How the League Adjusts to Hitters Over Time

Mets first baseman Ike Davis has seen the number of fastballs thrown to him drop significantly since his rookie season in 2010. In that year, 57% of the pitches thrown to Davis were some type of fastball. So far in 2012? Only 51%. There have been only 30 seasons between 2007 and 2011 where a hitter with more than 100 plate appearances saw a lower percentage of fastballs in a season than Ike this year — and only five where a player accumulated more than 500 plate appearances.

Clearly pitchers are adjusting to Davis, altering their approach based upon Davis’ perceived offensive strengths and weaknesses. This got me thinking about the extent to which major league pitchers adjust to hitters from year to year. Was this change significant, or more common based on the normal adjustments hitters can expect to see from year to year.

As a first cut, I decided to look at changes in the pitch types that batters faced in consecutive years. Throwing hitters a different mix of pitches (i.e. fastballs, curveballs, sliders, etc.) is just one way the league can adjust. Pitchers can alter location, sequence and speed. However, the data was more readily available for pitch types, so the choice was made to focus there first. I decided to use the pitch-type distributions that are based off of PITCHf/x data. This allowed me to collect data on hitters with 100 at least plate appearances each year from 2007 through 2011. For reference, here are pitches as classified by PITCHf/x:

Abbreviation Description
FA Four-seam Fastball
FT Two-seam Fastball
FC Cutter
FS/SI/SF Sinker, Split-fingered Fastball
SL Slider
CH Changeup
CU Curveball
KC Knuckle-curve

Now, there are obviously some coding issues that come into play. PITCHf/x and the classification algorithms that it uses aren’t perfect and have changed a bit over the years. For example, it isn’t uncommon for sliders and cutters to be mistaken for each other. This is a problem, but not a fatal one as long as we acknowledge it up front. To get a first look I decided to group all fastballs together as this might alleviate some of the potential coding issues within the fastball category. The table below shows the results for all hitters with at least year-two data and for those with year-two and year-three data:

Sample N Fastball% YR1 to YR2 Fastball% YR1 to YR3
All hitters with only two years of data 142 .620** NA
All hitters with at least three years of data 367 .586** .519**
**Sig at the .01 level

For hitters with only two years worth of data, we see a highly significant correlation between the percent of fastballs seen (.620). The correlation drops a bit, to .586, for hitters with three years of data. Additionally — for hitters with three years of data — we see that the relationship decreases further once we get to that third year. This likely reflects the fact that hitters who only managed to accumulate at least 100 plate appearances in two consecutive years were lesser hitters and, therefore, required less adjustment from the league. These hitters eventually drop out of the sample and we’re left with hitters who perform well enough to require additional approach changes from opposing pitchers. The sample above includes all hitters, but is there a difference between established hitters who have major league track records and rookies who haven’t accumulated a significant number of plate appearances? My initial hypothesis was, yes, we should see a greater adjustment by the league in terms of rookies versus players who have already established their habits and tendencies. To test this I calculated separate correlations for hitters whose first season in the sample was their first with at least 100 plate appearances. Here are the results for hitters with two years of data, compared to those with three years:

Sample N Fastball% YR1 to YR2 Fastball% YR1 to YR3
Non-rookies with only two years of data 88 .569** NA
Rookies with at least two years of data 54 .710** NA
Non-rookies with at least three years of data 291 .575** .527**
Rookies with at least three years of data 76 .633** .508**
**Sig at the .01 level

Contrary to what I expected, the league feeds rookies a similar percentage of fastballs between year one and year two than for non-rookies, regardless of which sample we look at. But the adjustment from the first year to the third year is much larger for those who were rookie hitters. My guess here is that pitchers need more than the first year’s worth of plate appearances to update their approaches. The average number of plate appearances in players’ rookie years was 359, compared to 418 in the second season and 450 in the third. For non-rookies, the average plate appearances were 404, 405 and 438. And if we assume that year one in the data set for non-rookies is at least their second year in the league, we can assume that the league as on average 763 plate appearances to refer to for non-rookies going into year two, 112% more than for rookies. (Remember, year one in my data set isn’t the first season in the league for non-rookies, just the first season in the data set. That means pitchers have had a longer look at those hitters than rookies.)

I should also note, though, that the differences observed rookies and non-rookies were not clearly significant. If you compare the correlations for each category, the closest we come is between rookies and non-rookies with only two years of data (p-value of .087). For rookies and non-rookies with three years worth of data the p-value was .24. And what about off-speed offerings? Obviously, if the fastball percentages are fluctuating yearly, we should see changes in off-speed percentages. For that, I decided to use the specific pitch-type categories — rather than a composite. The table below shows correlations for sliders, curveballs and changeups for all hitters with data for only years one and two, as well as those with data for all three years:

N YR1 to YR2 YR1 to YR3
SL% (pfx) – two years only 142 .499** NA
SL% (pfx) – three years 367 .673** .663**
CU% (pfx) – two years only 142 .335** NA
CU% (pfx) – three years 366 .363** .240**
CH% (pfx) – two years only 142 .650** NA
CH% (pfx) – three years 367 .595** .541**
**Sig at the .01 level

At first glance, sliders and changeups pop as the most consistent off-speed offerings that hitters face each year. Sliders in years one and two have a .673 correlation, and the correlation between years one and three is only slightly different (.663). Changeups also show a fairly strong consistency, even three years out. Curveballs, however, start with a low correlation (.363) and get even less consistent by year three (.240). As with fastballs, I wondered whether there would be a difference between rookies and non-rookies. Here are the results:

N YR1 to YR2 YR1 to YR3
SL% (pfx) – Rookies 76 .726** .650**
SL% (pfx) – Non-rookies 291 .660** .669**
CU% (pfx) – Rookies 76 .216 .343**
CU% (pfx) – Non-rookies 290 .405** .215**
CH% (pfx) – Rookies 76 .577** .600**
CH% (pfx) – Non-rookies 291 .599** .531**
**Sig at the .01 level

Outside of sliders, non-rookies showed a higher correlation between the off-speed pitches they faced between years one and two. Sliders were highly correlated for rookies between years one and two (.726); the relationship between year one sliders and year three sliders for non-rookies was nearly identical (.669 vs. .660). Curveballs were inconsistent regardless of hitter type. Rookies also saw their year-three correlations increase for both curves and changeups, versus year-two. Both were higher than non-rookies.

As with fastballs, I wanted to see if the difference between the rookie and non-rookie correlations was significant. It was but only in the case of curveballs between years one and two (p-vale of .05).

There are a factors that likely impact the data and patterns we see. One of those factors is age. The percent of fastballs faced increases in a fairly linear fashion as batters age. This increase corresponds somewhat to hitters’ run-creation ability as they age. Take a look at the graph below:

The percent of fastballs faced increases slightly until about age 26. Pitchers then throw hitters fewer fastballs until about age 30. The rate then increases dramatically as hitters age. If we think about a hitter’s productivity through time then this makes sense. Hitters who last until age 30 are likely better-than-average and will have hit their offensive peak during their age-25 through age-29 seasons. As these hitters begin to age, they’re likely losing bat speed and making pitchers throw them more fastballs. There are obviously exceptions, but the pattern passes the initial sniff test.

As an example, here is Justin Upton’s fastball percentage versus his wRC+ since 2007:

The pattern resembles the story above. As Justin Upton entered his peak offensive year, he saw a decrease in the percentage of fastballs thrown to him. So far this season, he’s seen fewer fastballs — and while his wRC+ is currently below average, my guess is he’ll finish his age-27 season north of 100.

So back to my original question about Ike Davis: Is the change in the percentage of fastballs he’s seeing that uncommon? The answer appears to be no.

While the low rate of fastballs is rather unique, the way in which the league adjusted to him between years one, two and three is in line with what we see in general. Rookies see a higher correlation between their year-one and year-two fastballs and less of a correlation between years one and three. Davis’ percent of fastballs seen has been 56.8%, 56.7% and 51.4%. Also, the 5%+ decline in fastballs seen is far from an outlier, as there have been 69 seasons where hitters saw the same or greater decline in fastball percentage since 2007, not including Davis.

Obviously, there is more to adjustments than pitch-type distributions. I’ll eventually get a chance to look at location and velocity, but if PITCHf/x and database-savvy readers want to tackle this, please jump in.

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Bill leads Predictive Modeling and Data Science consulting at Gallup. In his free time, he writes for The Hardball Times, speaks about baseball research and analytics, has consulted for a Major League Baseball team, and has appeared on MLB Network's Clubhouse Confidential as well as several MLB-produced documentaries. He is also the creator of the baseballr package for the R programming language. Along with Jeff Zimmerman, he won the 2013 SABR Analytics Research Award for Contemporary Analysis. Follow him on Twitter @BillPetti.

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SUPER interesting, Bill. Thanks for this.