Introduction

Much has been made of the ‘bullpen revolution’ over the past couple of years. Andrew Miller and Chris Devinski represent relievers on the forefront of the revolution, on teams at the forefront of innovation. The Astros routinely use Devinski in the middle innings, and for multiple innings. Devinski, a skilled pitcher who could close on many teams, provides a bridge to the ‘high leverage relievers’ , and affords the Astros bullpen flexibility that is usually unseen with conventional bullpen management. Conventional bullpen management calls the starter to pitch at least 6 innings, with high leverage relievers then entering to close the game. However, as pointed out in recent articles by Russel Carleton at Baseball Prospectus, starting pitchers often fail to reach the sixth inning. Starters are pitching less every year, and real evidence has been found of starters performing considerably worse towards the end of outings.

Carleteon, and others around the baseball community, have examined different possible rotations constructs that would make starters more efficient. Rotation ideas include tandem starters, four-man rotations, six-man rotations, and others. What I propose is something slightly different and more radical.

My method proposes a group of seven pitchers capable of handling a starters workload. The goal of these 7 pitchers is to maximize the amount of games the team enters the 7th inning with a lead. The traditional model would have the 5 best pitchers pitch every fifth day, and if they are unable to complete six, they are relived, usually by a mediocre reliever. Instead, my system proposes a tandem-type method where decisions are made based on the leverage of the game in different innings. If your goal is to reach the 7th inning with the lead, having a good pitcher available to bridge the gap and conserve the lead makes sense. Specifically, my method calls for a four-man rotation, with each starter going anywhere from three to give innings, rarely six. Here’s the kicker; the ace is not one of the 4 starters. Instead, the ace will often relieve a starter in the fourth, fifth or sixth innings in high leverage situations.

I believe this simulation hones in on one important question. How valuable is a pitcher throwing 6 innings of two run baseball, but doing so once every five days? Is he more valuable pitching three times a week, two important innings at a time, with his runs allowed more likely spread over three games? Many will say that a starter who can go deep into games and keep the your team in games is indispensable. It keeps the bullpen fresh and gives your team a great chance to win. I don’t disagree. But I think the notion that this is the most efficient way to manage a whole rotation is short sighted. By having the best starter available to come in in the third, fourth, fifth, or sixth of almost any game, you’re creating the opportunity to win games you might otherwise lose had you let the inferior pitcher remain in the game. I propose that it’s likely that six innings pitched over three games provide no disadvantage when compared to 6 innings pitched in one game.

And finally, the question of if pitchers can pitch on four days rest if they are only going three-five innings at a time is important to consider. Russel Carleton showed here that pitchers going on three days of rest are largely unaffected in their performance. Previous game pitch count has a much greater effect on current game performance than days of rest. However, the effects of pitching a couple innings every other day or every three days could catch up to a pitcher over the course of a season. Then again, it could be beneficial to the pitcher by allowing them more opportunity to work on their craft. The truth is, a a strategy that calls for pitching 3 innings at a time multiple times a week hasn’t been seen in decades, and the exact effect it would have on todays pitchers is unknown.

Simulation Specifics

I created a Monte Carlo simulation in Python with the ultimate goal of seeing if two teams, comprised of the same exact pitchers, may achieve different results using different pitching management strategies.

I started by gathering pitcher data from FanGraphs. I got ERA data for starters and relievers who qualified over the past three years. Then, using random sampling in Python, I randomly sampled 150 times from the starter data. These 150 samples represent the 150 starters in my simulation, and each starter was placed on one of 30 arbitrary teams. I did the same for relievers, generating seven per team.

Now, with 30 teams, each of differing skill levels, I could simulate a season. While each team had high leverage relievers, for the sake of this model, I only looked at the five starters and the 2 worst relievers ( the mop-up men) for each team. I also insured that the two mop-up men always had worse ERA’s than any starter on their team.

I first simulated the season using traditional rotation management. Each pitcher went as far as he could, and was removed based on simplistic criteria that relied on the amount of runs he had given up and the amount of innings he had pitched. Of course, more goes into deciding weather to pull a starting pitcher, but for the sake of this simulation, I kept the criteria simple. Innings were simulated all at once, with the amount of runs determined by a random number generator which incorporated the pitchers ERA. No offense was used in the run generation, only the pitching talent level was considered. After each game is simulated through six innings, the winner and loser is recorded; in the event of a tie, the away team gets credited with a win.

For the second simulation, the starting rotation is made up of the number two, three, four and five starters. The ace never starts!!

In the second simulation, the starter always pitches at least three innings, regardless of his performance. He goes out for the fourth inning only if he’s given up less than 3 runs and he pitched less than 5 innings in his previous start. He goes out for the fifth inning only if he’s given up less than 2 runs and he pitched less than 4 innings in his previous start. He goes out for the sixth inning only if the ace and the two mop-up men are not available. If the starter is pulled, either the ace or one of the mop-up men is brought into the game, depending on the leverage of the situation.

The criteria above is one instance in which the starter is removed due to rest or runs given up. There is another instance in which the starter can be removed. If the Leverage Index is greater than 1.1 heading into the fifth or sixth innings, and the ace is sufficiently rested (determined by other criteria), the ace will be brought in.

Results

For each of the thirty teams in my fictional league, I simulated 100 seasons where that team used the new pitching strategy, and every other team used the old pitching strategy. On average, teams added .6 wins a season. The max wins added was 1.32, the min wins added was -.666. There does seem to be a very slight advantage to be had from saving your ace for the big moments.

Conclusion

The future of the five-man rotation is in question. As teams and analysts explore alternate strategies, the question posed by this project will certainly be raised. Through this analysis, it seems the value of a start by an ace once a week can be matched, and beaten, by 2 or 3 separate two inning high leverage outings by that same ace. Furthermore, it is known that pitchers moving to the bullpen perform better because they are able to exert more energy per outing, since they pitch less than starters. A question remains, however, on whether pitching less per outing but pitching in more games allows pitchers to exert more energy per outing, despite pitching the same amount over a given time, say a week.

The practicality of this simulation is lost a bit in simplicity and in the unknown. Runs are modeled using a random number generator, and pitching changes are ruled by a small series of if-and-elif statments. Not to mention the simulation only allows pitchers to be subbed before an inning starts. Certainly, real baseball is more complex. Regardless, I believe the simulation provides a framework to understand different pitching strategies. Future work could involve an examination of other pitcher management strategies as well as added complexity.

All in all, a strategy such as the one proposed here calls for very short rest and short outings throughout the year, something that hasn’t been seen in decades. Furthermore, a team moving their ace to the bullpen to add marginal wins would face an uproar from the fans, the media, and the ace himself. It’s fun to imagine aces pitching this way in a simulation, but the reality of a strategy like this is a little far fetched. Nonetheless, as starters start to pitch less, good pitchers are going to be needed to bridge the gap to the late innings. There is value to be had in shortening outings and insuring good pitchers pitch in important situations.

The Rays always seem to be at the forefront of sabermetric innovation. They employ an army of Ivy League baseball analysts in the front office, they fully embrace the shift, and they employ pitch-framing superstars. The Rays like to stay on top of the ball. For the Rays, sabermetric advancement is a means of survival. And for the Rays, in the powerhouse AL East, it is the only way to survive.

Over the past seven years, it seems the Rays have been on to something. Looking at FanGraphs team offensive data from 2009 to 2016, there is a clear pattern with the Rays. They are third in fly ball% at 37.5%. The team with the highest FB% during that time span is the Oakland Athletics. The A’s pursuit of fly-ball-happy hitters was pretty well documented. In a great article over at Deadspin from 2013, Andrew Koo (who now works for the Tigers) shows us the advantages of hitting fly balls. First, Koo highlights how fly ball rates have decreased in the league since 2009. With an increasing trend towards ground ball pitchers, Billy Beane made a clear effort to acquire fly ball hitters. Why? Because as Koo shows us, fly ball hitters are significantly better against ground ball pitchers compared to other batters.  Tom Tango, who is mentioned in the Koo article,  found that this platoon advantage is very minimal, and is really only realized and meaningful when the “advantage is multiplied through several hitters. This is exactly what the A’s and Rays have done over the past seven years. Both teams have stockpiled fly ball hitters.

The Rays have done something else too. They have stockpiled fly ball hitters that also have a knack for pulling the ball. Over the past seven years, they lead the league in Pull% at 42.8%. Looking at this year’s team, the strategy seems to be in full effect once again. Of all the Rays hitters with at least 100 PA this year, only three players (Miller, Forsythe, and Dickerson) are below the league average in Pull%. Now, it could be pure coincidence that the Rays pull the ball so much. But I think we all know this is no coincidence at all. They seem to be preaching the pull-happy approach.

When looking at offensive data on pulled balls vs. data on other batted ball directions, the strategy makes sense. Looking at league data from 2009 to 2015, the average wRC+ on balls hit to the pull side is about 157, compared to 112 on balls hit up the middle. Isolated power on balls hit to the pull side is over 100 points greater than on balls hit up the middle or to the opposite field. There is an offensive advantage to pulling the ball, when the ball is put in play. Given the clear advantage to hitting the ball to the pull side, one might ask why wouldn’t every team stockpile dead pull hitters?

One answer: conventional wisdom says dead pull hitters don’t have the right approach. From the time I started playing baseball, I have been told to hit the ball to all fields. And I don’t disagree with this philosophy. Staying back and being able to drive the ball to all fields definitely makes for a very productive hitter. But it also results in dead pull hitters being undervalued.

Another knock on pull hitters is that when they hit ground balls, they roll over the ball and commit easy outs.  Looking at the soft hit percentage vs ground ball percentage on balls that are pulled for all 30 teams from 2009-2016, I found this to be a valid concern about pull hitters. The data shows a positive correlation between ground balls and soft hit percentage. 

The Rays, however, have the fourth-lowest GB% on pulled balls. During that same time span, the Rays have the sixth-lowest Soft% on ground balls. They aren’t hitting weak ground balls. The Rays have made a concerted effort to pull the ball and they have avoided the weak contact that comes with pulling the ball on the ground.

Conclusion

The Rays have found and pounced on a market inefficiency. They have optimized their offense by targeting and developing players that consistently pull the ball in the air and avoid weak contact on the ground. Since these players aren’t the conventional hit-to-all-fields type of player, they can get these players for cheap. Simply put, the Rays have have capitalized on the offensive advantage of pulling the ball.

Food For Thought

Something to think about further is the trend of Pull% in the MLB from 2002-2016. It is down 5%. Intuitively, this makes sense, as velocity is way up over that time span. With velocity up, it is harder to pull the ball. This trend reminded me of a trend mentioned earlier in the article. As noted by Koo and Tango, ground balls are up around the MLB. As Tango found, fly ball hitters have an advantage against ground ball pitchers, and it is beneficial to utilize that advantage. What if there is a similar platoon advantage regarding pull hitters vs. power pitchers? In line with Tango’s logic, what if dead pull hitters have a platoon advantage against power pitchers? What if the Rays have figured out this advantage and have been exploiting it for years? The platoon advantage makes sense. Dead pull hitters, by nature, go up to the plate looking to pull the ball. Which means they are early on almost everything. As a result, they wouldn’t have as much trouble catching up to gas. This is definitely something to think about, and something I will be certainly researching in the coming weeks.