MLB Franchise Batting Leaders Pitch Deck

This app can be used to find the top historical batters by a number of categories for each active franchise in the MLB.

The categories include:

• Batting average
• Home runs
• Runs batted in
• Stolen bases

To use the app, simply select the franchise that you are interested in looking at from the drop down menu at the top. The app will update to graph the top 10 batters by each of these statistics in the plots below.

Data in the app comes from the Lahman Baseball Database. http://SeanLahman.com. This data is maintained in an R package, “Lahman”, using a number of tables to represent team and player performance over the 100+ years of MLB history. The tables that I use are listed below with summaries:

• Batting - contains batting data for each player broken down by season.
• People - contains biographical information for each player. I use this table to get the player's full name.
• TeamsFranchises - contains information about franchises. I use this table to get the full name of each franchise and also to determine which franchises are still active today.

Utilizing dplyr, the data is prepared into a table called “battingSeasons” containing all of the columns that I need according to the code below:

library(Lahman)
library(dplyr)

data(Batting)
data(People)
data(TeamsFranchises)

battingSeasons <- left_join(Batting, Teams, 
                            by = c("teamID" = "teamID", "yearID" = "yearID") ) %>%
    left_join(TeamsFranchises, by = c("franchID" = "franchID")) %>%
    left_join(People, by = c("playerID" = "playerID")) %>%
    filter(active == "Y") %>%
    select(yearID, franchID, franchName, playerID, nameFirst, nameLast, AB.x, H.x,
           HR.x, RBI, SB.x) %>%
    rename(AB = AB.x, H = H.x, HR = HR.x, SB = SB.x)

battingSeasons$nameFull <- paste(battingSeasons$nameFirst, battingSeasons$nameLast)
battingSeasons[1, ]

  yearID franchID   franchName playerID nameFirst nameLast  AB  H HR RBI SB
1   1876      CHC Chicago Cubs addybo01       Bob     Addy 142 40  0  16 NA
  nameFull
1 Bob Addy

Next the data is aggregated for each of the statistics to combine a player's performance for each franchise that they played for. Then tables are built representing each franchise's top 10 players for each statistic. The example for batting average is shown below.

avg <- battingSeasons %>% 
    group_by(nameFull, franchName) %>% 
    summarize(ABs = sum(AB), Hs = sum(H)) 

avg$avg <- avg$Hs / avg$ABs 

avg_10 <- avg %>%
    group_by(franchName) %>%
    filter(ABs >= 1000) %>%
    slice_max(order_by = avg, n = 10) %>%
    select(nameFull, franchName, avg)

head(avg_10)

# A tibble: 6 x 3
# Groups:   franchName [1]
  nameFull         franchName             avg
  <chr>            <chr>                <dbl>
1 Luis Gonzalez    Arizona Diamondbacks 0.298
2 Paul Goldschmidt Arizona Diamondbacks 0.297
3 Danny Bautista   Arizona Diamondbacks 0.296
4 Orlando Hudson   Arizona Diamondbacks 0.294
5 David Peralta    Arizona Diamondbacks 0.291
6 Ketel Marte      Arizona Diamondbacks 0.289