PART II: Examining Non-Effective Cash Usage in MLB Spending Optimization

Part II of MLB Spending Optimization goes into Non-Effective Cash.

This is part two of a six-part series exploring spending optimization in Major League Baseball. Each article will dive deep into different ways that teams spend their dollars to produce the best value. You can find part one (and the rest of the series) here.

The entanglement between spending optimization and Major League Baseball consists of specifically looking at the implications of keeping dollars on the actual playing field. Ideally, as many dollars as possible should go to the players playing and producing runs. Yet, injuries and bad contract obligations take these dollars away from other spending.

Year over year, teams have to designate large amounts of funds for non-productive causes. Fans anguish over the player that their team is still somehow paying after they traded him many years ago, or the veteran who is barely healthy for 20% of the season. Teams lose millions of dollars to such ordeals each year. In putting together the best team, the extent and impact of possible liabilities must be known. Articulating the degree of effect of this spending on team performance allows an evaluation of an optimized configuration to lower the probability of such expenses.

In clarifying this type of spending, it will here on be referred to as Non-Effective Cash Usage. Three types of spending are included in this group: Injured List, Retained, and Buried Spending. The definitions of these types of spending are as follows: injured list spending includes dollars allocated for players who are on the injured list. Retained spending includes money designated for released, bought-out, or traded players. Buried spending includes funds spent on players earning a Major League salary while on a minor league roster (not including rehab assignments). As a whole, these terms will be classified as non-effective spending.

To input a precise definition of the overall aggregate, non-effective spending is any amount spent on players that are not playing. Non-effective is a sum of the three aspects, as shown.


SNE  = (SIL + SR + SB)


In that respect, S represents Spending while NE represents the non-effective type, IL represents the injured list amounts, R equates to the retained aspect, and B refers to the buried portion.


The Sample(s)


Each sample will be discussed within its respective category, but it seems necessary to lay out the nature of the data collected due to the varying results. All data is from Spotrac.com, which is known to track player salaries and transactions all around professional sports. It arguably has the best public database of the spending this piece is viewing, but that does not exclude it from some flaws. Their available data for the aforementioned types of non-effective spending is somewhat inconsistent, with most of the findings coming from 2013 to 2021. Various teams seem to be missing given numbers throughout the year, possibly skewing some of the data. Amassing a large sample was a priority, so any figures available for each type from 2013 to 2021 (without 2020) will be included. These figures will also be compared using total cash, as payroll does not account for some of these expenditures. Every number will be relative to cash spent as the same nominal amount can have a drastically different effect on two different teams. This is done using Cash Scores (used prior in this article about Spending Inequality), which takes the average spending amount of a given year and scales it to 100, with any number above or below being the percentage difference between the average. All of these factors will be included when suggesting guidelines for teams to follow.


Effects of Different Types of Spending

Injured List Spending

Like it or not, guaranteed contracts (unlike the NFL) are a facet of Major League Baseball. Whether injured or healthy, a player is getting paid either way. These guaranteed deals can often spell out trouble for some teams, as the player that they signed who consistently gets hurt is taking up lots of cash while providing no on-field value. Injuries are fairly variable, and accurate predictions of when they will likely occur is hard to come by. There is some evidence that effective preventative measures for some injuries do exist, but random accidents also happen quite a bit. It is worth noting that injuries are the only factor out of the three examined that cannot be directly avoided.  Teams may be able to control some aspects of these injuries though, hence the need to figure out the actual effect.

A total of 205 team seasons had injured list spending data from 2013-2021 (minus 2020), which is now the sample. Putting the data through a basic linear regression, 3% of winning percentage changes and 4% of run differential changes could be owed to the percent of total cash spent on injuries. These percentages are far from significant, and can likely be owed mainly to plain randomness. Although injuries can be logically proven as costly, during this period the effects seem to have evened out across almost all of the squads. Prevention could still be proven as beneficial, but the lack of apparent causation between the two factors proves teams have likely not navigated the field very successfully, as a higher correlation would’ve suggested that teams were indeed able to avoid the spending in the first place.


Retained Spending

To continue to pay for a player that is playing against you has to be one of the most counterproductive things that is even possible within the sport. Do not get confused – this type of spending is often warranted. But, it is only warranted because a team got themselves into an unfortunate situation in the first place. When a team is looking to cut an unproductive costly player, they often shop them around with other teams first. In the best-case scenarios, a team retains only some of a player’s contract and receives a prospect or two in a trade, getting the receiving team to pay for the rest of the contract. In the worst-case scenarios, no such trade occurs and the player is cut for a total loss. Either way, the team that originally signed the contract is losing badly, and avoiding these deals could add serious value.

The data on retained spending was a bit better than the injuries, yielding 239 total seasons during the same period as above. Evidence of a relationship was much more evident here – 23.9% of the changes in winning percentages and 22.7% of the changes in run differentials could be owed to the percentage of retained spending of total cash spent. These are somewhat significant amounts. The two series have a negative correlation, meaning that as the percentage of retained spending goes up, performance goes down. These points suggest that the data needs to be more thoroughly examined to explain the relationship.

When examining the clusters of teams that used a given range of percentages for retained spending, the data showed the largest cluster was in 0.1% to 10.1% usage, with the following points following a staircase downward as the ranges went up. This shows that most teams are good at avoiding retained spending, but the ones who do not do worse. Rather than being considered a skill, it is an expectation. And when a team couldn’t meet that expectation, they suffered. A clear effect is evident. 


Buried Spending

This type of spending is another example of something that teams have control over, having costly players sit in their farm system making Major League money. This type of move directly avoids the sunk-cost fallacy, which would have teams keep players on their roster just because of their contract despite a better replacement being in the system. The move in itself is better for the greater good of the ball club, but it is the result of prior poor decision-making. A team has to drastically falter to pay someone big-league dollars while having a replacement player who can perform better. Identifying the effect of this spending can emphasize the importance of avoiding it, leading to an optimized plan for success.

A total of 230 team seasons were in this sample despite the data limitations, providing a reasonably sized group to make conclusions. The causation numbers between these two were in between the others. 14.4% of winning percentage changes and 11.2% of run differential changes from season to season for individual teams could be owed to the percentage of buried spending to overall cash spent. While these amounts do seem significant enough to not be solely owed to variance, they also demonstrate that buried spending has a smaller effect than retained spending. In fairness, the median percent of buried payroll in the sample (2.41%) was much less than the median percent of retained payroll (11.74%). That factor makes the concept that buried payroll has a smaller impact than retained payroll in the varying team outcomes much more plausible. If they were somewhat equal in effect, that would be worrisome. 

This data also illustrates a clear downward escalator-type shape in the clusters, with the majority of teams having a lower retained payroll percentage and fewer having higher numbers. Like retained spending, this demonstrates that limiting retained spending is more of an expectation than an added skill. Teams are expected to not allocate a lot of their funds to buried players, although the ones that do can be hurt. While not as strong as some other effects, buried spending does seem to play at least a minute factor in team success. 


All Non-Effective Spending

Each factor of non-effective spending has been tested – but what about the whole? While teams may have different levels of control for each individual factor, they are all going to the same place – not to the players producing value. Keeping that in mind, examining this whole factor can show the total effect of these types of spending, which is part of the goal of determining how teams should act to be the most successful. 

Using the data available, 197 team seasons spanning from 2013-2021 (excluding 2020) were able to be examined. Regressing for causation, 25.8% of the fluctuation in winning percentage could be owed to the percent of cash being used on non-effective spending. Run differentials had a similar result, with 27.1% of their varying being owed to the non-effective figure. As the percentage of non-effectiveness increased, the winning percentage and run differential decreased. While intriguing, the confidence level in these measures is somewhat lower. The sample had to completely remove any team’s season with incomplete numbers, taking from various spots that would’ve possibly affected the outcome. These results should be considered with a wide range as they are far from iron-clad. 


R-Squared Scores Between Spending Types and Win Measurements


It feels fair to say that teams that have to allocate most of their funds to an off-the-field product struggle a lot more. They have a much harder time producing more runs than their opponents and winning ball games. Assuming the reader has read part one of this series by now, they can recall the fact that roughly 15.8% of the winning percentage and 11.3% of run differential variations could be owed to the relative payroll (percent of total MLB payroll in a given season). The extent of this non-effective spending effect may be variable, but a large range doesn’t take away from the fact that it likely causes a more significant effect on run differentials and win percentages in comparison to relative payroll. Put simply, the wasted dollars really do matter. 

Out of every aspect of Non-Effective Spending, retained spending proved to have the strongest relationship with team production. While avoiding all non-effective spending would be ideal, such a reality is not feasible. Of course, as mentioned in the chart, it is worth noting that retained spending had the highest median usage out of any one of the groups, almost doubling that of the second-highest (injured list spending). However, when considering the proportion of spending dedicated to Buried Payroll and its relative impact, it could be argued that the slightest change in buried spending habits is more important than retained spending. On that note, though, it is worth noting that such types of spending may not exhibit a direct linear relationship, with the marginal utility of a given dollar varying depending on the overall usage. However, such a glance is meant to clarify their overall importance (not their relationship within), providing a guideline to what should be followed rather than an exact step, as exact spending steps tend to not allow for dynamic changes in the marketplace. Hence, it would be wise to potentially order their priorities for avoiding ineffective spending in that order of the estimated impact.


Advice to Follow for Non-Effective Cash Usage and Spending Optimization

Investing in Avoiding Retained Spending

Retained spending proved to be the overall most damaging factor of the three non-effective spending types, making avoiding the issue so much more important. With 23.9% of winning percentage variance and 22.7% of run differential variance being explained by the factor, pouring dollars into aiding the probability of avoiding retained spending could bring serious value. If turned into a skill, teams could see proportional payoffs. But, how do you invest in something like this? For starters, teams can pour more funds into the data departments that project future value. Retained spending is a result of a team misjudging a player’s future, hoping to cut losses. Even if an estimate is only marginally better, it could be the difference between a team doing a deal or not, avoiding these losses altogether. 

Avoiding long-term deals would likely be a recommendation yielded from an improved data department, with the level of confidence decreasing as they attempt to predict further into the future. Much of retained spending is due to these types of deals, as teams are stuck with players that no longer produce value as time goes on. Teams instead could use cash to pay a premium in the short-run, avoiding long-run contracts and damage to the squad while keeping them in as a competitor in the marketplace. 

Teams could also buy an insurance plan. While commonly talked about in regard to injuries, a team could craft a plan with a company to reward a payout if a given player did not produce above a given mark for a set timeline. Of course, objective measures would have to be worked out (like using Fangraphs WAR) and criteria would need to be established, but it is possible. This goes hand-in-hand with data (again), as to benefit a team would have to be able to calculate the expected value of a given performance and ultimately beat the insurance company to gain any value. While doable, it would not be easy. But, it would very much help with retained spending, which obviously has a huge impact on team performance.


Sizing Different Investments into Buried Spending Risk

Buried spending is not the most important liability to a team’s production, but it does have a great effect compared to its median usage. Though, there are very many effects and only a limited amount of money to invest. Most teams generally regard avoiding buried spending as an expectation, with only the worst failing to do so. Like retained spending (which it shares a lot of qualities with), it can be turned into a skill – all a team needs to do is follow stricter procedures and allocate their funds more wisely. So, what should a team do with the second-most significant non-effective spending factor, and how should they invest?

It depends on the situation. Specifically, the amount of money a team has. If the Los Angeles Dodgers are looking to allocate some funds to some off-the-field efficiency efforts, they could benefit from spreading around their funds to invest in all the factors of risk (weighted on the significance of course). Buried spending, like retained, is the result of misevaluating contracts. A team that spends as much as the Dodgers can afford to focus on buried-prone contracts (typically more short-term) and retained-prone contracts (typically longer-term), covering a wider array of players in analysis. This would provide diversification, which is necessary when considering a causation factor with range. On the other hand, a team spending like the Pittsburgh Pirates would not have the benefit to hire extra data analysts and resources, making focusing most of their attention on the players subject to the most significant factor a better bet to contribute to team success. Diversification is a luxury that not everyone can afford. 

These types of buried deals are mostly short-term due to their nature, but that doesn’t exclude the need to mention that long-term deals should again be avoided. Cutting out some uncertainty from evaluating contracts should lead to less buried spending (although the improbable can happen). Insurance on these types of contracts is also applicable using a similar method as mentioned above, although this would only be recommended to bigger-market teams. Small-market teams need to focus their resources on the facets that will likely produce the biggest impact – this is not one of them. The larger market teams could research more thoroughly and use this to further diversify to lessen the risk, which could prove fruitful if an expected player suddenly drops off in performance. 


Staying Away from Injuries in Relativity to Team Situation

While the former two recommendations were mainly data-centric, staying away from injuries has a slightly different approach. Teams seemingly have little control over injuries, with the r-squared scores between win percentages and run differentials being entirely negligible – any correlation can likely be owed to pure chance. 

So with no team having a clear advantage over the other in preventing relative dollars spent on injuries, the choice becomes whether a team wants to play offense or defense. If they want to play offense, they can proactively dedicate time, money, and effort to identifying certain traits between positions that lead to injuries (assuming that it is possible). They would need lots of technology and lots of people, but the payoff could at least exploit a major market inefficiency (at least temporarily). If they want to play defense, they can continue to dedicate their resources to other factors that they believe they have a better understanding of and hire people when an avoidance effect becomes public market knowledge. There is no straight right or wrong answer, but money-laden teams would probably be considered more fit to pursue the possibility of discovering a market inefficiency. They are sacrificing less to go after the opportunity, in theory. 

Although, the smaller teams would benefit more than the bigger teams. Smaller teams are more likely to be held out of the premium free agency market, making exploiting a market inefficiency crucial to their success. Large-market teams do not need to worry about this – their payroll is so ample that these are far from difference-makers. Any type of team could benefit – they just need to factor in the optimal allowed spending and expected payoff to decide whether such a move is worth investing in, acknowledging the effects of both a win and a potentially dangerous loss. 


Adjusting Based on Market Conditions

In the above contemplations on guides to lead a team to optimize their portfolio, the specific guides for investing to avoid certain spending types were focused on. Each solution mainly focused on utilizing data, as data has proven to produce massive returns with little invested over the past twenty or so years in baseball – these types of spending are no exception. It’s been assumed that over the course of this study that this spending would come out of payroll and go to baseball operations, as that is directly what makes the most sense for teams (unless they are endowed with a willing spender for an owner). But as the market begins to adjust, teams will likely begin to produce less value (at least compared to the overall market) than before. There is a scenario in the future where payroll dollars should be limited on analytics for reducing this spending and focused more on acquiring and sustaining players – an optimized level is surely available.

Under the current market conditions, a team could invest massively into a separate data unit focusing on reducing non-effective spending. MLB analysts are relatively cheap starting out, often making under the general job market value at $50K – $90K per year. With experience and possibly heading a department… salaries do vary, but it is estimated that they make $100K to $200K per year. The supply of baseball jobs has not yet caught up with demand of employees wanting to work them, making spending on analytics still relatively cheap. Mixed with spending a bit on data services, a team is still yielding a massive profit by say… developing Artificial Intelligence software to identify sustainable MLB skill traits to avoid a faulty contract extension. This may not be sustainable in the long term – massive discovery leads to less to be found and wages will eventually be properly valued, which decreases the added utility of analytics. But temporarily, an investment into such a unit could produce massive returns.

In future market conditions, my suggestions are entirely different. The most probable scenario might be that analysts are eventually overpaid market value (unless teams came together to sedate the growth). With ever-rising revenue and the consequent rise of general manager and player pay, I could see these raises following as MLB teams try to attract the best talent in their analytics departments. They will deem it necessary to find an edge, overpaying for a fraction of the added value (due to the quality of diminishing returns). In that case, the allocated dollars to the proposed analytics unit should be entirely decreased to only keep up with the average of different teams. In this scenario, the market’s non-effective spending new average is so low that a marginal difference does not provide much value. Hence, the teams could allocate their new funds to paying higher AAVs (to avoid long-term deals) and better living conditions that would decrease the likelihood of a buried or unsustainable contract. This is more theory than an exact science (as with any market projection), although such a proposition could prove to be optimal. 


Concluding Thoughts


Combining all of the findings and mixing in the suggested guidelines, there are quite a few things to take away on the possibility of avoiding these types of spending. Baseball is far from a simple game, and with all of the money involved in Major League Baseball, it can pay to be creative about problems. Every team deals with non-effective spending, but discovering the actual impact and the possible proposition of investing to limit these types of liabilities proves to be another area that teams could possibly exploit.

The findings show that non-effective spending has highly negative impacts on Major League Baseball team performance. Injured, retained, and buried spending all account for a decent portion of total cash spent while providing zero value to a team. A team that can avoid this spending as much as possible will undoubtedly perform better than if they did spend on these non-effective expenses. If a team can manage to allocate their portfolio of cash to certain parts of their organization, this should create an efficient outcome by leveraging the resources available to get the wanted results – limiting inefficient spending. Clearly, this is easier said than done. The guidelines provided suggest a few paths that would likely work, but they are far from guaranteed success.

The clear application of these concepts is far from a guarantee, as applying general concepts to the vast intricacies of a Major League Baseball team is not always straightforward. Say a team does manage to apply these suggestions perfectly, fitting it into their franchise as was presented here. It is possible that the propositions, especially regarding the suggestion to invest heavily in data, are misguided. With the available information, they seem to be the most likely to garner success – non-effective spending is just misunderstood risk that data and manpower could possibly solve. But in the end, the suggestions can’t be flawed or correct until they are tested. Optimized spending rate can’t be truly optimized until the result is over a large sample. Baseball is a trial by fire.



Dylan Drummey

Studying Economics and Finance at the University of Kentucky. Founder and Writer for sabermetrics blog The Drummey Angle. Loves trying to identify the inefficiencies that remain within baseball.

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