How Does Age and Position Affect MLB Slot Bonus Signings?

Getting “The Call” from a MLB team is often the best moment of a players life. All of the years of hard work have finally paid off, and they have obtained their goal of becoming a professional baseball player. But in an era where teams use advanced analysis to make the best financial decisions for their organization, a players age and position may be used against them when it comes to signing their first check.

Each MLB team is allotted a predetermined amount based on how much each of their picks in the first 10 rounds are “worth”. The team that pick higher, and have extra picks on the bonus rounds, will have the most money available to spend, while the teams that pick later in rounds will have the least amount of money. In rounds 11–20, teams are allowed to spend $150,000 on each player without it going against their bonus pool. However, teams do not have to sign their players to those assigned values; often times in earlier rounds where the slot value is higher, they will sign the player under slot value, and use the extra money for later in the draft to sign a player they really like for above slot value. Some teams will do the opposite, where they will go over-slot for the best player available, and then sign players for cheap later in the draft. I have followed the draft fairly closely the past 2–3 years, and have been intrigued by some of the patterns I have noticed in the draft process. That got me thinking: Does the age and/or position of a player affect how much their signing bonus is relative to their slot value?

Players and their agents are always trying to find the right answer on when to sign with a team. Do they sign right out of high school when they are guaranteed money, but forfeit the opportunity to play college baseball? Do they sign after their junior year of college, when they have some leverage by saying they will go back to school for their senior year if they do not receive the money they want? Or should they complete their education, and only sign with a team once they complete their degree? Oftentimes it varies by each player, but if there was an answer on when a player got the most money, would they start to follow the money more often than not?

To start my research, I imported the 2023 MLB Draft data from the baseballr package. It contained 77 variables, but a lot of them were repetitive logistical columns, which I filtered out. I ended up taking the players’ name, pick information, birthday, pick and signing values, school class, and primary position. From there I added columns containing their age on the day of the draft, and what percent of their slot the player signed for. I had to do some cleaning of the data, such as changing signing_bonus and pick_value to numeric so I could complete the needed computations. I also combined 5th year draftees and graduate draftees, since they were the same age even though they were classified differently.

draft2023 <- get_draft_mlb(2023)

draft_subset <- subset(draft2023, select = c('person_first_last_name', 'pick_round', 'pick_number', 'person_birth_date', 'pick_value',
                                             'signing_bonus', 'school_school_class', 'person_primary_position_name'))
draft_subset$signing_bonus <- as.numeric(draft_subset$signing_bonus)
draft_subset$pick_value <- as.numeric(draft_subset$pick_value)
draft_subset$pick_value <- ifelse(draft_subset$pick_number >= 315, 150000, draft_subset$pick_value)

draft_subset <- draft_subset %>%
  rename(school_class = school_school_class) %>%
  filter(school_class %in% c('HS SR', '4YR SO', '4YR JR', '4YR SR', '4YR 5S', '4YR GR', '5S')) %>%
  mutate(school_class = case_when(school_class == '5S' ~ '4YR SR',
                                  school_class == '4YR 5S' ~ '4YR GR',
                                  TRUE ~ school_class),
         person_primary_position_name = case_when(person_primary_position_name == 'Outfield' ~ 'Outfielder',
                                                  TRUE ~ person_primary_position_name),
         draft_date = case_when(pick_number <= 70 ~ '2023-07-09',
                                between(pick_number, 71, 314) ~ '2023-07-10',
                                pick_number >= 315 ~ '2023-07-11'),
         draft_date = ymd(`draft_date`),
         person_birth_date = ymd(`person_birth_date`),
         draft_age = as.numeric((draft_date - person_birth_date) / 365),
         percent_slot = signing_bonus / pick_value)

Now that we had our completed dataset, we could start performing an exploratory analysis. First up, I wanted to look at how each school class performed. Historic trends are that kids drafted immediately out of high school tend to get the highest percentage of their slot value, because they hold all the power. They can ask for a high number, and if they do not get it, they just go to school and will try and get a higher number in 3 years when they try and get drafted again. The same can be said about draft eligible college sophomores. With college juniors, they still have a little say, but often times will take the money since their value typically decreases exponentially as a senior. After running the analysis, we see that high school seniors tend to sign for the most relative to their slot value, with each grade older signing for less and less. There was a massive drop-off once we got to 5th year players; teams take advantage of the fact that these players have to sign for whatever they are offered, or else they will not play professional baseball. They do not have the option to go back to school, so this is it for them if they do not sign.

Next we will look at if position affects signing bonuses. Typically, outfielders, shortstops, and pitchers are viewed as the most valuable. Outfielders — primarily centerfielders — and shortstops are seen as the best athletes on the field, and figure to be able to play anywhere on the diamond. Pitchers are special because they are extremely limited with their skillset, and of course because one star pitcher can completely change the direction of a team.

We do see that shortstops do indeed hold the highest premium, with pitchers ever so slightly behind. Catchers are valued the lowest, most likely due to the fact that they are often selected for their defense, and struggle to hit. If they are unable to perform at a high level defensively, their bat is usually not good enough to move to a different position.

The biggest limitation with this analysis is that only 2023 draft data was used. While the eye test says that these trends are usually fairly reliable, the only way to prove that is by doing this same analysis on other draft years. Another limitation is not having each individual outfield position labeled. As with infielders, the position up the middle — centerfield — is typically valued more than corner outfielders due to the fact that a center fielder is more than capable of changing positions if needed. Grouping all the outfielders together as one may not paint the whole picture.

Attached is a link to my GitHub containing my code for this project:

https://github.com/jaronrichman/INST414-Module-Assignments/blob/main/Module%201%20Assignment.R