Matthew Lengua
Data Visualization by Peter Majors
Introduction
Despite the fact that strikeouts rates are on a meteoric rise, hitting in two-strike counts may not be confined by the proverbial "glass ceiling" that you would expect it to be limited by. This is particularly apparent when considering the impact-over-expectation that hitters can produce in this state of the count.
As league-wide offense has slowed, hitters abandoning a traditional "two-strike approach" has made the game even easier for pitchers to dominate. The count reaching two strikes already implies that control of the at-bat has shifted into the pitcher's hands. However, most hitters' unwillingness to adjust and master a two-strike approach is the "last straw" in making success in those counts nearly impossible to achieve for offenses.
In two-strike counts, the pitcher has a far greater selection of pitch types and locations at his disposal than in non-two-strike counts. Rarely, unless a pitcher makes a woeful mistake, does a hitter get a pitch that is begging to be hit a country mile when he has two strikes on him.
In fact, more times than not, the hitter is not even receiving a strike. In two-strike counts during the 2016 season, 57.3% of pitches thrown were categorized as being out of the strike zone. Conversely, in non-two-strike counts, 48.3% of all pitches were thrown out of the strike zone. Here is a graph depicting those splits over the past five seasons:
Moreover, hitters are also more likely to expand the zone in two-strike counts and inclined to chase more often in two-strike counts than they would in non-two-strike counts.
In that same 2016 season, 147 qualifying batters swung at 23% of the pitches thrown outside the strike zone in non-two-strike counts. In two-strike counts, those same hitters chased at a staggering 41% clip. Here is a graph detailing those splits over the past five seasons.
Despite teams promulgating the importance of selectivity, On-Base Percentage, and plate discipline now more than ever, qualifying batters still chased 40.3% of pitches with two strikes in 2019.
When evaluating each individual season from 2016 to 2019, no qualifying player, in a sample of 566, chased at a lower rate in two-strike counts than in non-two-strike counts.
If hitters are consistently chasing more frequently in two-strike counts than in non-two-strike counts, what a batter can produce on a pitch he does chase with two strikes could tell us something about how effective the hitter will be in that state of the count.
Just like a hitter can become consistently productive in non-two-strike counts, focusing on his mechanics, timing and swing path, there is sufficient evidence that hitters can similarly develop certain traits that allow them to consistently perform with two strikes.
One of those traits is avoiding swings & misses on pitches outside of the strike zone.
Background
A metric gaining recognition quickly, known as run expectancy values, is a unique indicator of performance, using the measuring stick of expected performance (in terms of runs scored) to assign a run value to the result of each pitch of an at-bat. The metric uses "base-out" states, the ball-strike count and the location of the pitch thrown among other factors to identify the level of performance that would be expected of a player in a particular situation.
If a player performs above what is expected of him, the player is credited with producing positive run expectancy for that particular pitch. If a player produces below the predetermined expected level of runs, then he will be credited with a negative run value, decreasing his run expectancy output. Run values are applied to all pitches in an at-bat, including takes, swings & misses, and foul balls.
For example, last season, the highest run expectancy value assigned to an individual pitch took place on a Tomás Nido grand slam. Nido's grand slam produced a run expectancy of 3.586 runs greater than the predetermined expected run output in that particular situation.
To calculate the run expectancy produced on a particular pitch, a team's run expectancy before the pitch is subtracted from the number of runs produced on the pitch, factoring in several other considerations such as the pitch's location. After the result of the pitch, in Nido's case a grand slam, the run expectancy of the new "base-out" state and count are added to the difference, giving us an estimate for the expected runs accumulated based on the result of that one pitch.
On its face, Nido's home run was not unique. There were 45 grand slams hit in the Majors in 2020. However, very few of those grand slams were hit when the count was 0-2, there were two outs, and the pitch location was not in the heart of the plate.
Nido's home run came on a pitch in the "shadow" region. The above diagram depicts the different attack regions which Statcast tracks for every pitch of a plate appearance. The "shadow" region represents the perimeter of the strike zone, not a location where hitters tend to do their most damage.
Run values are also assigned to pitches where the ball is not put into play, accounting for a hitter's ability to take a close pitch, failure to swing at a good pitch to hit, or foul off a difficult pitch.
Put more succinctly, using run expectancy levels, the playing field for all batters being judged against its scales, accounting for the totality and context of a player's contribution, not just the number of hits or total bases he accumulates in a given season.
Therefore, using run expectancy is the optimal methodology for judging the quality of player performance, especially over extended periods of time.
Before digging into the numbers that prove that cutting down on swings & misses on pitches out of the zone in two-strike counts can help a player perform consistently, it is important to first prove whether or not two-strike performance can be repeated and stabilized over time.
To prove that two-strike hitting could stabilize over time, I looked at each hitter who recorded at least 250 at-bats in consecutive seasons dating back to 2016. To control for a player's two-strike performance increasing or decreasing as a product of the player improving or worsening as an overall performer, I looked at the difference between each player's production in two-strike and non-two-strike counts for pairs of consecutive seasons in which they recorded 250 or more at-bats.
Using non-two-strike performance as a baseline controlled for players who due to aging or their overall skills declining saw their performance with two strikes, likewise, erode.
The four pairs of consecutive seasons all showed a positive relationship when correlating the run expectancy differentials produced. This means that a player's prior performance level with two strikes (measured by calculating the difference in performance in two-strike and non-two-strike counts) was a solid predictor of how well he would perform in that same statistic in the subsequent season.
In 2020, with the season shortened to 60 games, the correlation between a player's performance in two-strike versus non two-strike counts for 2019 and 2020 was not as strong due, in part, to the vast difference in games played during the two seasons.
R-value = .35
Note: In the scatter plot, a positive value indicates the player produced more runs above expectation in two-strike counts than in non-two-strike counts.
In the scatter plot above, the difference in run expectancy produced by individual players in two-strike versus non-two-strike counts is charted for consecutive seasons (2018 & 2019). 2018 performance is charted on the x-axis and 2019 performance is charted on the y-axis for the players who qualified for the analysis.
The difference in a player's performance in two-strike and non-two-strike counts was, in fact, a strong predictor of the difference in performance that the player produced in the following season, as evidenced by an R-value of .35 which indicates a strong direct correlation between 2018 and 2019 performance.
If a player's performance can stabilize in two-strike counts, it is reasonable to believe that there are common traits attributable to the many players that have succeeded in those counts consistently over time.
Analysis
I parsed out each MLB player's run expectancy output in non-two-strike and two-strike counts for each season from 2016 to 2019. Knowing that batters' backs are against the wall in two-strike counts, the predetermined level of expected runs on two-strike pitches is generally lower than the performance expectation would be in a more favorable state of the count. Therefore, the playing field is leveled and we can evaluate which players are, relatively speaking, performing above or below the level of performance that is expected of them.
Of the 121 players who recorded at least 250 at-bats in each season from 2016-2019, here is a cumulative list of the top performers in two-strike counts using the run expectancy metric:
Of the sample evaluated, the average runs over expectation produced by a player in two-strike counts during the 4-year stretch was 18.29 runs.
At first glance, the players listed as the ten most productive two-strike performers are ones you would expect to see. All ten of the players have been in MVP conversations and are considered among the best hitters in the sport on a seasonal basis.
Upon further review, we see a common thread between these players which sets them apart from their peers and other hitters who are considered as dangerous, but incapable of producing with two strikes.
To further examine the potential similarities amongst hitters increasing their team's run expectancy in two-strike counts, let's investigate a measurable known as out-of-zone swing & miss rate (OZ swing & miss %) - which represents the number of pitches a hitter makes contact with, including foul balls, outside of the strike zone divided by the number of pitches they see outside of the strike zone. Drilling down further, let's look specifically at players' two-strike out-of-zone swing & miss rates.
If we know that hitters expand the zone in two-strike counts, then there could be great value in a player who is able to avoid whiffing at pitches that he would be far more likely to chase when the count reaches two strikes.
R-value = -.46
The 121 players who have amassed 250 official at-bats in each season from 2016-2019 are plotted based on two metrics in the scatter plot above, out-of-zone swing & miss rate with two strikes and run expectancy produced with two strikes. Highlighted in yellow are the 10 players (see the previous table) who have produced the most runs above expectation in two-strike counts.
An R-value of -.46 denotes a strong inverse relationship between out-of-zone swing & miss rate and run expectancy produced in two-strike counts^. In a dataset of 121 MLB players with at least 1000 at-bats during a four year-stretch, if a clear relationship exists between low out-of-zone swing & miss rates and increased run expectancy production in two-strike counts, we can assume that a relationship between these two variables existed before the 2016-2019 seasons and will continue to exist in the future.
Of the players in the sample, the four-year average out-of-zone swing & miss rate with two strikes was 35.4%. Each of the top ten players in run expectancy produced with two strikes had a lower out-of-zone swing & miss rate with two strikes than average. Mike Trout, at 34.9%, was the only player in the top ten whose out-of-zone swing & miss rate with two strikes approached the average.
Looking at each of the hitters who placed in the top ten out-of-zone swing & miss rate with two strikes on a seasonal basis, we can see that their ability to make contact on pitches out-of-the zone is not a result of chance - but rather skill.
*League average amongst players who amassed 250 at-bats in each season from 2016-2019. Highlighted in green are the seasons when the player performed better than the average. Highlighted in red are the seasons in which the player performed worse than the average.
In 37 out of the 40 seasons individual player seasons where data was collected (94%), the game's best two-strike hitters swung & missed out of the zone with two strikes below the league average in a given season.
While the most productive two-strike hitters consistently increase their team’s run expectancy in those counts, there are some instances where the top performers were not above average in terms of out-of-zone swing & miss rate. For each season from 2016-2019, the top 10 players in runs produced above expectation with two strikes is listed, accompanied by their out-of-zone swing & miss rate with two strikes.
*The dotted line represents the seasonal MLB average out-of-zone swing & miss rate with two strikes for players who recorded at least 250 at-bats in each season from 2016-2019.
In 33 of the 40 seasons evaluated (83%), the best producers of runs above expectation in a particular season swung & missed less at pitches out of the zone in two-strike counts than the average player.
Conversely, here are the charts listing the ten least productive players in two-strike counts in each season from 2016-2019.
*Similar to the preceding chart, the dotted line represents the MLB average out-of-zone swing & miss rate with two strikes for players who recorded at least 250 at-bats during the 2016 season
In only 5 of the 40 seasons (13%), the players producing the least amount of runs above (below) expectation in two-strike counts were able to avoid swinging & missing at pitches outside of the strike zone at a better (lower) than average clip.
Looking only at the best and worst performing two-strike hitters from 2016-2019, we may infer that out-of-zone swing & miss rate in two-strike counts plays a pivotal role in determining the quality of player performance in those counts.
However, looking at the extremes does not tell the whole story. To gain a more complete understanding of this phenomenon, we should instead look at all of the qualifying players for a particular season.
R-value = -.42
Looking further at player performance in two-strike counts for 135 qualifying hitters during the 2019 season, of the 69 players who swung & missed at pitches out of the zone in two-strike counts less frequently than average, 48 or (69.6%) produced more runs above expectation than the average amount of run expectancy qualifying players produced (5.69). For reference, the average out of zone swing & miss rate with two strikes for among qualifying players in2019 was 36.7%.
Plotting out-of-zone swing & miss rate with two strikes against run expectancy produced in two-strike counts shows a strong inverse relationship with an R-value of -.42 between the two metrics for all players being tested. Put simply, the less a hitter swings & misses at a pitch out of the zone with two strikes, the more runs above expectation he is able to produce in two-strike counts.
To further solidify this assessment, of the 299 qualifying players who swung & missed on pitches out of the zone with two strikes at a lower than average rate in a given season from 2016-2019, nearly 59% of those players produced more runs than the average player produced with two strikes in that season.
Conclusion
Conventional wisdom has ultimately led most people (myself included) to believe that two-strike counts, and the sub-.525 OPS hitters have induced in those counts, are the death of the batter in the box.
It would have been no surprise, based on the significantly higher level of total production hitters put forth in non-2-strike counts, to discover that players increase their teams' chances of scoring runs and winning games more significantly when the count has zero or 1 strike.
In actuality, both of those generally accepted assumptions have been turned on their heads thanks to the advent of run expectancy data. In fact, to truly test the power of the run expectancy metric, I took a look at the number of runs above (below) expectations that were produced by the entire league in non-two-strike counts compared to run expectancy produced in two-strike counts.
The results were surprising to say the least:
When taking into consideration every plate appearance that was recorded in the Major Leagues during the past five seasons, more runs above (below) expectation were produced in two-strike counts than in non-two-strike counts.
Remember, the run expectancy metric does not solely measure how many total runs are being produced on a particular pitch. It accounts for the runs that are produced in relation to the runs that are expected to have been produced based on the context of the situation.
In two-strike counts, the expected production of runs diminishes from the level which it stood in a zero or one-strike count. Meaning that if we hold everything else equal, a two-strike single is increasing a team's run expectancy by a greater amount than a zero or one-strike single would.
If batters are indeed able to produce just as many, if not more, runs above expectation in two-strike counts than in non-two-strike counts, finding out what enables players to produce in two-strike counts is valuable.
As we have just discovered, there is a strong correlation between out-of-zone swing & miss rate and run expectancy produced in two-strike counts. It turns out that avoiding swings & misses on pitches out of the strike zone is a key driver of players being able to produce in two-strike counts and with a greater run expectancy produced than when the count has zero or one strike.
Such an unexpected phenomenon can be explained further by simply looking at swings & misses and foul balls. The difference in the result of those two outcomes on run expectancy is far different in a two-strike count than in a non-two-strike count. Whereas in a zero or one-strike count, a foul ball and swing & miss will impact the count similarly, a foul ball and a swing & miss in a two-strike count have starkly varying effects. After a foul ball in a two-strike count, the count remains the same and the team's run expectancy will not fluctuate. If the batter swings and misses in a two-strike count, he strikes out and the team's run expectancy decreases.
This implies that what separates the best two-strike hitters from weak two-strike hitters, by and large, is their ability to put the bat on the ball. More specifically, since pitchers are significantly more likely to throw pitches outside of the strike zone in two-strike counts, putting the bat on pitches off the plate should be of primary concern.
In the best-case scenario, fouling off a pitch out of the zone and staying afloat in the plate appearance affords players the opportunity to get a pitch in the zone that he can drive as the at-bat continues. Now, a pitch that a hitter spoils not only means the difference between the at-bat continuing and him striking out but also was the precursor to the hitter hitting a ball into a gap, producing runs for his team.
All in all, a hitter's ability to make contact on pitches off the plate in two-strike counts is what can flip the script on a state of the count that has forever been dominated by pitchers. The impact that offensive two-strike success can have on a team's chances of increasing their run output strengthens the case that two-strike hitting should not be abandoned but healthily incorporated into each club's approach at the plate.
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