## Fixing how wins are awarded in baseball

If it drives you bananas when a starting pitcher throws eight brilliant shutout innings and leaves with the lead but fails to get the win, then you’ll want to read this article.

In 2002 I found myself deciding to complain to Major League Baseball about the unintelligent way in which wins are awarded to pitchers. A way that often awards the win to the least deserving pitcher. But then I realized that would make me someone I don’t like – a complainer without a viable alternative to offer. So I resolved to come up with a viable alternative. But I couldn’t.

Then five years later, I could. And now, finally, eleven years after that, I offer you the merit win.

I’d rather it not be known as the “merit win”, but rather simply the new way of awarding wins, but until it becomes adopted as such, we’ll need a name to distinguish it from the current way. So, “merit wins”.

A key was to figure out how to give credit for innings pitched. Pitching 7 innings and giving up 1 run is probably more valuable than pitching 1 inning and giving up 0 runs. So I had to figure a way to give the correct amount of credit for those innings pitched.

The winning idea was that for each inning a pitcher throws, they get credit for the number of runs their own team scored per inning in that game. So if your team scored 4 runs and did not bat in the bottom of the ninth inning because they’d won the game, then they scored (4 runs)/(8 innings) = 0.5 runs per inning. Each pitcher is then credited with half a run per inning pitched. From this, the number of runs they allowed is subtracted. This gives the pitcher’s number of “runs ahead”. The pitcher with the highest number of runs ahead on the winning team earns the merit win.

It works for losses too – the pitcher with the lowest number of runs ahead for the losing team earns the merit loss.

One nice result is that you’re pretty much assured that the pitcher that earns a merit win will have a positive number of runs ahead, and the pitcher that earns the merit loss will have a negative number of runs ahead.

### Example of how to calculate merit wins

Here’s a real-world example. On July 6 of this year, Jacob deGrom of the New York Mets threw 8 innings, giving up one run, in a home game against the Tampa Bay Rays, but was awarded no decision, leaving with the game tied 1-1. His teammate Jeurys Familia pitched a scoreless top of the 9th, then earned the win when the Mets hit a grand slam with two outs in the bottom of the ninth.

To determine who earns the merit win, first notice that the Mets scored 5 runs in 8 and 2/3 innings. That works out to 15/26 runs scored per inning, so we award runs to deGrom and Familia for their innings pitched at this rate. Here are their lines:

 deGrom 8 IP, 1 R, 1 ER Familia 1 IP, 0 R, 0 ER

So:

deGrom’s Runs Ahead = 8 IP $\times \frac{15}{26}$ runs/IP – 1 R allowed = ${4\frac{16}{26}}$ – 1 = ${3\frac{8}{13}}$ runs ahead

Familia’s Runs ahead = 1 IP $\times \frac{15}{26}$ runs/IP) – 0 R allowed = ${\frac{15}{26}}$ runs ahead

deGrom has the higher runs ahead total, so deGrom earns the merit win.
There are two reasons why I chose this particular example. One is that it demonstrates one of the many ways in which regular wins are flawed and merit wins are not. The other is that it provides an example of a pitcher who may be denied his just reward – specifically the 2018 NL Cy Young award – because the current way of awarding wins and losses has made his undeserved poor record even worse than it needs to be.

### A flaw of the win – dependence on which team bats first

In our real-world example above, what if the pitching performances, and each team’s turn at bat, had gone exactly as it did, the only difference being that it had been a road game instead of a home game for the Mets? Then deGrom would have left the game at the end of the bottom of the 8th inning, instead of leaving in the middle of the 8th inning as he did. Also, his team’s four-run scoring outburst would have occurred in the top of the 9th inning, instead of in the bottom of the ninth. deGrom would still have been the pitcher of record when his team took that lead for good in the top of the ninth, and because that’s how wins are currently determined, he would have earned the win.

Pitching 8 innings for the home team got him the benefit of only 8 innings of scoring by his team; pitching 8 innings for the visiting team would get him 9 innings of scoring by his team, increasing his odds of earning his team’s eventual win. In fact, no matter when the starting pitcher leaves the game, he always benefits from an extra inning of his team’s offense when pitching on the road. This frequently results in an arbitrary switch of which pitcher earns the win. I consider that a flaw – do you, too?

Merit wins are never arbitrary in this way. They don’t care about the order in which teams bat.

There are a lot of other ways in which the current way of awarding wins is flawed, and merit wins are not. One of the worst is described at the very beginning of this article. I expect to soon post a full description of all of them.

### Justice for Jacob deGrom

The reason I’m finally getting this idea out now is that I hope it can save Jacob deGrom’s chance at winning the 2018 National League Cy Young award. It’s in jeapardy, despite his clearly being the best pitcher in the leage when you look at all statistics other than wins and losses. And the reason it’s in jeapardy is that his win-loss record is only 8-9, which is far from being a Cy-Young-worthy record.

Here is some of the talk about it.

But if you evaluate based on the merit win method, deGrom gains 3 wins he didn’t earn before, while losing none of the 8 he had. He also loses 4 of the losses he had before, while not gaining any of the 10 he didn’t have. Based on merit wins, his record becomes 11-5 – and that, I believe, should be good enough to convince Cy Young voters to vote for him.

(Early in the season, he left three games with a lead after 7 or more innings pitched, only to have the bullpen blow the lead and lose the game. Had it not been for those three blown leads, deGrom’s record would be 11-9 by the conventional method, and 14-5 by the merit win method.)

For merit wins to improve a pitcher’s record by this much over traditional wins is rare. With 3 wins added and 4 losses substracted, the merit win method improves deGrom’s Win-Loss differential by 7 games. In the 2012 season (the only season I’ve fully analyzed), no pitchers had a bigger improvement, only 5 had as much of an improvement, and only 4 improved by 6.

On the whole, in 2012, starting pitchers had 9.1% percent of their wins taken away using the merit wins method, but had new wins added totalling 18.3% of their regular wins total, for a net increase of 9.2% in their wins total. They had 13.6% of their losses taken away, and another 9.9% added for a net decrease of 3.6% in their losses total. So merit wins do tend to improve the records of starting pitchers, yet what is considered a “good record” going by regular wins and losses is likely to also be a good record when going by merit wins and losses; we maybe need to increase our win expectation by one win per starter.

Another look at it: 49.4% of starting pitchers’ decisions in 2012 were wins; 52.5% of their “merit decisions” were merit wins.

### Tiebreakers

In 2012, 9.1% of merit win calculations resulted in a tie, requiring a tiebreaker, and 2.6% of merit loss decisions required a tiebreaker. The tiebreaking procedure is certainly something I’d like to hear some good debate about. What I came up with on my own involves repeating the calculation using earned runs as the first tiebreaker; most innings pitched as the second tiebreaker (reversing this to fewest in the case of evaluating for losses); fewest (most) batters faced after that; fewest (most) baserunners allowed (by hit, walk, or hit by pitch) after that; fewest (most) total bases allowed after that; and finally, the last pitcher to pitch. In the calculations I’ve cited here, I used this tiebreaking procedure with the exception of skipping the total bases allowed criterion, for lack of data on total bases allowed per pitcher.

### One last telling statistic about merit wins

There is a lot of information about the effects of merit wins that I found when crunching the numbers on the 2012 season, that I plan to share in other posts. For now, I want to end with just one of those statistics, which I think gets to the essence of why I’d like to see official wins calculated in this way.

In 396 games in 2012, the starter threw six or more shutout innings in a game his team ended up winning. In 31 of these games, the starter did not earn the win. That’s 7.8% of these excellent starting performances that could have been awarded a win, but weren’t. By contrast, in all 396 of these games, the starter earned the merit win.

In that they attribute a team stat to an individual, wins and losses have always been flawed, and flawed they shall remain. But at least let’s start awarding them to the right player. That would make them a little less flawed.

## Why does Mookie Betts get so much more out of each swing than anybody else?

Each swing of Mookie Betts’ bat produces more hits, and far more total bases, than anybody else’s. Have a look:

Hits per swing leaders as of mid August 2018
rank Name H/Swing
1 Mookie Betts .197
2 Andrelton Simmons .185
3 Nick Markakis .177
4 Michael Brantley .175
5 Jose Altuve .172
6 Ben Zobrist .171
7 Joe Mauer .169
8 Daniel Murphy .164
9 Tony Kemp .163
10 Jesse Winker .163
11 Jean Segura .163
12 Christian Yelich .162
13 Jose Martinez .160
14 David Freese .160
15 Lorenzo Cain .160
16 DJ LeMahieu .160
17 David Fletcher .157
18 Alex Bregman .157
19 Buster Posey .156
20 Isiah Kiner-Falefa .156
Total bases per swing leaders as of mid August 2018
rank Name TB/Swing
1 Mookie Betts .375
2 Matt Carpenter .313
3 Jose Ramirez .309
4 Mike Trout .308
5 J.D. Martinez .294
6 Max Muncy .292
7 Alex Bregman .288
8 Steve Pearce .282
9 Ryan Zimmerman .278
10 Juan Soto .276
11 Ronald Acuna .275
12 Nick Markakis .274
14 Eugenio Suarez .274
15 Francisco Lindor .273
16 Michael Brantley .273
17 Christian Yelich .270
19 Aaron Judge .269
20 Javier Baez .265

(My apologies for the use of mid-August numbers. It has taken me a while to complete this article due to lack of available time.)

On the total base per swing list, the difference between Betts and second place is bigger than the difference between second place and 37th place.

What’s especially interesting to me is that if you look at the top 11 names on each list, you see that they’re entirely different lists, except for the one name, Betts, at the top of each list. That’s remarkable when you consider the seemingly opposed approaches to getting on one list versus getting on the other. The hits per swing list is full of guys who’ve optimized their games for making contact, and perhaps aiming the ball to “hit ’em where they ain’t”. The total bases per swing list is full of guys who’ve optimized their game for impacting the ball, driving it hard and presumably with a good launch angle. These differing approaches display for us a tradeoff that exists throughout sports – the tradeoff between accuracy and power. Think of a pitcher who overthrows a fastball and loses control of it. Think of a bowler who might slow down his roll to be more accurate, or speed up his roll to get more power. You can surely think of some other examples.

Betts appears to be defying that tradeoff, excelling at both accuracy and power with each swing of his bat. How does he do it?

### An attempt to break down the skills that contribute to turning swings into hits

To get an idea, let’s try breaking down the different skills that would go into producing high numbers for bases per swing.

Consider four main divisions: pitch recognition, accuracy of swing, power in swing, and sprinting speed. (A fifth, park factors, is relevant, but not one I’ll spend much time on in this article. It does come out at the end though, for Betts.)

This second division, “accuracy of swing”, can be further subdivided into three dimensions: timing (depth), horizontal accuracy (width), and vertical accuracy (height).

We can break down power into some components, too, but we’ll do that later to keep things from getting too confusing.

Which results do each of these skills affect?
If your pitch recognition is poor, you’ll have a lot of swings and misses, and possibly a lot of bad contact. Your HpS (Hits per swing) and TBpS (Total Bases per swing) will both suffer.

If your timing is off, you’ll be either early or late. It can add to your swings and misses, but perhaps the best indication of poor timing will be hitting a lot of foul balls relative to balls hit fair. While this doesn’t necessarily hurt you as a hitter (it works great for Mike Trout), it will lower your HpS and TBpS.

If your vertical accuracy is off, that brings some swings and misses, but mostly popups and weak ground balls. This could be hard to separate from poor pitch recognition. I’m assuming poor pitch recognition is more closely associated with no contact, and poor vertical accuracy is more associated with poor contact.

If your horizontal accuracy is off, you’ll still hit the ball, but you won’t hit it on the sweet spot. This will sap your power, because it causes vibrations and bending in the bat that don’t happen when the ball hits the sweet spot. That bending sends energy away from the point of contact, so there is less energy stored in the compression of the ball and bat at that point of contact. Thus less energy rebounds back into the ball, and it leaves the bat with less velocity.

I had previously written in this article that putting more strength behind a swing would make up for some horizontal inaccuracy, but according to this David Kagan article, that’s not true. After a lot of thought about it, I agree with that assessment.

So apart from horizontal accuracy, power is increased by faster bat speed, and having a denser or heavier bat in the barrel. Since bats all appear to be at the regulation maximum width, and most players use the already dense wood maple for their bats, the only real variation in bats today will be in bat length. A longer bat will be harder to control, but will have a bigger sweet spot that moves at a greater speed due to being farther from the bat’s pivot point (near the hands). Players with the forearm strength to control a longer bat will generate more power using one.

Players without as much forearm strength must generate momentum by increasing bat speed. This is also increased by strength, but a not-as-strong player can still match stronger players in terms of strength put into the swing by being effective at involving their strongest muscles, those in their legs and core. It’s easier said than done. It takes a lot of whole-body coordination and skill. Mookie Betts has always been known for having exactly that.

### Basic skills Betts is known for

What else of these things is Betts known for? Well, he was a standout in neuroscouting tests done in his prospect days, tests that try to measure how quickly and accurately a player recognizes pitches. And this article speaks to his and Mike Trout’s excellence at swinging at strikes and not at balls, with Betts in the top 1% of players for both. So there’s already good evidence of great pitch recognition on his part.

Commentators frequently speak of his quick hands. So he’s got a reputation for bat speed, which means power when combined with horizontal accuracy or strength of swing.

So to summarize, out of bat speed, strength of swing, pitch recognition, timing, vertical accuracy, and horizontal accuracy, Betts has some reputation for the first three, and we don’t know about the last three. So, let’s look at some stats!

### The Numbers

These numbers are taken from mid-August. They include the 397 players who, at that point, had at least 100 plate appearances and 60 “batted ball events” (batted balls that produce a result, such as a hit, out, or error; this includes some foul balls).

### Not swinging and missing

We’ve already referenced how Mookie Betts is in the top 1% of players at not swinging at balls, and at rate of swinging at strikes, which may be the best indication that he doesn’t get fooled. But having a low rate of swings and misses should more directly impact his TBpS and HpS numbers, so let’s see the data on misses per swing for the two groups:

Misses per swing, for TBpS leaders
Name Miss per Sw rank pctl
Mookie Betts 15.0% 33 91.9%
Matt Carpenter 23.8% 181 54.5%
Jose Ramirez 13.5% 15 96.5%
Mike Trout 18.9% 76 81.1%
J.D. Martinez 28.5% 293 26.3%
Max Muncy 28.8% 301 24.2%
Alex Bregman 13.8% 21 94.9%
Steve Pearce 23.9% 182 54.3%
Ryan Zimmerman 25.8% 234 41.2%
Juan Soto 22.4% 144 63.9%
Ronald Acuna 26.3% 246 38.1%
Nick Markakis 11.3% 5 99.0%
Eugenio Suarez 24.1% 190 52.3%
Francisco Lindor 18.8% 75 81.3%
Michael Brantley 11.6% 6 98.7%
Christian Yelich 24.3% 196 50.8%
Aaron Judge 36.3% 388 2.3%
Javier Baez 32.4% 354 10.9%
Misses per swing, for HpS leaders
Name Miss per Sw rank pctl
Mookie Betts 15.0% 33 91.9%
Andrelton Simmons 12.4% 10 97.7%
Nick Markakis 11.3% 5 99.0%
Michael Brantley 11.6% 6 98.7%
Jose Altuve 18.0% 66 83.6%
Ben Zobrist 14.7% 31 92.4%
Joe Mauer 14.5% 28 93.2%
Daniel Murphy 10.0% 1 100.0%
Tony Kemp 16.0% 43 89.4%
Jesse Winker 15.3% 36 91.2%
Jean Segura 12.2% 8 98.2%
Christian Yelich 24.3% 196 50.8%
Jose Martinez 19.4% 83 79.3%
David Freese 23.0% 158 60.4%
Lorenzo Cain 18.7% 74 81.6%
DJ LeMahieu 14.5% 26 93.7%
David Fletcher 10.5% 2 99.7%
Alex Bregman 13.8% 21 94.9%
Buster Posey 13.6% 18 95.7%
Isiah Kiner-Falefa 14.6% 30 92.7%

Of the Hits per Swing leaders, 14 of the 20 are in the top 10% for not missing when swinging. Christian Yelich and David Freese are the anomalies in that group. The ability to not be fooled appears to be one of the top skills that will help a player become a HpS leader. But notice that even among those in the top 10% for making contact, about two thirds are not on this list. So clearly there are other skills that will help.

For the Total Bases per Swing leaders, however, it’s all across the board. One is in the bottom 3%; four are in the bottom 30%; and only seven are in the top 36%. However, five of the twenty are in the top 10%. The ability to make contact does appear to have a positive impact on a player’s placement on the TBpS leader list, but that positive impact seems small; there have to be some other skill or skills that make a much stronger impact on TBpS. You likely already have an idea of what some of those other things are, but I’ll be getting to those later, so I won’t mention them just yet.

Except for Christian Yelich, all five guys who appear on both the TBpS and HpS leader lists (names in bold italics) have high rates of contact. (What’s up with Christian Yelich?)

Given his high percentile for not swinging and missing (top 9% of all players), this is clearly a skill that is helping Mookie Betts appear on the HpS list. Given how few people on the TBpS list have a high rate of contact, it ought to be a separator for him there.

### Timing – keeping it fair

Now let’s look at balls put in play (fair territory) as a ratio of swings that made contact. So we divide balls in play by the sum of balls in play and foul balls. Excellence at this probably indicates the player has good timing, though a player with an all-fields approach, or with an approach of intentionally fouling off difficult two-strike pitches, might have good timing and still show poorly here.

Name IP p Cont rank pctl
Mookie Betts 56.2% 23 94.4%
Matt Carpenter 51.0% 155 61.1%
Jose Ramirez 49.0% 232 41.7%
Mike Trout 45.5% 329 17.2%
J.D. Martinez 45.8% 321 19.2%
Max Muncy 48.9% 238 40.2%
Alex Bregman 56.0% 31 92.4%
Steve Pearce 52.1% 103 74.2%
Ryan Zimmerman 58.6% 13 97.0%
Juan Soto 50.3% 171 57.1%
Ronald Acuna 45.1% 342 13.9%
Nick Markakis 54.8% 45 88.9%
Eugenio Suarez 49.5% 209 47.5%
Francisco Lindor 50.8% 161 59.6%
Michael Brantley 60.6% 6 98.7%
Christian Yelich 51.4% 135 66.2%
Aaron Judge 49.3% 215 46.0%
Javier Baez 52.0% 113 71.7%
Name IP p Cont rank pctl
Mookie Betts 56.2% 23 94.4%
Andrelton Simmons 66.8% 1 100.0%
Nick Markakis 54.8% 45 88.9%
Michael Brantley 60.6% 6 98.7%
Jose Altuve 55.8% 36 91.2%
Ben Zobrist 56.5% 22 94.7%
Joe Mauer 61.8% 4 99.2%
Daniel Murphy 54.3% 56 86.1%
Tony Kemp 60.4% 7 98.5%
Jesse Winker 54.3% 54 86.6%
Jean Segura 53.0% 82 79.5%
Christian Yelich 51.4% 135 66.2%
Jose Martinez 54.2% 59 85.4%
David Freese 54.2% 57 85.9%
Lorenzo Cain 53.0% 79 80.3%
DJ LeMahieu 59.1% 11 97.5%
David Fletcher 61.3% 5 99.0%
Alex Bregman 56.0% 31 92.4%
Buster Posey 53.7% 67 83.3%
Isiah Kiner-Falefa 57.0% 19 95.5%

Of the Hits per Swing leaders, 11 of the 20 are in the top 10%, and all but one are in the top 21%. (The one who isn’t: Christian Yelich. Really, what is up with Christian Yelich?) This shouldn’t be surprising; if you hit a ton of foul balls, that adds a lot to your swings total without adding to your hits total, making your hits per swing ratio small. As with contact rate, the ability to keep the ball fair appears to be one of the top skills that will help a player become a HpS leader, but also clearly not the only one.

The Total Bases per Swing leaders, however, are once again all across the board. (According to Sam Miller of ESPN.com, all those foul balls work for Mike Trout, because with his excellent eye for the strike zone, they earn him more walks.) The ability to keep the ball fair seems to have a small impact, relative to some other skills, on becoming a TBpS leader.
But with 30% of the TBpS leaders list in the top 12% for keeping the ball fair, it certainly seems to help, and so it also can be a separator on this list for those who excel at it. With Betts in the top 6% at keeping his hit balls fair, it serves as another separator for him among the TBpS leaders.

If you multiply balls in play per contact by contacts per swing, you get balls in play per swing. And it should be apparent that improving your balls in play per swing is typically going to increase your hits per swing. And this product we speak of is just the product of the two stats we’ve looked at so far. So it makes sense to see so many players on the HpS leaders list among the best at both of these skills. And if you look, you’ll see that several of the other top players on the HpS list have more balls in play per swing than Betts. So there must be something about the balls that Betts puts in play that makes them more likely to become hits, than those of the other guys on the HpS list. Could it be power?

### Hitting the ball hard

Okay, let’s have a look at some power stats, then. We’ll focus on average exit velocity. but we’ll also list FanGraphs’ rates of hard and soft contact here, for a different look.

Average exit velocity (in MPH), rates of hard, soft contact of TBpS leaders
Name Avg exit vel rank percentile Soft% Percentile Hard% Percentile
Mookie Betts 92.5 17 96.0% 13.5% 84.8% 44.8% 91.8%
Matt Carpenter 90.4 69 82.8% 9.5% 98.8% 51.1% 99.8%
Jose Ramirez 89.2 139 65.2% 18.4% 42.0% 38.2% 64.0%
Mike Trout 91.4 34 91.7% 14.8% 78.5% 45.3% 93.3%
J.D. Martinez 93.3 9 98.0% 10.1% 98.0% 46.1% 94.5%
Max Muncy 90.9 52 87.1% 11.7% 95.8% 46.7% 95.5%
Alex Bregman 89.1 145 63.6% 17.5% 51.0% 37.0% 55.8%
Steve Pearce 90.2 81 79.8% 18.4% 41.3% 40.0% 76.0%
Ryan Zimmerman 94 5 99.0% 16.1% 66.8% 44.5% 91.5%
Juan Soto 88.9 162 59.3% 20.8% 21.8% 36.7% 53.5%
Ronald Acuna 91 49 87.9% 12.0% 94.0% 47.1% 96.5%
Nick Markakis 90.8 54 86.6% 12.9% 89.3% 40.4% 78.5%
Manny Machado 91.9 23 94.4% 17.7% 50.3% 38.4% 65.5%
Eugenio Suarez 91.1 43 89.4% 8.4% 99.5% 50.5% 99.3%
Francisco Lindor 90.6 65 83.8% 14.9% 77.0% 42.0% 84.0%
Michael Brantley 90.5 67 83.3% 11.4% 96.5% 38.7% 67.0%
Christian Yelich 92.9 11 97.5% 14.5% 79.0% 47.0% 96.3%
Nolan Arenado 90.5 68 83.1% 13.2% 86.8% 43.8% 89.8%
Aaron Judge 95.8 1 100.0% 13.0% 88.8% 47.9% 97.5%
Javier Baez 90.2 84 79.0% 18.5% 40.5% 37.5% 60.3%
Average exit velocity (in MPH), rates of hard, soft contact of HpS leaders
Name Avg exit vel rank pctl Soft% Percentile Hard% Percentile
Mookie Betts 92.5 17 96.0% 13.5% 84.8% 44.8% 91.8%
Andrelton Simmons 88.1 204 48.7% 20.6% 23.0% 36.9% 54.5%
Nick Markakis 90.8 54 86.6% 12.9% 89.3% 40.4% 78.5%
Michael Brantley 90.5 67 83.3% 11.4% 96.5% 38.7% 67.0%
Jose Altuve 87.4 238 40.2% 14.9% 76.5% 35.0% 42.5%
Ben Zobrist 89.4 129 67.7% 12.0% 93.5% 36.9% 54.8%
Joe Mauer 90 89 77.8% 13.1% 87.3% 43.3% 88.3%
Daniel Murphy 87 259 34.8% 13.5% 85.3% 24.9% 5.5%
Tony Kemp 82.5 385 3.0% 17.1% 55.5% 28.7% 14.8%
Jesse Winker 90.2 77 80.8% 11.8% 95.5% 43.9% 90.5%
Jean Segura 87.1 254 36.1% 22.0% 14.3% 26.9% 9.3%
Christian Yelich 92.9 11 97.5% 14.5% 79.0% 47.0% 96.3%
Jose Martinez 90.9 50 87.6% 14.8% 77.8% 39.8% 75.3%
David Freese 90.1 87 78.3% 16.9% 57.5% 34.8% 41.0%
Lorenzo Cain 89.3 133 66.7% 19.0% 35.0% 38.8% 68.0%
DJ LeMahieu 91 45 88.9% 15.1% 75.0% 36.1% 49.8%
David Fletcher 82.9 382 3.8% 20.2% 26.3% 31.2% 25.3%
Alex Bregman 89.1 145 63.6% 17.5% 51.0% 37.0% 55.8%
Buster Posey 89.2 140 64.9% 13.7% 83.5% 36.6% 53.3%
Isiah Kiner-Falefa 83.7 369 7.1% 19.1% 33.5% 31.0% 24.5%

This time, it’s the Hits per Swing leaders that are all across the board, while the Total Bases per Swing leaders all do well, all being in the top 41% for exit velocity, and all but 3 in the top 21%.

So the ability to hit the ball hard would seem to be a prerequisite for being a TBpS leader, just as not being fooled and having good timing would seem to be a prerequisite for being a HpS leader. But these things by themselves are not enough. For example, though 17 of the 20 TBpS leaders are in the top 21% for exit velocity, so are 67 other players who are not on this list. A little more digging will be required to see what puts any one player on this list. For the scope of this article, we’ll keep it to Mookie Betts. Well, actually, I will have some comments along the way for a couple of other guys on these lists.

### Excelling at all aspects

Now have a look at these three lists and see who ranks in the top 10% on more than one of them.

There are nine players who are in the top 10% of the misses per swing and the balls in play per contact lists:

• Mookie Betts
• Andrelton Simmons
• Michael Brantley
• Ben Zobrist
• Joe Mauer
• DJ LeMahieu
• David Fletcher
• Alex Bregman
• Isiah Kiner-Falefa

There is only one player, however, who is top 10% for exit velocity and is top 10% on either of the other lists: Mookie Betts, who is top 10% on all three.

There are a few players who come close, however:

• Nick Markakis
• Michael Brantley
• DJ LeMahieu

These three players are in the top 20% of all three lists, and Markakis and Brantley are both on the leader lists for TBpS and HpS.

How do these few players manage to pull off both so well? Let’s think for a moment about the players we saw who are good at keeping the ball fair. We can hypothesize that it’s because these players have good timing. It ought to help them direct the ball to the part of the field where they want it to go. One way to ensure good timing is to slightly slow down your swing, extending the time at which it’s at the angle needed to keep the ball fair. But this saps power, so if most of these guys are indeed slowing down their swings a bit to attain that better timing, this would explain why they don’t put up good power numbers.

But Markakis, Brantley, LeMahieu, and especially Betts do manage those good power numbers, while having good timing, too. This would seem to indicate that these guys are not slowing down their swings. Or they have naturally quicker swings. Or they may have naturally better timing, and thus not need to artificially improve their timing by slowing down their swings. Betts’ Neuroscouting test results lend credence to that idea. So Bett’s ability to combine pitch recognition, timing, and power so well may boil down to his ability to recognize and physically react to pitches more quickly than anyone else.

Though Betts may be the best at combining well-timed contact with power, given that some other guys do that well, it might not be enough to show how he separates himself on the TBpS list. What else could go into this?

### Running speed

There’s running speed. We should look at that.

I went on BaseballSavant.com and looked at guys with at least 50 “qualifying runs” on the season. These are events at which they’re presumed to have reached their top speed. There were 378 such players. When they take the top two-thirds of these qualifying runs and average them, here is how our TBpS and HpS leaders fared:

Sprint speed (in feet/second) of TBpS leaders
Name Sprint speed rank percentile
Mookie Betts 28.1 99 74.0%
Matt Carpenter 26.4 264 30.2%
Jose Ramirez 27.6 161 57.6%
Mike Trout 29.2 17 95.8%
J.D. Martinez 26.8 231 39.0%
Max Muncy 27.6 159 58.1%
Alex Bregman 27.8 126 66.8%
Steve Pearce 26.1 289 23.6%
Ryan Zimmerman 26.6 252 33.4%
Juan Soto 27.3 196 48.3%
Ronald Acuna 29.6 9 97.9%
Nick Markakis 26.4 267 29.4%
Eugenio Suarez 26.1 287 24.1%
Francisco Lindor 28.4 70 81.7%
Michael Brantley 26.1 283 25.2%
Christian Yelich 28.5 64 83.3%
Aaron Judge 28 102 73.2%
Javier Baez 28.8 43 88.9%
Sprint speed (in feet/second) of HpS leaders
Name Sprint speed rank percentile
Mookie Betts 28.1 99 74.0%
Andrelton Simmons 27.2 206 45.6%
Nick Markakis 26.4 267 29.4%
Michael Brantley 26.1 283 25.2%
Jose Altuve 28.3 73 80.9%
Ben Zobrist 26.6 246 35.0%
Joe Mauer 25.9 304 19.6%
Daniel Murphy 25.3 337 10.9%
Tony Kemp 27.5 176 53.6%
Jesse Winker 26 292 22.8%
Jean Segura 27.9 119 68.7%
Christian Yelich 28.5 64 83.3%
Jose Martinez 26.4 272 28.1%
David Freese 26.5 257 32.1%
Lorenzo Cain 28.6 60 84.4%
DJ LeMahieu 26.9 218 42.4%
David Fletcher 28.1 98 74.3%
Alex Bregman 27.8 126 66.8%
Buster Posey 24.9 355 6.1%
Isiah Kiner-Falefa 28 110 71.1%

I expected to see some plodders on the TBpS list, but the real surprise was that only three of the top 10 players for HpS are in the top half of players for running speed. Speed is clearly not a primary factor in hitting well. However, it can certainly help a player get a few extra hits and a few extra bases taken, and that should help a player separate himself. And it helps Betts in this case. Of the top 10 on the HpS list, only Jose Altuve is faster than Betts; of the top 10 on the TBpS list, only Mike Trout is faster than Betts.

There’s two more things to look at. One, we’ll look at vertical accuracy by looking at Betts’ ratios of ground balls, line drives, fly balls, and popups. Then we’ll also look to see if he uses all fields, a skill that can keep defenses from shifting on pull hitters. For these, we’ll use numbers from Fangraphs’ batted balls stats page.

### Vertical Accuracy

I expected to see a high rate of line drives and a low ratio of infield popups to fly balls for Betts. But I didn’t see that:

Most line drives and fewest popups for TBpS leaders
Name LD% Percentile IFFB% Percentile
Mookie Betts 20.2% 40.8% 11.0% 40.0%
Matt Carpenter 28.1% 97.0% 2.0% 95.8%
Jose Ramirez 21.6% 55.3% 14.1% 20.8%
Mike Trout 23.7% 72.0% 9.1% 55.3%
J.D. Martinez 23.6% 71.3% 2.7% 92.8%
Max Muncy 18.8% 24.0% 6.3% 74.3%
Alex Bregman 21.7% 56.0% 11.6% 35.5%
Steve Pearce 24.8% 82.8% 8.9% 57.3%
Ryan Zimmerman 19.4% 31.3% 5.6% 78.5%
Juan Soto 16.6% 7.0% 7.6% 65.3%
Ronald Acuna 17.3% 11.3% 8.2% 62.8%
Nick Markakis 27.3% 94.5% 6.4% 73.3%
Manny Machado 18.7% 22.8% 11.6% 35.3%
Eugenio Suarez 25.4% 87.5% 3.4% 90.5%
Francisco Lindor 23.9% 74.3% 9.7% 49.5%
Michael Brantley 22.9% 65.5% 3.8% 88.8%
Christian Yelich 24.7% 81.5% 5.4% 79.5%
Nolan Arenado 22.9% 66.0% 12.9% 28.0%
Aaron Judge 21.4% 52.8% 4.7% 84.5%
Javier Baez 22.6% 62.5% 11.9% 33.8%
Most line drives and fewest popups for HpS leaders
Name LD% Percentile IFFB% Percentile
Mookie Betts 20.2% 40.8% 11.0% 40.0%
Andrelton Simmons 20.4% 41.8% 14.6% 18.0%
Nick Markakis 27.3% 94.5% 6.4% 73.3%
Michael Brantley 22.9% 65.5% 3.8% 88.8%
Jose Altuve 24.4% 78.3% 5.5% 78.8%
Ben Zobrist 21.6% 54.8% 5.3% 80.8%
Joe Mauer 25.3% 85.8% 4.2% 86.8%
Daniel Murphy 25.4% 87.8% 2.9% 91.8%
Tony Kemp 24.1% 75.8% 6.3% 73.8%
Jesse Winker 24.0% 75.0% 8.9% 58.5%
Jean Segura 19.6% 33.3% 16.8% 10.5%
Christian Yelich 24.7% 81.5% 5.4% 79.5%
Jose Martinez 25.3% 86.0% 4.1% 87.5%
David Freese 21.3% 50.5% 6.7% 71.3%
Lorenzo Cain 20.1% 38.8% 7.5% 67.3%
DJ LeMahieu 21.2% 48.5% 3.7% 89.0%
David Fletcher 25.3% 86.5% 18.0% 8.0%
Alex Bregman 21.7% 56.0% 11.6% 35.5%
Buster Posey 21.8% 56.8% 2.8% 92.3%
Isiah Kiner-Falefa 24.5% 78.8% 9.5% 52.0%

Betts is in the lower half of all players in terms of most line drives and lowest ratio of popups to fly balls, and most of his peers on these top 20 lists have done better than him. It seems that horizontal accuracy is not a separator for him.

But let’s look at ground balls and fly balls. The percentiles below are for lowest ground ball rates, highest fly ball rates, and lowest ground ball to fly ball ratio.

Ground balls versus fly balls for TBpS leaders
Name GB% Percentile FB% Percentile GB/FB Percentile
Mookie Betts 34.8% 88.3% 45.0% 90.0% 0.77 89.8%
Matt Carpenter 24.1% 100.0% 47.8% 96.5% 0.5 100.0%
Jose Ramirez 32.6% 94.0% 45.8% 93.0% 0.71 94.5%
Mike Trout 32.7% 93.5% 43.5% 84.5% 0.75 91.8%
J.D. Martinez 44.4% 42.5% 32.0% 30.5% 1.39 35.8%
Max Muncy 36.2% 84.3% 45.1% 90.5% 0.8 87.8%
Alex Bregman 34.3% 90.0% 44.0% 87.3% 0.78 89.0%
Steve Pearce 39.2% 70.8% 36.0% 53.8% 1.09 63.3%
Ryan Zimmerman 45.8% 33.5% 34.8% 45.5% 1.31 41.3%
Juan Soto 53.0% 7.8% 30.4% 22.0% 1.74 14.3%
Ronald Acuna 41.8% 56.5% 40.9% 76.3% 1.02 70.8%
Nick Markakis 40.9% 62.8% 31.8% 30.0% 1.28 42.8%
Manny Machado 37.4% 79.3% 43.9% 85.8% 0.85 83.5%
Eugenio Suarez 37.3% 79.8% 37.3% 63.0% 1 73.8%
Francisco Lindor 37.5% 78.3% 38.6% 70.5% 0.97 75.8%
Michael Brantley 45.7% 34.5% 31.4% 28.8% 1.45 29.5%
Christian Yelich 53.3% 7.0% 22.0% 3.3% 2.42 4.0%
Nolan Arenado 38.8% 73.3% 38.3% 69.5% 1.01 71.5%
Aaron Judge 42.4% 52.3% 36.1% 54.5% 1.17 54.5%
Javier Baez 46.1% 32.5% 31.2% 27.5% 1.48 28.0%
Ground balls versus fly balls for HpS leaders
Name GB% Percentile FB% Percentile GB/FB Percentile
Mookie Betts 34.8% 88.3% 45.0% 90.0% 0.77 89.8%
Andrelton Simmons 49.4% 17.8% 30.2% 21.5% 1.63 20.0%
Nick Markakis 40.9% 62.8% 31.8% 30.0% 1.28 42.8%
Michael Brantley 45.7% 34.5% 31.4% 28.8% 1.45 29.5%
Jose Altuve 44.8% 39.5% 30.8% 25.5% 1.45 29.0%
Ben Zobrist 46.2% 31.8% 32.2% 31.0% 1.44 30.5%
Joe Mauer 51.0% 13.5% 23.7% 5.3% 2.15 6.8%
Daniel Murphy 36.8% 81.8% 37.8% 66.0% 0.97 75.5%
Tony Kemp 45.6% 34.8% 30.4% 22.5% 1.5 26.3%
Jesse Winker 42.1% 54.8% 33.9% 39.3% 1.24 47.3%
Jean Segura 52.0% 9.8% 28.4% 15.0% 1.83 11.5%
Christian Yelich 53.3% 7.0% 22.0% 3.3% 2.42 4.0%
Jose Martinez 46.5% 29.3% 28.2% 14.8% 1.65 18.5%
David Freese 53.4% 6.8% 25.3% 7.5% 2.11 7.0%
Lorenzo Cain 56.6% 3.5% 23.3% 4.5% 2.43 3.8%
DJ LeMahieu 46.1% 32.3% 32.7% 32.8% 1.41 33.8%
David Fletcher 38.8% 73.0% 35.9% 52.5% 1.08 63.5%
Alex Bregman 34.3% 90.0% 44.0% 87.3% 0.78 89.0%
Buster Posey 47.4% 25.0% 30.8% 25.3% 1.54 23.5%
Isiah Kiner-Falefa 49.8% 17.0% 25.7% 8.5% 1.94 9.0%

Hang on a moment – Betts does well in all of these! Top 12% in each. So let’s see – slightly above average number of popups, a high number of fly balls, slightly below average number of line drives, and a low number of ground balls. All that adds up to a guy who has prioritized getting the ball in the air. He’s sacrificing a few line drives and taking on a few extra popups in order to avoid hitting ground balls. This has likely earned him more extra base hits.

### Vertical accuracy and Christian Yelich

Before we move on to looking at use of all fields, let’s examine two more players here. Christian Yelich is the opposite of Mookie Betts here. An extremely high rate of ground balls, a high rate of line drives, an extremely low number of fly balls, of which a very small fraction are popups. He avoids having his balls caught for outs, and given that he’s one of the speediest players in these top 20 lists, that will help bring his speed into play to his advantage. More on this later.

### Vertical accuracy and Matt Carpenter

The other player is Matt Carpenter. Look at those numbers. Matt Carpenter is the absolute king of vertical accuracy with the bat. He’s a wizard. He simultaneously has the lowest ground ball rate of all players, while also having one of the lowest fractions of fly balls that are popups. It’s all line drives and driven fly balls for Matt Carpenter. Yelich avoids popups by hitting a lot of ground balls; Betts avoids ground balls by hitting a few extra popups; Carpenter avoids both. His line drive and fly ball rates are among the best, and his ground ball to fly ball ratio is the lowest of all players. He’s number 2 in all of baseball in percentage of balls hit hard. And the thing is, accuracy with bat placement is his one exceptional skill. He’s got fairly average numbers for swing and miss and foul balls, so he doesn’t excel at not getting fooled and having good timing. His power isn’t great, either – sure, he’s top 20% in exit velocity, but when you’re top 0.5% in your fraction of hard-hit balls, that’s not impressive. His sprinting speed is in the bottom third of all players. The fact that he ranks second in all of baseball in total bases per swing is based entirely on his exceptional accuracy in positioning his bat when he swings.

### Using all fields

At last, let’s look at whether Betts uses an all-fields approach, again using batted ball numbers from FanGraphs. They provide percentages of balls hit to the opposite field, up the middle, and pulled. We’ll rank high fractions higher for opposite field hitting, and low fractions higher for pull hitting.

All fields approach for TBpS leaders
Name Pull% Percentile Oppo% Percentile
Mookie Betts 49.2% 8.3% 16.9% 2.5%
Matt Carpenter 47.4% 13.3% 23.2% 36.3%
Jose Ramirez 52.4% 1.8% 19.4% 10.8%
Mike Trout 41.7% 43.0% 23.0% 34.5%
J.D. Martinez 40.9% 50.8% 30.3% 89.5%
Max Muncy 44.4% 26.5% 24.3% 45.3%
Alex Bregman 47.5% 12.3% 19.7% 13.0%
Steve Pearce 59.2% 0.3% 14.4% 0.5%
Ryan Zimmerman 33.6% 88.8% 22.6% 31.0%
Juan Soto 34.8% 82.5% 29.9% 87.3%
Ronald Acuna 45.2% 22.5% 19.7% 13.5%
Nick Markakis 32.1% 92.0% 28.9% 81.8%
Manny Machado 38.1% 69.0% 26.7% 66.5%
Eugenio Suarez 43.1% 33.0% 20.9% 19.3%
Francisco Lindor 39.4% 59.3% 26.2% 63.8%
Michael Brantley 40.6% 52.3% 20.9% 19.0%
Christian Yelich 35.5% 80.8% 26.6% 66.0%
Nolan Arenado 39.1% 61.0% 24.0% 42.0%
Aaron Judge 42.4% 39.3% 27.7% 75.0%
Javier Baez 41.7% 43.8% 24.9% 51.0%
All fields approach for HpS leaders
Name Pull% Percentile Oppo% Percentile
Mookie Betts 49.2% 8.3% 16.9% 2.5%
Andrelton Simmons 51.0% 3.8% 16.3% 1.0%
Nick Markakis 32.1% 92.0% 28.9% 81.8%
Michael Brantley 40.6% 52.3% 20.9% 19.0%
Jose Altuve 37.2% 73.8% 21.8% 26.3%
Ben Zobrist 48.5% 10.3% 20.8% 18.0%
Joe Mauer 26.9% 98.3% 33.8% 97.0%
Daniel Murphy 36.8% 77.0% 35.1% 98.5%
Tony Kemp 49.4% 7.8% 22.0% 27.0%
Jesse Winker 37.1% 74.8% 25.7% 58.0%
Jean Segura 38.4% 66.0% 22.7% 31.5%
Christian Yelich 35.5% 80.8% 26.6% 66.0%
Jose Martinez 30.8% 95.8% 35.2% 98.8%
David Freese 38.2% 68.5% 28.7% 81.0%
Lorenzo Cain 31.2% 94.5% 32.4% 94.8%
DJ LeMahieu 28.6% 97.5% 29.5% 84.3%
David Fletcher 42.8% 36.5% 22.5% 29.5%
Alex Bregman 47.5% 12.3% 19.7% 13.0%
Buster Posey 33.7% 88.3% 28.2% 78.0%
Isiah Kiner-Falefa 39.7% 57.5% 28.6% 79.8%

Wow. There’s really only one player on each list that is less of an all-fields hitter than Betts. He’s one of the most extreme pull hitters in all of baseball. But so is Jose Ramirez of the Indians, his fellow MVP candidate. Could pull hitting work in his favor?

Consider this: he pulls balls and hits them in the air. He also doesn’t hit foul balls, which would lead one to suspect that pulling the ball is deliberate. And when he pulls the ball in the air in home games, it goes right to Fenway Park’s Green Monster, a nice, big, close target. Sure enough, his batting average is more than 40 points higher at home than on the road this season.

Remember how Mookie Betts was a standout in neuroscouting tests. That says he identifies pitches quickly. This gives him the ability to not be fooled, and to have excellent timing. With excellent timing and quick hands, he can consistently pull the ball while keeping it fair. And by pulling it and keeping it in the air, he maximizes distance traveled. He’s leveraging his natural abilities and the Green Monster to get the ball past the warning track and rack up a lot of bases.

### What is up with Christian Yelich

And finally, looking over these all-fields numbers, we now know what is up with Christian Yelich. He’s got one of the strongest all-fields approaches on this list. He hits the ball hard – Fangraphs has him in the top 4% of players in percentage of balls that are hard hit, and BaseballSavant has him in the top 3% for average exit velocity. He’s top 20% in sprint speed. All of which combines to make him hard to defend on balls in play, which would explain why he has the highest BABIP on these lists, and is in the top 1.5% in the league for BABIP. This is why hitting ground balls works for him. He can use his speed and the fact that he’s a step closer to first base than right-handed hitters to beat out ground balls. And he can’t be shifted on, so more of those ground balls will get through to the outfield. And let’s recall that he’s not bad at not swinging and missing and keeping the ball fair; he’s average at these things, where his peers on these lists excel at these things. He’s just on these lists for different reasons.