their 2023 performance in the "current" bucket. In order to properly weight everything, we weight each batter-pitchHand by the Harmonic Mean of the PA of the two buckets. That just means I keep things properly proportioned. Anyway, so the previous wOBA was .321. And the current wOBA is... .321! Yes, after all the massive change in defensive alignments, we simply get back to where we always were. It's just that now, well, it's about the aesthetics. It wasn't about getting more offense or less offense. It was a (pardon the pun) shift in aesthetics. Or a restoration anyway.
As for RHH, well, as I've pointed out since 2017, shifting against RHH was always wild. (Though in 2022, clubs finally figured it out.) RHH simply feasted on shifted alignments. And so, preventing shifts would actually hurt RHH. And, did it? Yes, it did. We went from .331 previous wOBA to .321 current wOBA. In this case, the rules actually saved the clubs from their inefficiency.
We always had two camps. One focused on the Inertial Rules, and the other focused on the Inertial Aesthetics. When you watch baseball, what exactly brings you back in time to your childhood? Is it the rulebook, or is it the playing style? What's your inertia? Mine has always been for the rules to serve the playing style and not for the playing style to be at the mercy of the rules. In the end, entertainment is what will always win out. Fun. And this is fun.
How many fewer wins would the Angels have if they maintained a 26-player roster, but every other team got a 27-player roster?
Maybe in a handful of games would that player have any kind of value?
Knowing that a star player adds about 0.010 wins per PA, and an average player adds 0.003 wins per PA and that this 27th player is below average, what are we guessing? 0.001 wins per PA? Add it up: Rounding Error.
In The Book, we learned that the wOBA of a pitcher as a starter is some 25 to 30 points higher than that same pitcher was getting as a reliever. I used 1999-2002 data. How does it look historically? Here it is from 1974 to present. As you can see, it's basically the same throughout. There's a natural advantage a pitcher has knowing he's coming in as a reliever instead of as a starter, likely due to not needing to pace themselves. (click to embiggen)
There are 78 RHH who have been shifted at least 100 times since 2016, and whose wOBA with an without the shift has a gap of at least 20 points in some direction. Coincidentally, there are 78 LHH in the same boat.
52 of these 78 LHH lower their wOBA by 20+ points with the shift. And therefore, when a team calls a shift on these 52 players, that’s a “good” call. The other 26 players increase their wOBA by 20+ points with the shift. Therefore, it’s a “bad” call when a team shifts on those players.
In contrast, only 17 RHH lower their wOBA by 20+ points on the shift. It’s a “good” call when a team shifts these 17 RHH players. The other 61 RHH increase their wOBA by 20+ points with the shift. So, it’s a “bad” when a team shifts on these 61 RHH.
All I’ll do here is tally the results. So here are the totals of the good call rate by team, by bat-side. You will notice that 28 of the 30 teams have a good call rate on LHH above 50% of the time. Even the two teams with a good call rate below 50% were very close. In other words, among those batters that they are shift-happy on, they are choosing wisely.
The best team with a good call rate on RHH is 37%, the A’s. In other words, had the league decided to not call a shift on any of these RHH, even those “deserving” of a shift, it would be a gain. In other words, among those batters that the league has targeted for shifting, none of them are making a positive choice.
When I first started this, it was in response to the Brushback or Purpose pitch: A pitcher has an 0-2 count, throws a fastball in (high and tight). The question was does this make sense to waste a pitch? Intentionally getting yourself a called ball here (costing you .025 runs) is a big dice roll on the idea that you can get into the batter's head.
In doing that research, I decided to set that aside, and focus on something more generic: WHERE does the 1-2 pitch go under the above condition (of having an 0-2 count and getting a called ball on a fastball). Game theory would suggest that the batter can't guess. Well, he can.
Here's what I did. I limited the data to RHP v RHH. First, I figured for each 0-2 called ball fastball, how often the next pitch (the 1-2 pitch) went into one of the 33 regions. (Click here for a reminder of what those 33 regions look like.) Second, I needed a baseline, so I figured how often a pitcher has a tendency to throw a 1-2 pitch to each of the 33 regions. This is the "expected next" pitch. Finally, I compared the actual next pitch of each region for each pitcher to that pitcher's expected next pitch. That difference is converted to a z-score and we get this (click to embiggen):
That MAY look random. But it is not. The standard deviation of the z-scores, if it were random, would come back with 1.000. This one comes back with almost 1.25. When dealing with small samples, I start to get interested when I get to 1.10. I'm excited when I get to 1.40 or higher. At 1.25, I'm intrigued that something exciting can be there.
Because we're dealing with several hundred pitchers, the idea that there's some common pattern would seem mostly ludicrous. Therefore, getting a 1.25 z-score would imply that some pitchers are indeed random, and therefore, those pitchers are the noise that are masking that tremendous signal that the remaining pitchers truly are not randomizing their locations.
It can also be that I didn't control for batters (other than they are RHH). And maybe that's enough to explain these findings. But, I am doubt that it can explain anything close to what I am seeing.
In the comments, I'll go through a specific example so you can see what it is that I did, so you can replicate and extend this research. And who knows, maybe in a couple of weeks I'll show you the full SQL code so you can have fun learning that too.
?An excellent article on the topic. I'll pluck out a few talking points from the article, but there are obviously alot more things to consider:
Point 1: Defining the zone
“We feel fairly confident that we have a system that works well,” says MLB senior vice president Morgan Sword. “The emerging question is more of a baseball question and that is: What do you want the strike zone to be? We’ve noticed through experience that the rulebook strike zone, which is what’s called by ABS, is different from the strike zone everybody knows and loves. We’re going to have to think hard about how to manage the transition between a human zone and an automated zone because they’re not the same.”
Point 2: Impact of a fixed zone by plate-count
In practice, the width is sometimes extended an inch or two off both corners of the plate; the literal top of the strike zone is not regularly called a strike; and the zone can fluctuate depending on the count, with 3-0 pitches more often a strike and 0-2 pitches more often a ball.
Point 3: How to set the zone by batter
“We know that batters are extremely consistent in their stances, pitch to pitch and at bat to at bat, because we measure,” Sword says. "So you really don’t lose much accuracy by using some kind of lagging average of where the top and bottom was, but it's another policy question for the baseball people.”
?In the 2020 Bill James Handbook, Bill details 30 things that he would implement to improve the aesthetics of baseball. I won't list out all 30. Anything that is unique to Bill, we'll keep it unique to Bill, and you can read it in his book.
I will list any of those that we've previously advocated or otherwise discussed. There's probably a dozen or so. What I'll do is update this thread as time allows, and I'll reiterate the prior view of the Straight Arrow readers and whatever we discussed on this blog or my previous one, with maybe some links. (I'll update the comments section so you know when I've made an update to the thread.)
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