Pitch Count Efficiency is Undervalued

During Game 6 of the World Series, Kevin Cash infamously replaced his cruising starting pitcher, Blake Snell, with reliever Nick Anderson. Anderson would give up the lead before registering an out, and the Los Angeles Dodgers won the Series for the first time in 32 years.

A heavily criticized decision by many, both in the moment and in hindsight, the move is representative of the new direction many clubs have been heading towards. This is calculated and analytics-heavy decision-making on reliever usage that has caused both a major shift in the value of relievers and a steady increase in pitchers used in games.

The consistent incline of pitchers used per game notably paired with the decline of average pitches and innings thrown by starters begs the question: how should pitch count factor into removing pitchers from games? If starters are removed for the fact that they are facing the top of the order for the third time rather than because they are fatigued or have seen a decline in their outing performance, is it important to pass on hittable pitches in order to drive pitch count up? Alternatively, is there value in being a pitcher who can record outs quickly if by the time Mookie Betts comes to the plate in the 6th inning, the threat of impending doom will chase an ace at 73 pitches out of the game?

The trends seem to indicate that pitcher efficiency and hitter’s ability to drive pitch counts is less valuable than it used to be. With an average as high as 4.65 pitchers per team per game, clubs have shown willingness to remove pitchers based on the hitter’s handedness, or a number of situational reasons rather than due to pitch counts. Even with the new rule eliminating single batter appearances, shortened reliever outings have opened the gates for consecutive day pitching and a neglect of pitch counts.

Here’s a chart showing the percentage of pitcher appearances coming off of zero days rest:

Additionally, influence research such as this 2012 study suggests that rest days do not have a strong impact on performance (NIH). Although the accumulation of pitches and lack of rest associates negatively with overall performance, sporadic acts of consecutive day usage prove to be insignificant. Managers are more willing now to utilize the reliever best fitted to the situation somewhat regardless of pitch count. This trend ultimately decreases value both in efficient pitchers and hitters who work counts. However, is this justified?

First, I attempted to analyze a number that accurately represented how efficient a pitcher is and inversely how good a hitter was at drawing pitches. For pitchers, I used pitches per out. Very simply, this is a great way at judging averages for both teams and individual pitcher’s efficiencies.

Pitches per out turns out to be a solid correlator of team ERA and win percentage. Although this is somewhat predictable since this pitches/out is influenced by the number of runners allowed on base, it still remains pertinent.

A pitcher’s ability to keep his pitch count down allows a team to do a number of things. First, shutdown relievers are hard to come by, and teams who keep their bullpen to three or fewer innings per game will tend to have more success than a team who consistently relies on the bullpen for four or five frames every night. The distribution of relief pitcher innings compared to starting pitcher innings shows a clear reliance on fewer-but-stronger pitchers to carry the load of a team. Lower pitch counts from starters helps to utilize a team’s optimal relievers.

Second, although pitching on short rest does not show an immediate drop of performance, long stretches of high pitch counts can have effects on pitchers. This is often more pertinent for young arms, but the protection of players in order to keep their physical stock high has been a positive trend that can influence a team’s season.

Lastly, the ability to sustainably place your highest impact reliever in a close game is extremely important. Teams winning close games are rewarded in the long run, and close game wins are often the result of availability of strong relievers. Furthermore, reliever depth is expensive, and limiting bullpen innings and pitch counts of top arms allows for a better distribution of funds from a team. We often see clubs in contention scramble for strong relievers at the trade deadline. This problem is lessened when teams can rely on a smaller subset of pitchers to efficiently get outs.

So which pitchers were the most efficient in 2020? Here they are, with a minimum of 400 pitches thrown.

And the least efficient?

Additionally, the question arises of which types of pitchers are most efficient? In comparing pitchers to K% and BB%, BB% is shown to be far more of a factor.

The top 10 pitchers in pitches/out boasted an average 4.78 BB% compared to the league average 8.47%. However, K% showed little bearing, as the top 10’s average of 22.06% is only 2.05 percentage points off the league average 24.11%.

Finally, I wanted to inspect the flip side of this argument in the value of efficient pitchers and take a look at which hitters were best at driving pitch counts. Instead of using pitches/out, I estimated “pitches earned” as the number of pitches taken by a hitter per plate appearance accounting for getting on base:

Pitches Earned = [Total Pitches Seen + (OBP*PA*3.97)]/PA

The numerator includes the number of times on base multiplied by the league average number of pitches per batter (3.97), because getting on base results in the pitcher throwing to another batter before recording an out. It’s a simple concatenation of OBP and pitches seen, but it should represent a number that is equal to the number of pitches a player “earns”, i.e. forces from the opposing team. Here’s the best and worst pitch “earners” from 2020, again at a minimum of 400 pitches.

All of this draws certain conclusions, but not without fault. First, all data was taken from 2020, which is obviously a widely skewed year for baseball. There was a small sample sizes, a shortened spring training, expanded playoffs, and rule changes, to name a few. Also, the argument of playing for pitch count isn’t new or groundbreaking, but in the direction baseball seems to be going, I think it is worth looking at as a factor in player/team judgement. Lastly, I have included plenty of of speculation, as I am only an observer of the game in terms of bullpen use and the reliever market.

However, I believe that pitch count and efficiency still have a strong significance in baseball. While it might not be the determining factor in starting and sitting a player in replacement of more advanced or reliable statistics, there is still long-term value in playing for pitch count. There are results to consider such as a beefier line up, a slimmer bullpen, and a stronger rotation, considering pitcher efficiency and pitches earned can have real value.

References

Albert, Jim. “Historical Look at Pitcher Usage.” Exploring Baseball Data with R, 28 Jan. 2019, baseballwithr.wordpress.com/2019/01/28/historical-look-at-pitcher-usage/.

“Baseball Savant: Trending MLB Players, Statcast and Visualizations.” Baseballsavant.com.

Clemens, Ben. “Are Starters Improving Relative to Relievers?” FanGraphs.com, blogs.fangraphs.com/are-starters-improving-relative-to-relievers/.

Jaffe, Jay. “Starting Pitcher Workloads Have Been Significantly Reduced in 2020.” FanGraphs Baseball, blogs.fangraphs.com/starting-pitcher-workloads-have-been-significantly-reduced-in-2020/.

“MLB Stats, Scores, History, & Records.” Baseball-Reference.com.

More Pitching Appearances Are Coming with Zero Days’ Rest ” Baseball-Reference Blog ” Blog Archive, www.baseball-reference.com/blog/archives/10071.html.

N, Brandon. “Pitch Count Trends – Why Managers Remove Starting Pitchers.” Community Blog, community.fangraphs.com/pitch-count-trends-why-managers-remove-starting-pitchers/.

SL;, Bradbury JC;Forman. “The Impact of Pitch Counts and Days of Rest on Performance among Major-League Baseball Pitchers.” Journal of Strength and Conditioning Research, U.S. National Library of Medicine, pubmed.ncbi.nlm.nih.gov/22344048/.