Part 3 is finally here! The Marlins are shedding payroll and trading players this offseason. Giancarlo Stanton was traded to the Yankees, Dee Gordon was sent to the Mariners, and Ozuna to the Cardinals. While most expect the Marlins to be rebuilding for the next few years, it is not impossible to field a competitive team with a low payroll. One example is the Brewers, the team with the lowest payroll of 2017, from earlier this year. By trading for players known to be available, or signing free agents, that are undervalued, the Marlins should be able to field a team projected to make the playoffs. In this post, we will discuss the results of our constrained optimization analysis. Catch up on the data and methods used in Parts 1 and 2.
REMEMBER: This analysis was initially performed in July, examining only players at the time that were on the Marlins roster, those who would be free agents in 2018 and anyone who was considered a trade target before the deadline in 2017.
Using OPS and WHIP, we treated this situation as a constrained optimization problem. In analyzing batters, the goal was to maximize OPS with the constraints of just 13 batters and a 16 million dollar salary cap. In analyzing pitchers, the goal was to minimize WHIP with 12 pitchers and a 25 million dollar salary cap. Overall the results will give us a full 25 man roster for 41 million dollars with an additional 15 million left over to fill out the 40 man roster. For reference, below are two charts that depict player salaries vs their consequent OPS or WHIP for this set of players. Can you spot any undervalued players?
The model using the player pool described above produced a team that is predicted to score 810 runs while only allowing 610. Using the Pythagorean Theorem of Baseball to help predict wins, this would give the Marlins a record of 103 wins and 59 losses, a win percentage of .643, and should easily help them qualify for the postseason. The 25-man roster as produced by the model is shown below:
Here are the subsequent graphs for this roster, depicting their OPS/WHIP vs salary respectively:
The Marlins already have a young group of batters under control through 2018 that will form the core of the hitting squad. The rest of the squad will be filled out with some veteran players possessing higher OPS stats who can likely be signed at a minimal salary. It is also notable that many of the players can play multiple positions which will give the manager a lot of flexibility regarding the starters for any given game.
As for the pitchers, the goal is to obtain some young, successful pitchers such as Gerrit Cole and Sonny Gray, and then fill out the roster around them with other starters and relievers possessing low WHIP stats. Dan Straily and Tyler Chatwood are two more starting pitchers that should do well in filling out the pitching rotation. A couple of the veterans – Fernando Rodney and Joe Blanton – could possibly be signed at relatively low salaries and provide mentorship to the younger players.
One important item to note is that Giancarlo Stanton is no longer on the team. Stanton is signed through 2028 and his current salary is set to increase to $25 million in 2018. Given that our goal is a payroll of around $56 million, we do not want to have one player taking up over one-half of the team’s payroll. Therefore, the goal will be to trade Stanton during the offseason. He may be especially valuable this offseason given that he is currently on pace to hit more than 60 home runs in 2017. However, it is difficult to determine how the trade would be transacted. Given that our modeled roster includes both Gerrit Cole and Matt Joyce from the Pirates, trading Stanton for Cole and Joyce from the Pirates is possible a trade could be worked out. That trade would likely have to involve a third team given that the Pirates are unlikely to want to take on Stanton’s salary.
We understand the hesitation the Marlins organization may have in giving up Stanton, the team’s superstar, and organizational cornerstone. Beyond Stanton’s contributions on the field, he is a franchise player that is exciting to watch and who Miami fans gravitate towards. We know the goal of the Marlins is to not only make the playoffs but also maximize revenues and profitability. It is difficult to quantify what a trade of Stanton may do to attendance and merchandise sales. This is a consideration in determining the merits of such a trade. (Obviously, Jeter has decided that these consideration did not matter.)
The list of players shown above does not necessarily have to be the exact roster the Marlins will field for the 2018 season. The model we created can be adjusted to swap any players the organization chooses to target, and then generates RS and RA results for the model and/or roster comparison. As we mentioned earlier, we do not know what trading Stanton would do for attendance, but moving Stanton – and creating the roster we have recommended – would greatly help the team’s payroll fall below $56 million payroll, and increase the likelihood of achieving the RS and RA thresholds needed to be a playoff contender. Even with Stanton, attendance has been consistently low over the years, so losing Stanton may have a minimal effect.
As it currently stands, the Marlins are not a championship caliber team, nor a playoff contender. The Marlins have the 20th lowest payroll in the league, and the 28th lowest attendance in MLB, according to ESPN. We understand the Marlins organization wants to move towards being a contender and to grow their fan base, but the low payroll, coupled with the low attendance numbers, meant we had to create a model that was both realistic and budget-sensitive.
What was displayed in the results of our analytics aligns with the viewpoints of Baumer (2008), McNeal (2009), and Houser (2005): the main offensive metric we want to maximize is OPS and the main defensive metric we want to minimize is WHIP. Both of these greatly influenced Runs Scored and Runs Allowed, and we configured a roster that met these metrics’ requisite thresholds for the Marlins to become a playoff contender while remaining below a $56 million payroll.
A decrease in attendance from the already low levels would pose the organization with a tough challenge, but we feel it is a worthwhile risk when considering what the organization can gain. If we observe the Los Angeles Angels, for instance, the team’s highest-paid player is Mike Trout – arguably one of the best players in baseball – but his $144.5 million contract has prevented the Angels organization from surrounding him with player personnel to complement his abilities. This has stagnated the organization and roster. Although with offseason acquisitions of Ohtani, Kinsler, and Cozart, perhaps they have a chance in 2018.
It is easy to see the excitement that comes with a great player like Stanton, and if the Marlins choose to keep him on their roster, they will have to surpass their $56 million payroll brink to bring in enough talent to complement Stanton. Using our model, if the Marlins organization adjusts their payroll ceiling, they can choose to create models with Stanton while also seeing what the optimal player mix is to maximize Runs Scored and minimize Runs Allowed. The primary recommendation and result of our analysis is the Marlins organization should focus on OPS and WHIP as drivers to the overall Pythagorean number of wins, constraining the team’s payroll as needed when modeling.
What do you think of our methods and results? Could this roster make the Marlins competitive? Let us know in the comments below! As always, our code can be found on Github.
The SaberSmart Team
Baumer, Ben S. (2008) “Why On-Base Percentage is a Better Indicator of Future Performance
Than Batting Average: An Algebraic Proof” Journal of Quantitative Analysis in Sports: Vol. 4: Iss.2, Article 3, 1-11. https://doi-org.turing.library.northwestern.edu/
Houser, Adam. “Which Baseball Statistic is the Most Important When Determining Team
Success?” The Park Place Economist, Vol. XIII, 29-35.
McNeal, Stan (2009) “How to Win the World Series.” Sporting News, 233(24) 28-31.