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There was no way Tanner Houck was ever going to complete the game he started for the Boston Red Sox on Saturday. He’d been moved to their bullpen a couple of weeks before, and was no longer stretched out enough to throw a complete game. People had their expectations set at 3 innings, when thinking about how many innings he’d complete. So to get 5 perfect innings was huge.

A nice bonus for Houck was that by completing 5 innings with the lead, he was qualified to be awarded the win. All the Red Sox’ relief pitchers had to do was maintain that lead through the rest of the game.

But they didn’t. They allowed the game to become tied, which took Houck out of the running for the win. The win went to Austin Davis, only because he was lucky enough to have been the last Red Sox pitcher to have thrown a pitch, at the time his team broke the tie and built a 4-run lead in the 9th inning. His results, 2 runs allowed over ⅔ of an inning, were the worst of any of the six Red Sox pitchers in the game; for this he was awarded the win.

Seems wrong, doesn’t it?

There is a way to fix this. There is a way of awarding wins that gives it to the right pitcher. I call it the merit method of awarding wins. I explain the method in my post The how and the why of awarding wins to pitchers by the merit method.

Using that method, we take the 5 runs that the Red Sox scored in Saturday’s game and divide by 9 to get the average number of runs they scored each inning (5/9, or 0.56). We then credit each Red Sox pitcher with this number of runs for every inning they pitched. We see this in the first three columns in the table below, IP, RCr/IP, and RCr, which stand for Innings Pitched, Runs Credited per inning pitched, and Runs Credited, respectively. You get the third column (Runs Credited) by multiplying together the first two.

Pitcher	IP	RCr/IP	RCr	R	RA	Result
T. Houck	5	0.56	2.78	0	2.78
G. Richards	1	0.56	0.56	0	0.56	Hold
R. Brasier	1	0.56	0.56	0	0.56	Hold
A. Ottavino	⅓	0.56	0.19	1	-0.81	Hold
A. Davis	⅔	0.56	0.37	2	-1.63	Win
H. Robles	1	0.56	0.56	0	0.56	Save

Then you subtract runs allowed (R) from this to get each pitcher’s number of Runs Ahead (RA) for that game. Because Tanner Houck had the highest number of Runs Ahead for the winning team, he would be awarded the win by the merit method. Instead, he was the only one of the six Red Sox pitchers that night who wasn’t awarded anything. Some thanks for being perfect.

The merit method of awarding wins is one I first publicly proposed in 2018 to fix all the flaws in the way wins are currently awarded in baseball. Some of those flaws are severe. I spell out some of these flaws in my original post, “Fixing how wins are awarded in baseball“.

The method focuses on the number of runs scored by each team, just like the current one does, but awards a winner based on which pitcher did the most to help his team win that game. The current method awards it rather randomly to whoever happened to be the pitcher at the time his team took its last lead of the game. This often awards the win to the least deserving pitcher, and can reward relief pitchers for pitching worse. The merit method does away with those problems, and a host of other problems with the current method.

To explain how it is calculated, and the reason for calculating it that way, a little background helps. There are a ton of pitching statistics in baseball, but for a pitcher, only two matter in determining the outcome of a game: outs recorded, which he wants to maximize, and runs allowed, which he wants to minimize. A merit-based method of awarding wins would do best to incorporate these two numbers, and just these two numbers.

After all, allowing 1 run over 7 innings is a better contribution than allowing 1 run over 1 inning, just like allowing 1 run over 1 inning is a better contribution than allowing 7 runs over 1 inning.

But what if one pitcher allows 3 runs over 7 innings, and another allows 1 run over 1 inning? Which of these pitchers does more to help his team win? We need a way to be able to attach a value to innings pitched, that allows us to compare it to runs allowed, and come up with a single number that determines how much that pitcher did to help his team win.

The merit method does this by crediting the pitcher with a number of runs per inning pitched, adding these up over all the innings pitched, and then subtracting from this the number of runs that pitcher allowed. The resulting number of runs is called that pitcher’s “Runs Ahead”. The win is awarded to the pitcher on the winning team with the greatest number of Runs Ahead for that game. (Likewise, we can award the loss to the pitcher on the losing team with the lowest number of Runs Ahead for that game.)

As for the number of runs per inning to credit that pitcher with in the first part of that calculation? That’s just the average number of runs his own team scored per inning played in that same game.

One nice thing about calculating things in this way is that the winner always has a positive number for Runs Ahead, and the loser always has a negative number of Runs Ahead.

Let’s see how this works in a game from August 23, 2021, when the Boston Red Sox were visiting the Texas Rangers. (This was the first in a string of 6 consecutive starts by Nathan Eovaldi that were no-decisions for him, but were wins for the team. I write about that streak in Nathan Eovaldi’s 6-game no-decision streak would be a 6-game win streak if wins were awarded in this way.)

In that game, the Red Sox scored 8 runs over 10 innings, for a run credit per inning of 8 / 10 = 0.8. In the table below, this rate is used to convert Innings Pitched (IP) into a number of credited runs, for each player who pitched for the Red Sox in that game. From this run credit, we then subtract the number of runs that pitcher allowed, to get that pitcher’s Runs Ahead for that game.

Pitcher	Innings pitched (IP)	Run credit per IP	Runs credited (RCr)	Runs allowed (R)	Runs ahead (RA)	Result
Nathan Eovaldi	7	0.8	7 ✕ 0.8 = 5.6	1	5.6 – 1 = 4.6	No dec (ND)
Adam Ottavino	1	0.8	1 ✕ 0.8 = 0.8	0	0.8 – 0 = 0.8	Hold (H)
Matt Barnes	⅓	0.8	⅓ ✕ 0.8 = 0.27	2	0.27 – 2 = -1.73	Blown save (BS)
Garrett Whitlock	2 ⅔	0.8	2 ⅔ ✕ 0.8 = 2.13	1	2.13 – 1 = 1.13	Win

As you can see, the merit method of awarding wins gives the win to Nathan Eovaldi by a wide margin, although the official win went to a reliever who pitched well, but didn’t do quite as much as Eovaldi to help the team win.

Tiebreakers

It does sometimes happen that two pitchers on the winning team end up tied for most Runs Ahead. In these cases, a tiebreaker is required to decide which pitcher gets the win. 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.

The following is the tiebreaking procedure as I initially imagined it.

• First tiebreaker: repeat the Runs Ahead calculation using earned runs in place of runs
• Second tiebreaker: most innings pitched (reversing this to fewest in the case of evaluating for losses)
• Third tiebreaker: fewest (most) batters faced
• Fourth tiebreaker: fewest (most) baserunners allowed (by hit, walk, or hit by pitch)
• Fifth tiebreaker: fewest (most) total bases allowed
• Sixth tiebreaker: the last pitcher to pitch.

Nathan Eovaldi of the Boston Red Sox is having a great year. He leads American League pitchers in fWAR, and his name has been mentioned as a Cy Young Award candidate.

But an odd streak is hurting his Cy Young chances right now. And that is that every one of his last 6 starts is a “no decision”. That means his record over those starts is 0-0.

If baseball used the “merit” method of awarding wins, however, he would be 6-0 over that span.

6 wins over 6 starts, instead of no wins. That’s a huge difference.

But should he have earned wins for those starts? Was he deserving of any wins in that stretch? And what is this “merit” method, anyway?

The merit method of awarding wins is one I first publicly proposed in 2018 to fix all the flaws in the way wins are currently awarded in baseball. Some of those flaws are severe. I spell out some of these flaws in my original post, “Fixing how wins are awarded in baseball“.

The method focuses on the number of runs scored by each team, just like the current one does, but awards a winner based on which pitcher did the most to help his team win that game. The current method awards it rather randomly to whoever happened to be the pitcher at the time his team took its last lead of the game. This often awards the win to the least deserving pitcher, and can reward relief pitchers for pitching worse. The merit method does away with those problems, and a host of other problems with the current method.

To explain it, a little background helps. There are a ton of pitching statistics in baseball, but for a pitcher, only two matter in determining the outcome of a game: outs recorded, which he wants to maximize, and runs allowed, which he wants to minimize. A merit-based method of awarding wins would do best to incorporate these two numbers, and just these two numbers.

After all, allowing 1 run over 7 innings is a better contribution than allowing 1 run over 1 inning, just like allowing 1 run over 1 inning is a better contribution than allowing 7 runs over 1 inning.

But what if one pitcher allows 3 runs over 7 innings, and another allows 1 run over 1 inning? Which of these pitchers does more to help his team win? We need a way to be able to attach a value to innings pitched, that allows us to compare it to runs allowed, and come up with a single number that determines how much that pitcher did to help his team win.

The merit method does this by crediting the pitcher with a number of runs per inning pitched, adding these up over all the innings pitched, and then subtracting from this the number of runs that pitcher allowed. The resulting number of runs is called that pitcher’s “Runs Ahead”. The win is awarded to the pitcher on the winning team with the greatest number of Runs Ahead for that game. (Likewise, we can award the loss to the pitcher on the losing team with the lowest number of Runs Ahead for that game.)

As for the number of runs per inning to credit that pitcher with in the first part of that calculation? That’s just the average number of runs his own team scored per inning played in that same game.

One nice thing about calculating things in this way is that the winner always has a positive number for Runs Ahead, and the loser always has a negative number of Runs Ahead.

Let’s see how this works in each of the games in Nathan Eovaldi’s 6-game no-decision streak.

The first game of the streak came on August 23, at home against the Texas Rangers. In that game, Eovaldi’s Red Sox scored 8 runs over 10 innings, for a run credit per inning of 8 / 10 = 0.8. In the table below, this rate is used to convert Innings Pitched (IP) into a number of credited runs, for each player who pitched for the Red Sox in that game. From this run credit, we then subtract the number of runs that pitcher allowed, to get that pitcher’s Runs Ahead for that game.

Pitcher	Innings pitched (IP)	Run credit per IP	Runs credited (RCr)	Runs allowed (R)	Runs ahead (RA)	Result
Nathan Eovaldi	7	0.8	7 ✕ 0.8 = 5.6	1	5.6 – 1 = 4.6	No dec (ND)
Adam Ottavino	1	0.8	1 ✕ 0.8 = 0.8	0	0.8 – 0 = 0.8	Hold (H)
Matt Barnes	⅓	0.8	⅓ ✕ 0.8 = 0.27	2	0.27 – 2 = -1.73	Blown save (BS)
Garrett Whitlock	2 ⅔	0.8	2 ⅔ ✕ 0.8 = 2.13	1	2.13 – 1 = 1.13	Win

As you can see, the merit method of awarding wins gives the win to Nathan Eovaldi by a wide margin.

Now let’s look at the second game of Eovaldi’s ND streak. On August 28 the Red Sox played in Cleveland against the Indians.

August 28, 2021 – Boston Red Sox at Cleveland Indians

• Runs scored by offense: 5
• Innings played by offense: 10
• Run credit per inning: 0.5

Pitcher	IP	RCr/IP	RCr	R	RA	Result
Nathan Eovaldi	5⅓	0.50	2.67	2	0.67	No decision
Josh Taylor	⅔	0.50	0.33	0	0.33
Hirokazu Sawamura	1	0.50	0.50	0	0.50
Austin Davis	⅔	0.50	0.33	0	0.33
Garrett Richards	⅓	0.50	0.17	0	0.17
Garrett Whitlock	1	0.50	0.50	0	0.50	Win
Martin Perez	⅓	0.50	0.17	1	-0.83	Hold
Adam Ottavino	⅔	0.50	0.33	0	0.33	Save

This was an extra innings game in which the Red Sox took the lead for good in the top of the tenth inning. The pitcher who pitched in the ninth inning got credit for the win.

The next three games in the streak are similar to this one, in that the runs Eovaldi needed from his team’s offense didn’t show up until it was too late for him to get credit for the win. However, the last game, on September 19, as we’ll see, is different …

September 3, 2021 – Cleveland Indians at Boston Red Sox

• Runs scored by offense: 8
• Innings played by offense: 8
• Run credit per inning: 1

Pitcher	IP	RCr/IP	RCr	R	RA	Result
Nathan Eovaldi	6⅓	1.00	6.33	3	3.33	No decision
Adam Ottavino	⅔	1.00	0.67	0	0.67	Win
Ryan Brasier	⅔	1.00	0.67	1	-0.33
Garrett Whitlock	1⅓	1.00	1.33	1	0.33	Save

September 8, 2021 – Tampa Bay Rays at Boston Red Sox

• Runs scored by offense: 2
• Innings played by offense: 8
• Run credit per inning: 0.25

Pitcher	IP	RCr/IP	RCr	R	RA	Result
Nathan Eovaldi	7	0.25	1.75	0	1.75	No decision
Josh Taylor	⅔	0.25	0.17	1	-0.83
Garrett Richards	⅓	0.25	0.08	0	0.08	Win
Hansel Robles	1	0.25	0.25	0	0.25	Save

September 14, 2021 – Boston Red Sox at Seattle Mariners

• Runs scored by offense: 8
• Innings played by offense: 9
• Run credit per inning: 0.89

Pitcher	IP	RCr/IP	RCr	R	RA	Result
Nathan Eovaldi	5	0.89	4.45	2	2.45	No decision
Darwinzon Hernandez	1⅔	0.89	1.48	0	1.48
Adam Ottavino	⅓	0.89	0.30	0	0.30	Win
Michael Feliz	1	0.89	0.89	0	0.89
Hirokazu Sawamura	⅓	0.89	0.30	2	-1.70
Austin Davis	⅔	0.89	0.59	0	0.59

September 19, 2021 – Baltimore Orioles at Boston Red Sox

• Runs scored by offense: 8
• Innings played by offense: 8
• Run credit per inning: 1

Pitcher	IP	RCr/IP	RCr	R	RA	Result
Nathan Eovaldi	5	1.00	5.00	3	2.00	No decision
Garrett Whitlock	1	1.00	1.00	1	0.00	Hold
Hirokazu Sawamura	1	1.00	1.00	2	-1.00	Blown save, Win
Hansel Robles	1	1.00	1.00	0	1.00	Hold
Garrett Richards	1	1.00	1.00	0	1.00	Save

Eovaldi left the game after 5 innings with 5 to 3 lead. If the relievers had held that lead through the rest of the game, Eovaldi would have gotten the win. But the reliever Hirokazu Sawamura allowed the other team to score their 5th and 6th runs, and the lead was given up. For this, Sawamura was credited with a “blown save” – not a good thing. But, he was also awarded the win. Why? Because his team took back the lead in the bottom of the same inning by scoring 3 more runs.

Had Sawamura given up 0 runs instead of 2, and everything else being the same, Eovaldi would have been credited with the win. But because Sawamura pitched worse than that, he was rewarded with the win.

This is often referred to as a “Vulture Win”. It makes no sense to allow Vulture Wins to even be possible. I think the merit method of awarding wins is the best way of eliminating them.

All data used in this post was collected from Baseball-Reference.com.

In messages with new acquaintance jasoncards  in the wake of the most recent SABR Chicago Chapter meeting, he mentioned something about the role of psychology in home field advantage, and then I recalled an attempt I made last year to quantify home field advantage. At the time I believe my true aim was removing the effects of home field advantage from the boost that Yankee Stadium gives to Yankees’ home run totals. I had briefly thought about posting about it in and of itself, but as with many of my post ideas, just couldn’t manage the time, and soon forgot about it. Jasoncard’s comments reminded me, so I fired up my old spreadsheet, did some more work with it, and came up with the results I’m sharing with you now.

Approach

I could have attempted to do a park-by-park assessment of home field advantage, or a team-by-team assessment of the same, but these would prove difficult. It would be difficult to untangle the effects of the team’s differing abilities from the parks’ home field advantage, or the different park effects from the team’s home field advantage. But looking at home and away splits for the league as a whole gives a balanced view. A single team’s skill level does not skew the results because they play as many games as the home team as they do the away team. And a single park’s effects don’t skew the results because all parks contain an equal number of home and away games represented when using whole-season, all-teams results.

I used Fangraphs’ leaderboard for team stats, selecting both Home and Away splits in turn. This Fangraphs page lets you select a range of years over which to provide cumulative statistics. I found out that the Home and Away splits data there only seem to go back to 2002, which gave me a 17-year span to work with (2002 through 2018). To get a sense of any change or progression in home field advantage, I grouped the data into three portions, of 6 years (2002 through 2007), 6 years (2008 through 2013), and 5 years (2014 through 2018). I wanted to use multiple years at a time to limit the effects of small sample sizes.

I divided all cumulative statistics by plate appearances to turn them into rate statistics, to create a good basis for comparison. First I’ll present these rates, followed by the percent differences between them.

Home and Away rate statistics

The hitting stats:

Home and away hitting stats

	2002-2007		2008-2013		2014-2018
	Away	Home	Away	Home	Away	Home
PA	573,544	552,045	567,637	546,923	470,077	452,489
AB	512,295	488,837	508,995	485,948	423,243	404,424
1B/PA	.1546	.1575	.1525	.1551	.1472	.1503
2B/PA	.0474	.0479	.0454	.0464	.0441	.0454
3B/PA	.0045	.0053	.0042	.0053	.0043	.0051
HR/PA	.0270	.0282	.0249	.0266	.0281	.0291
BB/PA	.0818	.0880	.0803	.0869	.0781	.0840
IBB/PA	.0068	.0076	.0059	.0067	.0049	.0054
HBP/PA	.0094	.0098	.0082	.0086	.0093	.0094
SO/PA	.1726	.1622	.1917	.1821	.2158	.2071
AVG	.2614	.2699	.2532	.2626	.2485	.2573
OBP	.3275	.3399	.3181	.3317	.3129	.3252
SLG	.4153	.4317	.3966	.4165	.4007	.4172
OPS	.7428	.7717	.7147	.7482	.7136	.7424

The baserunning stats:

Home and away base stealing stats

	2002-2007		2008-2013		2014-2018
	Away	Home	Away	Home	Away	Home
SB/PA	.0143	.0145	.0159	.0163	.0135	.0143
CS/PA	.0061	.0058	.0062	.0058	.0056	.0052

The team collaborative stats:

Home and away collaborative offensive stats

	2002-2007		2008-2013		2014-2018
	Away	Home	Away	Home	Away	Home
R/PA	.1185	.1268	.1109	.1196	.1112	.1195
RBI/PA	.1129	.1209	.1056	.1140	.1060	.1138
SF/PA	.0071	.0076	.0067	.0072	.0064	.0069
SH/PA	.0084	.0090	.0079	.0087	.0057	.0058

Analysis misgivings

Now let me state before going any further that I have misgivings about what I’m about to do. Taking percentage increases in rate numbers that have an upper bound can be a misleading practice. For example, let’s say in some fictional league, players reached base between 98% and 99% of the time. In such a league, a 1% increase in on base rate (say, from .9800 to .9898) is a tremendous increase. And the highest possible increase is about 2%. But for the leagues we know, with a range of about .280 to .400, a 1% increase represents a change from, say, .300 to .303, which is pretty much statistically insignificant. There are better ways to handle it, but it’s not something I can cover in a quick aside here. So note that all the statistics we’re talking about here are taking values well under half their possible maximums (the maximum is 1.000 for most of these, 4.000 for slugging percentage and 5.000 for OPS). When that’s the case, talking about percentage increases in rate numbers is good enough, and it makes intuitive sense to people.

So to be clear, an increase from .200 to .400 would be a doubling, so would be a 100% increase (not 20%).

Percentage increase in Home stats over Away stats

Here are the percentage differences in these numbers, from the Away numbers to the Home numbers:

Hitting stats

	2002-2007	2008-2013	2014-2018
H/PA	2.4% ± 0.5%	2.8% ± 0.5%	2.8% ± 0.6%
1B/PA	1.9% ± 0.6%	1.7% ± 0.6%	2.1% ± 0.7%
2B/PA	1.2% ± 1.2%	2.2% ± 1.3%	2.8% ± 1.4%
3B/PA	19.0% ± 4.4%	25.2% ± 4.6%	18.0% ± 4.9%
HR/PA	4.4% ± 1.6%	6.8% ± 1.7%	3.7% ± 1.8%
BB/PA	7.6% ± 0.9%	8.2% ± 0.9%	7.5% ± 1.1%
IBB/PA	11.8% ± 3.4%	14.9% ± 3.8%	9.9% ± 4.4%
SO/PA	-6.0% ± 0.6%	-5.0% ± 0.5%	-4.0% ± 0.6%
HBP/PA	4.4% ± 2.8%	5.0% ± 3.0%	1.1% ± 3.1%
AVG	3.3% ± 0.5%	3.7% ± 0.5%	3.5% ± 0.6%
OBP	3.8% ± 0.4%	4.3% ± 0.4%	3.9% ± 0.4%
SLG	4.0% ± 0.7%	5.0% ± 0.7%	4.1% ± 0.7%
OPS	3.9% ± 0.4%	4.7% ± 0.4%	4.0% ± 0.5%

Base stealing stats

	2002-2007	2008-2013	2014-2018
SB/PA	1.4% ± >2.3% *	2.1% ± >2.1% *	5.6% ± >2.6% *
CS/PA	-5.0% ± >3.4% *	-5.9% ± >3.4% *	-5.8% ± >3.9% *

Team offensive stats

	2002-2007	2008-2013	2014-2018
R/PA	7.0% ± 0.8% **	7.8% ± 0.8% **	7.5% ± 0.9% **
RBI/PA	7.1% ± 0.8% **	7.9% ± 0.8% **	7.3% ± 0.9% **
SF/PA	7.7% ± >3.3% *	6.5% ± >3.4% *	8.3% ± >3.9% *
SH/PA	6.3% ± >3.0% *	9.8% ± >3.2% *	1.3% ± >3.9% *

About those error estimates

I calculated the ± error bars using the formula for the standard deviation of a binomial distribution, with Plate Appearances representing the number of trials, and the actual rate of occurrence as the probability of occurrence.

The error bar sizes represent two of these standard deviations, representing a 95% confidence interval. However, this approach isn’t quite right for the statistics marked with an asterisk (*), because PA doesn’t properly represent the number of trials. Because the actual number of trials ought to be smaller, I’ve placed a greater-than symbol in front of these symbols to demonstrate that the PA-based error bars are too small.
It also isn’t right for those statistics marked with a double asterisk (**), because a “successful trial” can add more than 1 to the statistic’s total, and because the probability of a successful trial varies greatly given the men on base and the number of outs. I’ve provided the PA-based error bars for these anyway, as a reference point.

Analysis

I must say I was surprised by how all-encompassing home field advantage turned out to be.

I’m not surprised that the biggest effect was for triples. Triples usually happen when the ball is hit to very particular parts of a ballpark, and these parts are, in many cases, unique to each park. The home team players will therefore have a better idea of when they should try to stretch a double into a triple.

But that the home team would have both more stolen bases and fewer caught stealings? That’s harder to explain. Do baserunners simply run faster in their home park? Or run smarter? Is it easier for them to focus on the pitcher’s tells at home because the familiar backdrop is less distracting? Perhaps a little of all of these. Perhaps the umpires slightly favoring the home team as well, on close plays.

Maybe the baserunners run faster because the visiting team’s locker room is smaller, more cramped? Or the visitors are more tired from travel?

You can’t make that case for the hit by pitch rate, though. If anything, the better rested team should be able to dodge an errant pitch more effectively, so the home team should have fewer of these. So it’s either the umpire, or the pitcher.

Hey – maybe I’ve been focusing on the wrong thing. Maybe it’s not that the hitters and runners on the home team do better. Maybe it’s that the pitchers on the visiting team do worse. Standing in the middle of all those thousands of people who don’t like you – the pressure has to be felt by the pitcher most. And throw a major league pitcher’s very finely-tuned control off ever so slightly, and sixty feet six inches away, you get difference between a ball and a strike, or a hittable strike and an unhittable one.  Could this be the psychological effect that Jasoncards was thinking of?

But look at those error bars on SB, CS, and HBP. These are not very significant home field advantages for the stats I’ve been discussing.

They are significant, however, for walks and strikeouts. And these are some of the larger effects we’re seeing: around -5% for strikeouts, and about +7.5% for walks. So is it the hitters, the pitchers, or the umpires making the biggest difference here? I’ll guess pitchers first, then umpires second, and hitters last.

One thing I haven’t mentioned is cheating. You’d think the home team would be more likely to have devices, people, or both planted to let them pick up signs, pitch grips, what have you, or to relay information to the players. Depends on how much of that sort of thing you think goes on. Some does, but how much?

There’s one number here that is surely immune to the effects of umpire’s calls, and to cheating, and that’s intentional walks.  Neither can intentional walks be directly attributed to the skill of the pitcher or the hitter. Yet this stat has one of the largest home team boosts, between 10% and 15% over the visiting team’s rate of intentional walks! There are two causes I can think of here:
1. Because the overall offensive performance is better for the home team, they more often end up in situations that call for an intentional walk;
2. Because the home team bats last, the visiting team has clearer choices in terms of the trade offs they can make in the final inning that will allow them to win the game, including intentional walks.

In the end, the more important stats are the traditional stats like on-base percentage, slugging percentage, and OPS, and these all show about a 4% boost for the home team. But interestingly, the most important stat of all gets a bigger boost. Runs per plate appearance is 7% to 8% higher for the home team. You’d think it would be closer in line to OPS, but it’s not. The nonlinear nature of run production versus the linear nature of OPS could explain this difference.

Right, I hear you.  The number of outs per inning is always 3.  So why ask how the number of outs per inning affects run production?

Well there was likely a time, somewhere in the 1800’s, when the number of outs per inning wasn’t decided for sure.  What if those crafting the game of baseball had settled on 4-out innings instead of 3-out innings?

What if those changing the game of baseball today were to settle on 4-out innings instead of 3-out innings? It may not be much more drastic than some of the changes that have been proposed.

Would 4-out innings have 4/3 the number of runs scored as 3-out innings? If you think about it, you may find some reasons why the answer is no. But what do those reasons amount to, in terms of actual numbers of runs?

There is a run scoring model that provides a prediction of what they would amount to. It’s called Expected Binomial Production (EBP). It’s nothing special – it’s middle-of-the-pack in terms of run-predicting accuracy, compared to the most well known run scoring models such as Base runs, Extrapolated Runs, and Estimated Runs Produced. But there is a key difference. Those other models were constructed with the help of empirical data, and all that empirical data is from games played with three-out innings. They therefore cannot make predictions about games played with any other number of outs per inning. EBP, on the other hand, is 100% derived, using no empirical data, and thus can be adjusted for any number of outs.

EBP makes some simplifying assumptions. Baserunners never advance, except by a hit. Baserunners never make outs, either. It turns out that this run-decreasing assumption pretty much neutralizes this run-increasing assumption, leaving EBP with little overall bias when applied to major league baseball games. This may not be the case for amateur or minor league baseball.

Because of these simplifying assumptions, EBP will not reflect how more or less conservative baserunning, or increased numbers of double plays, will affect runs per inning when the number of outs per inning is changed.  It solely reflects the change in runs scored due to fewer runners being stranded because they had extra opportunities to score.

The EBP formulas take as inputs team on base percentage, the team’s “power profile” (ratios of home runs to triples to doubles, etc.), and percentages of baserunners who take an extra base on a hit.  When using modern numbers for those things, EBP predicts that in a 4-out inning, instead of increasing by a factor of 4/3 over 3-out innings, runs per inning increases by an additional 16%.  Put another way, runs scored per out is increased by 16% when you switch from 3-out innings to 4-out innings, due to some runners that would have been stranded instead scoring.

Here is a full introduction to Expected Binomial Production.  However, more relevant to this post is this discussion of how the formula changes when numbers of outs per innings are changed, and this discussion of the meaning of the three factors that comprise the basic EBP formula.

For years, baseball writers and bloggers have properly referred to Mike Trout as the best player in baseball.

Now, they need to break themselves of that habit, and they’re really struggling with that. So I’m here to help them.

They’re struggling because Trout hasn’t received the kind of acclaim a player of his talents ought to deserve. They look and they see perhaps the best baseball player ever, going by WAR. For example:

Mike Trout is already an all-time great (Jordan Shusterman, Cut 4, September 2018)

GOAT vs. BOAT: Mike Trout already is best of all-time (Jim Turvey, Beyond The Boxscore, June 2018)

And they also look and see little fanfare. It doesn’t seem right, and by golly, they’re going to correct it, with articles like:

Mike Trout, the best player in his sport, is the mostly unrecognizable face of baseball  (Bill Shaikin, Los Angeles Times, July 2018)

The Incredible, Unprecedented but Unseen Greatness of Mike Trout  (Tom Verducci, Sports Illustrated, July 2018)

Mike Trout May Be the Greatest Baseball Player of All Time. And Hardly Anyone Even Knows Who He Is. (Will Leitch, New York Magazine Intelligencer, March 2019)

And it’s been that way for so long, it appears they’ve stopped bothering to check whether anyone else has become his match.  Baseball America seems to do so in their October 2018 article Trout Delivers ‘Best Year’ Yet, Wins Player Of The Year .  But the worst offense comes in Fred Bowen’s August 2018 article for The Washington Post’s KidsPost, Who’s the best player in baseball? The obvious answer is ‘Mike Trout’.  In this he writes “Mike Trout … has taken the fun out of the argument of who is the best player in Major League Baseball (MLB). It’s Trout, by a lot.”  His evidence?  Stating some of the really good numbers that Trout had to that point in 2018.  That’s it, which of course by itself doesn’t show that Trout is the best “by a lot”.  For that, he’d have to compare to other players’ numbers, and doing that would have shown that Mike Trout and Mookie Betts were neck and neck at that point in OPS, base stealing, and WAR.  That’s not even the best by a little.

Yes, I’m here to say that if you do your best to evaluate who is the best in the game today, you have to concede that it’s a tie between Mookie Betts and Mike Trout.  Yet nobody’s conceding that, which you can see in these quotes about Trout from some of those articles I mentioned above, from last season:

“The best baseball player in the world”

“The best player in his sport”

“The best player in baseball is better than he ever has been”

… and continuing just this past week, after news of Trout’s contract extension broke:

 “undisputed best player in baseball since his debut” (Anthony Witrado, Forbes)

“The best player in baseball” (Charles Curtis, USA Today)

“the best player in the game … considered the consensus best player in baseball since early in his career” (Dave Sheinin, Washington Post)

“The game’s best player” (Will Leitch, New York Magazine Intelligencer)

I find this frustrating.  I want all these writers to take a good close look at this question of whether Betts is now Trout’s equal.  So right now I’m going to help them do just that.

We’ll do this in two ways.  We’ll look at WAR totals take cumulatively over the last 3 seasons.  Then to take a different perspective, we’ll compare all-time WAR numbers over the first five years of each players’ career.  We’ll use Baseball-Reference.com’s version of WAR throughout.

It is common practice to examine only the last 3 seasons of a player’s career when projecting how they will play in the future, as prior seasons are not likely to be relevant to where the player is at right now.  That’s why it makes sense for us to focus on just the last three seasons of WAR.  Given that within a single year, players whose WAR totals differ by less than one are considered effectively tied, then over a three year span, we will presume a difference in total WAR of about 1.7 or less to be a tie. (There’s some math behind that statement involving the square root of 3.)

For advanced players, WAR acts like a cumulative stat (instead of a rate stat), so I prefer comparisons of WAR per 150 games played for such players, which acts like a rate stat.  We’ll consider differences of less than 0.6 WAR to be a tie, for this.

Over the last 3 seasons, Mike Trout’s 27.3 WAR and Mookie Betts’ 27.0 WAR qualify as a tie:

This chart of the top 10 players shows not only are Betts and Trout tied, but they are far apart from the remaining players.  Jose Altuve achieves some separation from “the pack”, yet he’s still about 6 WAR below Trout and Betts.

The WAR per 150 games list has Trout with a slight edge over Betts, but the two of them still closer to each other than to the rest of the pack:

WAR per 150 games over last 3 seasons (243 G min.)

Player	G	WAR/150G
Mike Trout	413	9.9
Mookie Betts	447	9.1
Jose Altuve	451	7.1
Aaron Judge	294	6.7
Kevin Kiermaier	291	6.7
Josh Donaldson	320	6.3
Nolan Arenado	475	6.1
Jose Ramirez	461	6.1
Andrelton Simmons	428	6.1
Francisco Lindor	475	6.1

Where things really get interesting is when we look at their first five seasons compared to all other players in Major League Baseball history.  For cumulative WAR, we consider a difference of about 2 or less to be a tie.  In the table below, we see that that’s about the gap between Betts and Trout, with Trout ahead.  But what is especially interesting is their overall positions on the list:

Total WAR over first 5 (* or 6, ** or 7) seasons

Player	G	WAR
Ted Williams	736	45.1
Albert Pujols	790	37.6
Mike Trout	652	37.0
Mookie Betts	644	35.2
Jackie Robinson	751	35.2
Wade Boggs	725	35.1
Arky Vaughan	723	34.3
Joe DiMaggio	686	33.6
Johnny Mize	728	33.6
Barry Bonds	717	33.3

They are right next to each other, numbers 3 and 4 on the all time list.  Albert Pujols is the only other active player on this list; next comes Evan Longoria, in 23rd place with a 29.7 cumulative WAR over his first 5 seasons.

For the WAR per 150 games version of this list, I should point out that I made adjustments for players whose first few seasons of play were extremely short ones.  I treated those combined seasons as effectively the player’s “first season”, to get a more apples-to-apples comparison.  In this chart, we consider a difference of about .4 or less to be a tie.  The results:

WAR per 150 games over first 5 (* or 6, ** or 7) seasons

Player	G	WAR/150G
Ted Williams	736	9.2
Mike Trout	652	8.5
Mookie Betts	644	8.2
Stan Musial	611	8.0
Willie Mays	610	8.0
Shoeless Joe Jackson**	601	7.9
Lou Gehrig*	613	7.8
Eddie Collins*	560	7.5
Joe DiMaggio	686	7.3
Wade Boggs	725	7.3

Trout and Betts are again right next to each other.  The gap between them is again just small enough to be considered a tie.  Only the great Ted Williams surpasses them.  They’re smack in the middle of a top 5 full of guys who are household names and some of the biggest legends of the game.

There are no other active players on this list.  The next two are again Albert Pujols, in 12th place with a 7.1 WAR/150, and Evan Longoria in 18th place with a 7.0 WAR/150.  Among the players of today, they are in a class by themselves.

I hope you are now convinced, as I am, that there is no significant difference at this moment in time between the greatness of Mike Trout and the greatness of Mookie Betts; the only real difference at this point is years of playing time.  This could, of course, change in the coming years, but as of right now, it’s a tie.  I just hope all the writers out there will catch on.

There is one that sort of already has.  In his article AL MVP Mookie Betts is the first real challenger to Mike Trout’s throne as baseball’s best player published last November, Mike Axisa points out some of this sustained success that Betts has had, and also compares Trout and Betts tool for tool, and concludes that Betts could stick with Trout for the long haul.  Good work, Mike.  You actually bothered to look.

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.

If it really drives you mad when the reliever who blew that lead also gets the win, then you’ll definitely 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 runs/IP – 1 R allowed = – 1 = runs ahead

Familia’s Runs ahead = 1 IP  runs/IP) – 0 R allowed = 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.

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
13	Manny Machado	.274
14	Eugenio Suarez	.274
15	Francisco Lindor	.273
16	Michael Brantley	.273
17	Christian Yelich	.270
18	Nolan Arenado	.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%
Manny Machado	23.0%	155	61.1%
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%
Nolan Arenado	24.6%	204	48.7%
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.

Balls in play per contact made, for TBpS leaders

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%
Manny Machado	55.1%	41	89.9%
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%
Nolan Arenado	48.9%	240	39.6%
Aaron Judge	49.3%	215	46.0%
Javier Baez	52.0%	113	71.7%

Balls in play per contact made, for HpS leaders

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%
Manny Machado	26.1	286	24.4%
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%
Nolan Arenado	25.7	315	16.7%
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.

You may disagree with me, but right now I see the American League’s 2018 Most Valuable Player award to have three clear top contenders: Mike Trout of the Angels (because of course), Mookie Betts of the Red Sox, and Jose Ramirez of the Indians. Right now (through Sunday’s games) they have fWARs of 7.6, 7.8, and 7.7, respectively. Only one other player in either league is above 5.2 (Francisco Lindor at 6.7). What’s that you say? Lindor deserves to be in the conversation? Well, he is surging a bit. Okay, you’ve convinced me. Let’s call this a four-horse race, including Lindor.

In this article we’ll attempt to determine how the MVP race will proceed from now through the end of the season. Before we get into that, though, let’s take a closer look at where the race is right now, by looking at some numbers other than fWAR, because of course there’s more to it all than just WAR.

Let’s start with some baserunning numbers. (I’m going to use a consistent order in these lists, and the reasons for my choice of order will become apparent later.) BsR here is an overall baserunning metric that considers not just stealing but also ability to take an extra base, avoid making outs when taking an extra base, and similar evaluations.

Baserunning of AL MVP candidates

Player	SB	rank	CS	BsR	rank
Mookie Betts	23	8 (t)	3	5.3	9
Mike Trout	21	11 (t)	1	5.5	8
Jose Ramirez	27	4 (t)	5	7.1	2
Francisco Lindor	19	14 (t)	6	1.2	60

They all steal a lot of bases, and except for Lindor, they all rank pretty highly on overall baserunning. In fact, Betts, Trout, and Ramirez are all pretty much elite baserunners.

How about defense?

Defense of AL MVP candidates

Player	Fielding	rank	Adjustment	Defense	rank
Betts	8.9	6; 1st of 22 RF	-3.9	4.9	32; 1st of 22 RF
Trout	1.1	67; 11th of 24 CF	-0.1	1.1	67; 12th of 24 CF
Ramirez	6.2	19; 3rd of 20 3B	1.3	7.4	19; 3rd(t) of 20 3B
Lindor	8.7	7; 3rd of 25 SS	5.1	13.8	4; 3rd of 25 SS

“Fielding” in this chart is UZR, the stat Fangraphs uses for determining number of defensive runs saved when compared to other players at the same position. To compare players at different positions, an “Adjustment” is applied based on the position played, and the number of games played there. This gives the overall number for Defense. I personally feel that these adjustments are too extreme in many cases, such as favoring shortstops too much, and punishing corner outfielders too much.
Lindor really shines here, adjustment or no adjustment. While Betts and Ramirez have good numbers for their positions, Betts seems to be punished for playing right field; he’s on his way to his third consecutive gold glove at the position, and yet he still ranks only 32nd overall in “Defense” despite being 6th overall in UZR, and tops at his position. Trout looks quite average on defense, despite having made a focus on improving it this spring; it’s the one area that doesn’t really add to his value (though it doesn’t subtract, either).

And now offense:

 

Offense of AL MVP candidates

Player	AVG	rnk	OBP	rnk	SLG	rnk	wOBA	rnk	wRC+	rnk
Betts	0.350	1	0.438	2	0.668	2	0.458	1	192	1
Trout	0.309	7	0.459	1	0.624	4	0.445	2	190	2
Ramirez	0.298	21	0.409	4	0.624	3	0.427	4	172	4
Lindor	0.292	30	0.372	22	0.561	11	0.393	11	149	9

It’s pretty clear that Betts, Trout, and Ramirez are (with J.D. Martinez) among the top 4 hitters in the game. When you break it down by hit type, Betts hits singles, doubles, and triples with greater frequency than the others; they hit home runs at a similar rate, with Ramirez ahead of the others; Ramirez and Betts have very low strikeout totals; Ramirez walks a lot, but Trout has an extremely high walk rate. When park factors are taken into account as with wRC+, Trout pulls to nearly even with Betts.

To sum up, Betts, Trout, and Ramirez are best-in-the-game hitters, while Lindor is next-tier, and still better than most teams’ best hitter.

Overall, Betts and Ramirez excel at everything; Trout excels at everything but defense; and Lindor excels at everthing but baserunning, at which he’s still above-average.

My question is, how will this three-horse race – uh, I mean four-horse race – proceed from here? Will one of these four players emerge and separate himself from the others by the end of the season? Will those who have been saying that the others have a long way to go to catch up with Trout’s excellence eat their words?

A lot of MVP voters will look at total WAR on the season as their primary metric for judging overall value on the season. I think it makes more sense (for players who have played at least most of the season) to look at the rate at which the top players have accumulated WAR, but I’m not an MVP voter, so we’ll try to predict overall WAR by two means.

1) Assume all players continue accumulating WAR at the same rate per game that they have been on average thus far this season, multiplying that by the expected number of games each will play, and come up with a projected WAR that way. (For elite players like these, we can treat WAR as a cumulative statistic; we can’t do that for replacement-level players whose WARs fluctuate between negative and positive values, therefore behaving like a rate statistic.)

2) Look at how each player has historically trended over the last two months of the season, and adjust the estimate upward or downward accordingly.

So, the first way. Here are the fWAR’s per 100 games and per 500 plate appearances, for each player thus far this year:

Pace of 2018 fWAR accumulation

Player	WAR per 100 G	rank	WAR per 500 PA
Betts	7.8	1	8.5
Trout	7.0	2	7.9
Ramirez	6.7	3	7.6
Lindor	5.8	4	6.2

Betts is showing some separation here, due to having played fewer games than the others. Let’s see if that separation carries over into projected WAR. We’ll predict the playoff-bound Indians and Red Sox, with somewhat comfortable leads in their division races, will rest their stars 4 games each the rest of the way, in preparation for the playoffs. Trout is currently injured and appears destined to return on Friday. After that, the Angels, whose playoff hopes have already faded pretty far, will likely give Trout rest to avoid further injury in games that don’t matter this September. So I’ll project him for 8 games off. This leads to the following projections:

Projected 2018 fWAR leaders at current pace

Name	Team G remaining	G proj	WAR proj
Mookie Betts	42	38	10.8
Mike Trout	45	41	10.5
Jose Ramirez	43	35	10.0
Francisco Lindor	45	41	9.1

But what if they don’t continue at the rates they’ve been at? Let’s look at some history.
Well I have some nice plots for you, but they’re not quite finished. For now, I’ll just show you an average of their last 3 complete years (or 2 years in the case of Jose Ramirez) of wRC+, broken out by month, in a table:

Name	Mar/Apr	May	Jun	Jul	Aug	Sept/Oct
Mookie Betts	96	120	125	113	130	141
Mike Trout	178	172	181	186	166	166
Jose Ramirez	126	117	134	120	119	185
Francisco Lindor	Not completed yet

The trend here appears to be that Trout’s offensive production drops off in August and September, his worst two months of the year; Betts’ production climbs through August and into September, his best two months of the year; and Ramirez’s climbs in September, his best month of the year. It’s hard to rely on this happening in any particular year, though. Each year, when you look at them individually, seems to follow its own unique pattern for each of these three players. However, it is a common thing for a player to be a habitually strong finisher, or a habitually poor finisher, so anticipating a downturn for Trout and upturns for Ramirez and Betts is probably as sound as anything else we could project. Especially given that Trout will be coming off an injury later this week, and will have little to play for in September.

Given that there’s only about a quarter of the season to go, the changes in trajectory won’t have a huge impact, so I’ll project modest 0.2 and 0.3 adjustments.

Name	fWAR proj
Mookie Betts	11.0
Mike Trout	10.2
Jose Ramirez	10.2
Francisco Lindor	8.8 to 9.4

So there you go. If we base things on fWAR, I’m projecting Mookie Betts will be this year’s fWAR leader. Of course I had to make some rather exact guesses to get this number, and real life has way more variability than is contained in the numbers I used, so consider these numbers to represent a most-likely outcome, but not a likely outcome. Still, there’s enough of a gap between Betts and the others that I would say it’s likely that he finishes the season as the fWAR leader.

Whether that earns him the MVP is another question. There are many who still claim that Betts and Ramirez haven’t yet reached Trout’s level of excellence, despite the evidence right in front of us. There are others who ignore baserunning and defense, and put achievement of the triple crown above all other offensive achievement. If J.D. Martinez even comes close to winning the triple crown, which he’s currently close to, then he may steal a lot of votes from these more deserving players, despite adding no value on defense or on the bases.

Mike Trout’s AL MVP win yesterday was preceded by a lot of talk about who really was the more valuable player in 2016, Mike Trout or Mookie Betts. I’ve noticed, though, a lot of those assessments didn’t have the facts quite right. Others overlooked some things that matter. Some may look at the competing lists of WAR numbers, see that Trout is ahead of Betts on both of them, and just call it for Trout. I say there’s more to it than that. So I wrote this article to try to ensure people have the comparisons correct, and to point out what they may be overlooking.

One thing about WAR is that there are at least two versions out there (the FanGraphs version and the Baseball-Reference.com version), and the variances in the different versions show that WAR is not a perfectly calculated statistic. Small differences in WAR leave room for further analysis, so I think it’s worth breaking down the two players based on each of their five tools. Beyond that, I’ll look at traits that don’t factor into WAR calculations but still impact a team’s win totals (clubhouse presence) and a ballclub’s revenues (fun to watch, and likeability). Though this article is focused on who Trout and Betts were in 2016, I may reference some things that happened in earlier seasons as examples.

I’ll rate these loosely using the following categories. As a rule of thumb, these correspond approximately to the following percentiles of performance:

Average	43rd to 57th percentiles
Above average	58th to 70th percentiles
Well above average	71st to 84th percentiles
Excellent	85th to 94th percentiles
Elite	Top 5%

Note that one of the tools is traditionally called “Hitting for average”. I’m updating this to “Reaching base”, as these days on-base percentage is considered more important than batting average. Another is traditionally called “speed”. I’m updating this to “baserunning”.

First the results, followed by the analysis. After looking at the 2016 numbers for both players and mixing in anecdotes and commentary I’ve come across, and actual play that I’ve witnessed, I came up with these assessments of their five tools:

Tool	Trout	Betts
Speed/baserunning	Elite	Elite
Hitting for average/on base	Beyond Elite	Excellent
Hitting for power	Well above average	Well above average
Arm strength	Average	Excellent
Fielding	Average	Elite

Looking at traits that don’t contribute to WAR but do contribute to a ballclub’s bottom line, I came up with these results:

Trait	Trout	Betts
Glue – clubhouse presence	Above average	Elite
Fun to watch	Above average	Elite
Likability	Well above average	Excellent

Let’s break these down.

Speed/baserunning
Both players are elite. On stealing bases, they’re about the same; most teams would prefer to have Betts’ 26 steals versus 4 times caught stealing over Trout’s 30 steals versus 7 times caught stealing, but this really is kind of a toss up. Fangraphs’ BsR agrees. BsR puts a value in runs produced on all aspects of baserunning, including base stealing prowess, extra bases taken, outs on the bases, and avoiding double plays. Mike Trout had the fourth best BsR in 2016 among all players, at 9.3. Betts had the third best at 9.8. It’s basically a toss-up.

Hitting for power
While most probably think of Trout as more of a power hitter than Betts is, that’s not really true anymore. Per plate appearance, Trout and Betts hit home runs and triples at the exact same rates in 2016. Betts hit doubles more frequently (5.8% to Trout’s 4.7%) and singles more frequently (18.6% to 15.7%). If we divide by at bats instead of plate appearances, however, Trout’s power numbers start looking better, because we’re not including his very frequent walks and hit-by-pitches in the divisor. If we were to look at slugging percentage alone, we might give both Betts and Trout an “Excellent” for power; but when you look at stats like ISO that isolate power from on-base ability, both end up in the well-above-average range instead.

Reaching base
Trout blows Betts out of the water in walk rate, 17.0% to Betts’ 6.7%. Also Trout’s hit-by-pitch rate of 1.6% was much higher than Betts’ 0.3%. Trout’s 20.1% strikeout rate was much worse than Betts’ 11.0%, though.

So at the plate, the main difference in results is Trout’s extremely high on-base rate due to taking so many walks, and the main difference in approach is that Betts puts the ball in play a lot, and Trout does not. Only about 4% of qualifiers put the ball in play more frequently than Betts; only about 7% put the ball in play less frequently than Trout.

Trout’s .441 on base percentage led all of baseball. In the American League, it wasn’t even close. The next several players on the list were all clustered around .400. In the National League, only Joey Votto’s .434 came close. It’s obvious Trout deserves elite status in this category, but I though his separation from the pack warranted a little more, so I gave him a “beyond elite” instead.

Arm strength
There’s a component of UZR that measures arm strength not just by velocity but also accuracy. Betts qualifies as “excellent” for arm strength based on this, and based on the eye test (such as when he threw a perfectly-placed laser beam of a throw to gun down one of the game’s fastest runners, Kevin Kiermaier, trying to take third on a fly out to right). Trout actually rates below average on this, though not by a whole lot. I’m upping that to “average” based on the anectodal evidence that he’s improved his arm strength to be average or a little above average.

Fielding
Some metrics have Betts as the best defender in baseball in 2016. Others have him a few notches down. But they certainly put him at an elite level, even when you remove arm strength from the equation. Trout’s fielding was actually average in 2016, even if you separate this from arm strength. This may surprise some who think of him as an above average fielder; they may have formed this impression based on his rookie season in which he was above average as a fielder. He hasn’t been better than average since, however.

Clubhouse Presence
Those are (a slightly altered version of) the so-called “five tools”. Now some may disagree, but I really do think there is a “sixth tool” a player can have to impact his team’s win total, which we can call “clubhouse presence”. It’s anything about a player that makes his teammates want to give their best effort during preparation to play and during actual gameplay. It affects win totals because, in my opinion, the results achieved on the field are a product of three things: skill, preparation, and effort, and clubhouse presence impacts two of those.

Mike Trout may or may not set a good example to his teammates by working hard on preparation. I don’t know. I do know he worked hard on his one area of weakness, his throwing arm, to eliminate the weakness. One thing I know about Mookie Betts is that he is constantly asking people questions on how to improve, and that will certainly set a tone that players can be working hard on preparation.

Trout is a positive, good-natured guy who plays Pokemon Go and Nerf basketball in the clubhouse. Certainly not a clubhouse drain. But at the same time he doesn’t seem to be very outgoing. Betts is the kind of guy who’s friends with everybody. He’s got friends on his team and on all the other teams. Having his personality in the clubhouse and on the field makes baseball fun for everyone, and that helps get maximal effort from the players on the team.

So I’m giving Trout an “above average” on clubhouse presence, but Betts an “elite”, because his type of personality is actually pretty rare.

Fun to Watch and Likability
These last two traits are all about the fans. What, aside from winning games, hitting home runs, etc. gets fans to fork over their money to watch a team play? To me, it’s being fun to watch and being likable.

Trout climbing the outfield fence to rob a home run makes him fun to watch. Mookie Betts stealing two bases on one play (with no error), or alertly taking an unmanned second base on an infield single, these are unique plays that nobody’s ever seen before, and help to make him an elite for this category. His catches on defense can be quite spectacular and acrobatic, too, such as the time he almost fell into the Fenway Park bullpen while making a game-ending, home-run robbing grab to preserve Rich Hill’s masterful shutout late in 2015. The excitement he visibly displayed while running back into the infield with the ball held high in his glove is an example of the enthusiasm for baseball that always radiates unrestrained from Betts. Oh, and who did he steal that home run from … that’s right, it was none other than Mike Trout. So I guess they kind of teamed up on that one. Trout probably had to smile at that.

Trout smiles a lot. So does Betts. But I’d say that Betts is the one with the “winning” smile. Electric. Unrestrained. Wins you over, and wins fans over. They’re both likable, but Betts is in a higher category of likable.

Summary
So there you have it. Looking over these ratings, Betts is one of the best in the game in every category except hitting for power, and he’s approaching being one of the best in that, too. He’s pretty much everything you could want in a player. Trout is definitely better at the plate, which is the most important part of achieving a high WAR. But his “secret weapon” has been his excellent baserunning, and Betts is his match there. In every other aspect of the game, the fielding aspects and also the less tangible but still valuable ones, Betts is his superior.

I’m not concluding that Betts should have gotten the MVP over Trout. But there’s been a lot of debate on that topic, and there may be some yet to come. But it seems to me that a lot of people have wrong ideas on how close Betts and Trout are on baserunning and power hitting, and many overestimate Trout’s fielding ability. There also never seems to be discussion of clubhouse presence, likability, and being fun to watch, and those do carry value. I wrote this to help ensure that discussion doesn’t miss any of these points.

I hope to hear some good discussion on this now!