Seeing more pitches rarely improves batter performance

What makes for a quality at-bat in college baseball?

Baseball is a game of a million variables flying around all at once, from pitch clocks and pitcher fatigue to defensive shifts and umpire error. Through all these calculated and random events, there are less conventional statistics that consider nuanced aspects of the game, such as the number of pitches seen. One such metric is Quality At-Bats (QABs), which remains integral to the game’s coaching philosophy. Quality At-Bats emphasize the importance of a batter’s approach and plate discipline, aiming to build a more potent offense (Austin Leonard, 2021).

How Meaningful are Quality At-Bats?

Quality At-Bats most commonly are defined as at-bats resulting in on-base events, hard contact, or an extended pitch count within that plate appearance. QAB’s are not officially defined and have different qualifications (depending on what coach you ask, some say 6+ pitches, others 8+). QAB’s become even more ambiguous to quantify when determining what the cutoff is for hard contact or a “barrel,” (and what difference does a barrel make if a hit sneaks through at 65 mph exit velocity and a line drive at 100 mph is caught (MLB.com, 2022)?) The importance of a QAB, however, may lie in some results that cannot be quantified in “productive” statistics like batting average (AVG) or slugging percentage (SLG).

Coaches that I have played for over the span of my career have emphasized QAB’s, particularly instructing players to “see more pitches.” Elevating pitch counts should produce better results due to improved pitch recognition, increased pitcher fatigue, more chances for stolen bases and wild pitches, and a higher chance of getting on base (walks can only occur on ball 4, after all). A Quality At-Bat signals to the hitter that they are in a good position to succeed. It follows naturally that the more often a player puts himself in a good position to succeed, the more often they will succeed. Managers at the highest level of baseball understand this concept better than anyone. Jerry Weinstein, a lifelong manager between the MLB and minor league affiliates, claims that an offense that is able to see more pitches will be more productive (Weinstein & Alston, 1998). He structures his entire coaching and managerial strategy around the idea of extending pitch counts, even to the point of causing chaos behind the plate with fake bunts and fake breaks by base runners to encourage extra and unnecessary movement by the catcher, hoping to result in a missed strike call by an umpire, effectively forcing more pitches.

This post investigates the emphasis on “seeing more pitches” by analyzing the effect of extended pitch counts on a player’s and team’s offensive statistics. Guilliams (2012) found that the most productive results for an offense came early in the count. Swinging early in the count goes against the QAB concept that seeing more pitches will provide better offensive results. At this point, there are two trains of thought that are in conflict. Seeing more pitches increases on-base percentage (OBP) but has a negative effect on AVG and SLG (Figure 1). Should hitters swing early for immediate impact, or exercise patience to increase OBP? Moreover, does the significance of seeing more pitches extend beyond individual performance metrics, impacting inning-long and game-long strategies in collegiate baseball? This study investigates the importance of seeing pitches and attempts to determine the approach that provides the most offensive value.

Figure 1. All complete plate appearances (PA) from the 2023 and 2024 Division 1 college baseball seasons. After the second pitch of a PA, both AVG and SLG plummet, never to recover. In contrast, after the third pitch of a PA, OBP starts to climb continuously for each additional pitch. At higher pitch counts, the statistics become less certain because there are fewer data to compute the mean from (the y2-axis at the top of the plot tells you the total number of PA’s for each pitch count).

Methods

Thomas Severini measured MLB P/PA and its effect on individual and team stats. He found that the higher an individual's P/PA is, the higher his OBP will be (naturally). He also found that team OBP does not increase with P/PA. I look at AVG and OBP to conduct my own research, building on the results of Severini (2020) and Guilliams (2012)’s. I also use SLG to gauge how well hitters are squaring-up the ball. I also conducted all the analyses in this post with wOBA, wRC+, and JOPS+, but found similar results and chose to stick with the simplest, most tangible statistics.

I use 2023 and 2024 data from the TrackMan stadium instrument that is installed in over 170 collegiate ballparks. Each data entry is tied to a specific player and logged in sequential order of game flow. The data most utilized in this analysis are related to player identification, pitch count information, plate appearance results, and personal batting statistics.

Results

Player statistics in isolation provide a first look at how pitches seen per plate appearance (P/PA) impact individual offensive production. Figure 2 shows that, despite significant correlations between P/PA and AVG and OBP, P/PA explains ≤3% of the variance in these statistics. Another way to phrase this result is that, on average, a player should not expect to improve their AVG or OBP simply be seeing more pitches.

Figure 2: Pitches per Plate Appearance (P/PA) has significant correlation with AVG and OBP. However, P/PA only explains 2% of the variance in AVG and 3% in OBP. The trends in both AVG and OBP are consistent with MLB results conducted by Severini (2020). P/PA has no correlation with SLG and all variances in the data can be attributed to randomness.

To get a grasp of how P/PA truly affects offensive output, we need to look beyond season wide statistics of isolated players. We turn to inner-inning dynamics that examine the impact of one player’s plate appearance on another’s.

Inner-Inning Dynamics I

As a hitter, excitement and anticipation build as a plate appearance extends. A common phrase in baseball, credited to Hall of Fame manager Tommy Lasorda, is that “hitting is contagious” (Baseball Almanac, 2024). A batter who has just watched his teammate have a QAB should be more confident as he walks to the plate. It also follows naturally that a pitcher, who has thrown more pitches, will begin to feel fatigue and frustration, especially with the relatively new external factor of a pitch clock and the rushed pace (Shaw, 2024).

I start by considering consecutive hitters. Specifically, how the previous plate appearance affects the next hitter. I tally the pitch count for each PA, and then study the results of the next PA provided it occurred within the same inning. The minimal effect of the previous PA’s pitch count on the next PA’s result is shown in (Table 1).

Table 1: P/PA has a significant relationship to the AVG, OBP, and SLG of the next hitter. The trends seen in the analysis of hitters in isolation (Figure 2) are similar for the immediate next hitter — negative against AVG, positive against OBP, and still very little variance explained (≤0.2%).

The r and p values in Table 1 can be misleading because, for each additional pitch a batter sees, there are many fewer instances (Figure 1). In the TrackMan data, there are 108,966 plate appearances lasting two pitches, but only 50,641 plate appearances lasting 7 pitches. To get a better sense of what is going on with each increasing pitch of a PA and its effect on the next PA, we need to understand the full distribution of each statistic at each pitch count. From these distributions, we can determine when, if ever, the stats for a particular pitch count (or category of pitch counts) are statistically different from one another. Using a two-sample Kolmogorov-Smirnov test, I find that grouping P/PA into three bins (1–2, 3–6, and 7–10 P/PA) leads to small but robust trends that are statistically significant (Figure 3).

Figure 3: The box plots exhibit the same trends in each statistic from Figure 1 when moving across bins. A reasonable explanation for a decrease in AVG is the inclusion of strikeouts after 2 pitches. OBP increases across pitch count bins with more walks. The median of each statistic bin is marked by the orange line within the interquartile range of each box. The means across each statistic are shown by the dotted lines across the figure. The whiskers on each end of the box display the range of the data from the 5th to the 95th percentile.

Inner-Inning Dynamics II

“A successful pitcher keeps the leadoff hitter from reaching first base and puts the first pitch over for a strike — the two most important rules of pitching.” — Nolan Ryan (1977), Hall of Fame pitcher.

“A good leadoff hitter is a pain in the ass to pitchers.” — Richie Ashburn (1956), Hall of Fame leadoff hitter.

Fueled by the quotations from Hall of Famers Nolan Ryan and Richie Ashburn, next I look into the impact that a leadoff hitter seeing more pitches has on scoring runs in an inning. The intuition is that a hitter that makes the pitcher throw more pitches to start the inning should be setting his team up for some success. Table 2 shows that the frequency of runs scored when the leadoff hitter sees between 3 and 6 pitches is over 3% lower than the 1–2 bin and over 2% lower than the 7+ bin. In other words, if the leadoff hitter wants to extend their PA, they better see at least 7 pitches to avoid a significant reduction in the chance that their team scores that inning.

Table 2: Changes in the percent of innings with runs scored and the average runs per inning are modest, but show significant declines for plate appearances lasting 3–6 pitches.

Let’s see if this phenomenon carries over to the second hitter in the inning. This time, there is a less complete recovery for long plate appearances, and the likelihood of scoring runs, as well as the runs scored per inning, are significantly higher when the second batter jumps on one of the first two pitches (Table 3).

Table 3: The pitch counts that yield the highest likelihood of scoring, and the largest number of runs scored, have two or fewer pitches.

Let’s combine the first two hitters and examine offensive production based on their combined pitch count. Yet again, we see that the runs scored in the inning plummet when the first two batters combined see more than 3–4 pitches (Figure 4).

Figure 4: Mean Runs Scored and Run Frequency peak when the first two hitters in an inning jump on the first couple pitches in their at-bats. When the first two hitters see beyond those initial pitch bins, mean runs and run frequency drop below the league wide run average. Each of the first two hitters would have to see 6+ pitches to continue scoring runs at an above average rate.

Inner-Inning Dynamics III

In a last effort to examine how seeing more pitches may positively affect offensive results, I look at specific plate appearances where hitters faced certain pitchers multiple times within a game. No matter who the pitcher is, seeing more of him should produce a better recognition of his arsenal (Reogele, 2013). The more pitches that a hitter sees should give him a better idea of pitch velocity, movement, and personal strategy that the pitcher tries to get batters out with. In Figure 5, hitters saw peak batting averages in the first two pitches of each at-bat (consistent with all my results so far). However, if a hitter only saw 1 or 2 pitches in his first at-bat, his batting average the second time facing that pitcher drops by 50 points. In contrast, when a hitter has a long first plate appearance off a pitcher (with predictably bad results), his second at-bat off that same pitcher typically improves by 100+ points (Figure 5). Therefore, a hitter who extends an at-bat and receives a poor result can still be setting himself up for improved success in his second at-bat (although that success still won’t be as good as seeing pitches to hit early in counts every time).

Figure 5: This heatmap shows the strongest and weakest results in AVG, OBP, and SLG based on the amount of pitches a hitter saw the first time facing a pitcher. Cells shown in dark red are the highest percentile values of each column and the deepest blues are the lowest percentile values. In both the first and second Plate Appearances, hitters will peak in AVG if they have PAs with ≤2 pitches. OBP is heavily influenced by walks, so it straddles both bins of 1–2 and 7+ pitches. Hitters that have plate appearances that last 2 pitches or less would be optimizing their results across all 3 metrics.

Stay Out of the Bullpen

To counter the strategy that is regularly taught to hitters in “Seeing more pitches,” pitchers theoretically should aim to use as few pitches as possible to get outs. This approach helps starting pitchers stay in the game longer, but that might not necessarily be productive from a defense perspective. In fact, starters have recently found more success when they pitch as if they were relievers with less of an emphasis on longevity and focusing on pitch execution with maximum effort on every pitch (Clemens, 2019). The question becomes: How should hitters react? Should hitters try to shorten the starter’s outing by forcing long at-bats, or keep the pitcher in the game as long as possible to keep seeing the same repertoire they have seen all game?

Table 4: All statistics improve in the second at-bat off the same pitcher.

Figure 5 and Table 4 show that, on average, all statistics improve in the second at-bat off the same pitcher. This result begins to build an argument to keep starters in the game from an offensive standpoint.

Weinstein argues that making a pitcher work will give the offense a boost towards scoring runs. However, giving up runs is not a direct result of a pitcher’s rising pitch count. I looked at how a pitcher’s pitch count in the first two innings of his outing would affect the rest of his game, particularly through the lens of allowing runs.

Figure 6: When excluding the runs allowed in the first two innings and only looking at pitch
count through those innings, pitchers were not affected later in their outing by that pitch count. The average runs allowed across all bins is 1.52, with the highest averages being in the first 3 bins, (0, 15], (15,30], (30, 45]. Those averages were 1.59, 1.81 and 1.64 respectively. All means are depicted by the dashed dark blue lines.

Not only does increasing the starter’s pitch count fail to provide an offensive advantage, but it inches the offense closer to facing bullpen arms. For some teams in the MLB, getting to the 7th inning with a lead is just about tying a bow on the ballgame. Facing new arms out of the bullpen essentially makes the offense start over against more specialized and fresher pitchers (Orwin & Tien, 2022). In collegiate baseball, this bullpen-effect varies depending on depth in the roster. Some teams in the SEC can carry up to 25 arms every season, operating very similar to MLB rosters. Teams in the Ivy League typically carry less than 15, so there are situations where getting to the bullpen could be beneficial, particularly when facing worse pitchers for the second or third time in a series. In fact, from my personal experience as an Ivy League catcher, an elite reliever in the conference is almost guaranteed to be a nominee for Pitcher of the Year due to the lack of effectiveness most Ivy teams experience when coming out of the pen. When teams do not have that top tier arm out of the pen, getting to the bullpen can often be the main priority. It is safe to say however, when approaching a leverage spot against a deep pitching roster, offenses should prefer to stay out of the bullpen.

How important are these small changes in production stats with P/PA?

Figure 3 shows a batting average drop from 0.277 to 0.275 when the previous hitter saw 3–6 pitches instead of 2 or less. To put that shift into perspective, a 2-point change in batting average is equivalent to adding or subtracting 1 hit out of a 500 PA sample. The highest number of PA’s that collegiate hitters have over the course of a season is usually around 300. So in a sense, the effect of having an average P/PA in the 3–6 bin could mean that the hitter after you would have one less hit than he would if you had a P/PA average of 2 or less. The value of that one hit could be anything from a walk-off grand slam in the NCAA tournament to a 2-out blooper in the 4th inning of a midweek game against a non-conference opponent (SLG also drops in that 3–6 bin, so staying in 2 or less gives you a better shot at that grand slam). If you presented that to a hitter, every single one would take the option that would yield another hit, especially the big ones, no matter what situation it came in.

By Figure 3, a hitter who seeks to be in the 1–2 pitch count bin (for that one more hit by the batter hitting after him) would be dropping their OBP by 0.001. This change is negligible and only becomes realized after 1000 at-bats, (well over the typical collegiate career, even for a 4-year starter). From the individual player’s point of view, putting the ball in play early on in the count is more profitable than seeing more pitches.

Benefits to swinging early

A natural explanation for the decrease across each metric in the 3–6 pitch bin (Figure 3) is that strikeouts are much more prevalent in that range. In the Trackman data spanning 2023 and 2024, hitters only struck out 8.4% of the time in at-bats that last 3 pitches or less. That percentage jumps to 38.2% in the 4–6 pitch range and 39.7% at 7 pitches and beyond. Of course, walks become available after 4 pitches in at-bat. However, strikeouts still occur more frequently until the hitter sees at least 7 pitches. In the 4–6 pitch range, batters will strikeout 1.4 times per walk. In the 7–10 pitch range, walks become slightly more frequent and there are 0.88 strikeouts per walk. These strikeout rates are one of the primary culprits leading to higher offensive statistics in the first few pitches of a plate appearance (Figures 2 & 5).

Figure 7: Strikeouts occur much more frequently early on in the count. Only after 7 pitches do walks become more prevalent and even then, are barely more frequent.

A player with short PAs is a player who will strikeout less and get more hits and help the hitter behind him. This “contagious” phenomenon is what Lasorda described, but it turns out that seeing more pitches is more likely to stem a rally than to start one!

Should hitters always swing early?

Allowing the leadoff hitter to see more than two pitches could lead to a loss of 0.06 runs/inning (Table 2), 0.54 runs/game, and nearly 30 runs lost per season. When the leadoff man extends their PA to ≥7 pitches, they can regain 0.01 runs/inning, 0.09 runs/game, and nearly 5 runs/season. Adding 5 runs to any team’s offense can be monumental, but it does not outweigh the risk of losing 30 runs.

Tango et al. (2014) developed an estimate of the average number of runs a team needs to score in order to add one win to their total (RPW; Slowinski, 2010). I calculated the Division 1 college baseball RPW in 2023 to be about 12.5. Therefore, having the first batter of the inning see over 7 pitches will not translate to any more wins in a season. Consistently leading off innings with at-bats lasting 3–6 pitches however can net nearly 3 additional losses. One win can mean the difference between making playoffs or falling short, and of course that win could come in the super regional round which means a trip to Omaha and a chance at a National Championship. Those 3 losses also have the ability to come at any point in the season and before you can blink, your season is over.

Analyzing the second hitter of an inning produced even more devastating results as they saw more pitches. The maximum results based on the second hitter’s seen pitches came in the 2 or less pitch bin (Table 3). Seeing pitches beyond that bin decreased runs scored considerably. Seeing 3–6 pitches could cost nearly 50 runs in a season. Even seeing 7+ pitches still loses 30 potential runs as the second hitter of the inning.

Swing Early, Swing Often; but not at everything

Being aggressive does come with its downsides. Forcing at-bats to be short and trying to put the ball in play as soon as possible is hardly sound advice. Overly aggressive hitters will tend to swing at bad pitches, getting themselves out. The results from this study should only provide batters with more motivation to swing and not be timid when they see a pitch they can handle. There are still situations where hitters should lean more on the patient side. If a pitcher is struggling to find the zone, hitters should force him to regain his accuracy. If a runner is in motion, a hitter should lay off and allow that runner to steal that extra 90 feet if possible. Figure 8 goes to show that if the pitcher wants to work around you, let him! Hitters perform very well when they are ahead in the count but suffer drastically after falling behind. Laying off of pitches out of the zone increases your chances of reaching base when you do see that first strike.

Figure 8: Similar to Figure 5, each column is split into percentiles for the color gradient. Dark red cells are the highest values in each column and the deepest blues are the lowest. An analysis of what counts usually produce offensive results shows that early counts are not always optimal counts. However, when the hitter falls behind, he will tend to do better earlier than he will later. Getting ahead produces the best results but a hitter cannot control where the ball is thrown. Therefore, he should jump on the first hittable strike he sees. Looking at strikes and falling behind will give him the worst chances of reaching base/producing runs.

So how should a QAB be defined? Should the number of pitches in an at-bat even be included? I think we should define a quality at-bat as one in which the hitter got ahead in the count or jumped on a pitch early. Hitters with these kind of QABs are the guys that build success for themselves as well as their teammates.