Chicago Cubs 2025 Batting Performance Analysis
Derek Williams
2025-10-11
Q1
data(cars)
median(cars[, 1])## [1] 15Q2
library(jsonlite)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionbtc <- "https://min-api.cryptocompare.com/data/v2/histoday?fsym=BTC&tsym=USD&limit=100"
btc <- fromJSON(btc)
df_btc <- btc$Data$Data
max_close <- max(df_btc$close, na.rm = TRUE)
cat("Maximum daily CLOSE (BTC-USD) over last 100 days:", max_close, "\n")## Maximum daily CLOSE (BTC-USD) over last 100 days: 124723Q3
Research Questions
1. Which Chicago Cubs hitters produced the most offense in 2025 (in terms of HR, RBI, and total bases)?
2. Who were the team’s most efficient hitters based on rate stats (AVG, OBP, SLG, OPS)?
3. How do plate-discipline measures such as walks (BB%) and strikeouts (K%) vary across players?
4. Which players demonstrated the most power independent of batting average (ISO)?
5. Are there any hitters whose strong rate metrics suggest breakout potential despite lower counting stats?
Extraction
library(jsonlite)
library(dplyr)
library(tidyr)
library(ggplot2)
url <- "https://statsapi.mlb.com/api/v1/stats?stats=season&group=hitting&season=2025&teamId=112&sportId=1"
raw_2025 <- fromJSON(url)
str(raw_2025)## List of 2
## $ copyright: chr "Copyright 2025 MLB Advanced Media, L.P. Use of any content on this page acknowledges agreement to the terms po"| __truncated__
## $ stats :'data.frame': 1 obs. of 8 variables:
## ..$ type :'data.frame': 1 obs. of 1 variable:
## .. ..$ displayName: chr "season"
## ..$ group :'data.frame': 1 obs. of 1 variable:
## .. ..$ displayName: chr "hitting"
## ..$ totalSplits : int 7
## ..$ exemptions :List of 1
## .. ..$ : list()
## ..$ splits :List of 1
## .. ..$ :'data.frame': 7 obs. of 8 variables:
## .. .. ..$ season : chr [1:7] "2025" "2025" "2025" "2025" ...
## .. .. ..$ stat :'data.frame': 7 obs. of 34 variables:
## .. .. .. ..$ age : int [1:7] 28 28 27 23 30 31 30
## .. .. .. ..$ gamesPlayed : int [1:7] 156 136 155 157 151 159 150
## .. .. .. ..$ groundOuts : int [1:7] 179 114 111 116 98 137 120
## .. .. .. ..$ airOuts : int [1:7] 197 169 139 192 177 147 165
## .. .. .. ..$ runs : int [1:7] 89 91 78 91 75 84 87
## .. .. .. ..$ doubles : int [1:7] 29 25 25 37 31 24 32
## .. .. .. ..$ triples : int [1:7] 4 4 5 4 3 3 0
## .. .. .. ..$ homeRuns : int [1:7] 7 22 34 31 32 24 23
## .. .. .. ..$ strikeOuts : int [1:7] 49 88 139 155 164 168 151
## .. .. .. ..$ baseOnBalls : int [1:7] 39 87 56 29 71 47 87
## .. .. .. ..$ intentionalWalks : int [1:7] 1 7 2 1 1 1 2
## .. .. .. ..$ hits : int [1:7] 178 133 137 146 140 144 138
## .. .. .. ..$ hitByPitch : int [1:7] 7 4 10 9 1 2 2
## .. .. .. ..$ avg : chr [1:7] ".297" ".266" ".261" ".247" ...
## .. .. .. ..$ atBats : int [1:7] 599 500 524 591 571 590 569
## .. .. .. ..$ obp : chr [1:7] ".345" ".377" ".343" ".287" ...
## .. .. .. ..$ slg : chr [1:7] ".394" ".464" ".523" ".481" ...
## .. .. .. ..$ ops : chr [1:7] ".739" ".841" ".866" ".768" ...
## .. .. .. ..$ caughtStealing : int [1:7] 6 3 0 8 2 3 3
## .. .. .. ..$ stolenBases : int [1:7] 29 25 4 35 5 20 6
## .. .. .. ..$ stolenBasePercentage : chr [1:7] ".829" ".893" "1.000" ".814" ...
## .. .. .. ..$ caughtStealingPercentage: chr [1:7] ".171" ".107" ".000" ".186" ...
## .. .. .. ..$ groundIntoDoublePlay : int [1:7] 4 8 2 1 13 9 8
## .. .. .. ..$ numberOfPitches : int [1:7] 2305 2306 2324 2312 2727 2521 2717
## .. .. .. ..$ plateAppearances : int [1:7] 649 597 592 647 651 645 663
## .. .. .. ..$ totalBases : int [1:7] 236 232 274 284 273 246 239
## .. .. .. ..$ rbi : int [1:7] 61 73 90 95 103 77 79
## .. .. .. ..$ leftOnBase : int [1:7] 233 180 191 274 246 264 207
## .. .. .. ..$ sacBunts : int [1:7] 0 1 0 6 0 1 0
## .. .. .. ..$ sacFlies : int [1:7] 4 3 2 12 8 5 5
## .. .. .. ..$ babip : chr [1:7] ".313" ".282" ".292" ".276" ...
## .. .. .. ..$ groundOutsToAirouts : chr [1:7] "0.91" "0.67" "0.80" "0.60" ...
## .. .. .. ..$ catchersInterference : int [1:7] 0 2 0 0 0 0 0
## .. .. .. ..$ atBatsPerHomeRun : chr [1:7] "85.57" "22.73" "15.41" "19.06" ...
## .. .. ..$ team :'data.frame': 7 obs. of 3 variables:
## .. .. .. ..$ id : int [1:7] 112 112 112 112 112 112 112
## .. .. .. ..$ name: chr [1:7] "Chicago Cubs" "Chicago Cubs" "Chicago Cubs" "Chicago Cubs" ...
## .. .. .. ..$ link: chr [1:7] "/api/v1/teams/112" "/api/v1/teams/112" "/api/v1/teams/112" "/api/v1/teams/112" ...
## .. .. ..$ player :'data.frame': 7 obs. of 5 variables:
## .. .. .. ..$ id : int [1:7] 663538 663656 683737 691718 673548 621020 664023
## .. .. .. ..$ fullName : chr [1:7] "Nico Hoerner" "Kyle Tucker" "Michael Busch" "Pete Crow-Armstrong" ...
## .. .. .. ..$ link : chr [1:7] "/api/v1/people/663538" "/api/v1/people/663656" "/api/v1/people/683737" "/api/v1/people/691718" ...
## .. .. .. ..$ firstName: chr [1:7] "Nico" "Kyle" "Michael" "Pete" ...
## .. .. .. ..$ lastName : chr [1:7] "Hoerner" "Tucker" "Busch" "Crow-Armstrong" ...
## .. .. ..$ league :'data.frame': 7 obs. of 3 variables:
## .. .. .. ..$ id : int [1:7] 104 104 104 104 104 104 104
## .. .. .. ..$ name: chr [1:7] "NL" "NL" "NL" "NL" ...
## .. .. .. ..$ link: chr [1:7] "/api/v1/league/104" "/api/v1/league/104" "/api/v1/league/104" "/api/v1/league/104" ...
## .. .. ..$ sport :'data.frame': 7 obs. of 3 variables:
## .. .. .. ..$ id : int [1:7] 1 1 1 1 1 1 1
## .. .. .. ..$ link : chr [1:7] "/api/v1/sports/1" "/api/v1/sports/1" "/api/v1/sports/1" "/api/v1/sports/1" ...
## .. .. .. ..$ abbreviation: chr [1:7] "MLB" "MLB" "MLB" "MLB" ...
## .. .. ..$ rank : int [1:7] 1 2 3 4 5 6 7
## .. .. ..$ position:'data.frame': 7 obs. of 4 variables:
## .. .. .. ..$ code : chr [1:7] "4" "9" "3" "8" ...
## .. .. .. ..$ name : chr [1:7] "Second Base" "Outfielder" "First Base" "Outfielder" ...
## .. .. .. ..$ type : chr [1:7] "Infielder" "Outfielder" "Infielder" "Outfielder" ...
## .. .. .. ..$ abbreviation: chr [1:7] "2B" "RF" "1B" "CF" ...
## ..$ splitsTiedWithOffset:List of 1
## .. ..$ : list()
## ..$ splitsTiedWithLimit :List of 1
## .. ..$ : list()
## ..$ playerPool : chr "QUALIFIED"cubs_hit_2025_raw <- raw_2025$stats$splits[[1]]Data Description, Cleaning, and Manipulation
cubs_hit_2025 <- cubs_hit_2025_raw %>%
unnest_wider(stat, names_sep = ".") %>%
unnest_wider(player, names_sep = ".") %>%
unnest_wider(position, names_sep = ".") %>%
unnest_wider(team, names_sep = ".")cubs_hit_2025 <- cubs_hit_2025 %>%
mutate(
across(
c(stat.avg, stat.obp, stat.slg, stat.ops, stat.babip,
stat.stolenBasePercentage, stat.caughtStealingPercentage,
stat.groundOutsToAirouts, stat.atBatsPerHomeRun),
~ suppressWarnings(as.numeric(sub("^\\.", "0.", .)))
)
)cubs_hit_2025 <- cubs_hit_2025 %>%
rename(
player_id = player.id,
player = player.fullName,
pos_code = position.abbreviation,
pos_name = position.name,
team_id = team.id,
team_name = team.name
)cubs_hit_2025 <- cubs_hit_2025 %>%
select(
rank, season, player_id, player, pos_code, pos_name, team_name,
stat.age, stat.gamesPlayed, stat.plateAppearances, stat.atBats,
stat.hits, stat.doubles, stat.triples, stat.homeRuns, stat.rbi,
stat.runs, stat.baseOnBalls, stat.strikeOuts, stat.stolenBases,
stat.caughtStealing, stat.totalBases,
stat.avg, stat.obp, stat.slg, stat.ops, stat.babip
) %>%
distinct(player_id, .keep_all = TRUE)# Guard against division by zero
safe_div <- function(n, d) ifelse(is.na(d) | d == 0, NA_real_, n / d)
cubs_hit_2025 <- cubs_hit_2025 %>%
mutate(
# Power independent of average
ISO = stat.slg - stat.avg,
# Rates per plate appearance
BB_rate = safe_div(stat.baseOnBalls, stat.plateAppearances), # walk rate
K_rate = safe_div(stat.strikeOuts, stat.plateAppearances) # strikeout rate
)# Basic shape
n_players <- nrow(cubs_hit_2025)
n_cols <- ncol(cubs_hit_2025)
# Missingness snapshot
na_counts <- sapply(cubs_hit_2025, function(x) sum(is.na(x)))
# Summary of key numeric columns
num_summary <- cubs_hit_2025 %>%
select(stat.plateAppearances, stat.atBats, stat.hits, stat.doubles, stat.triples,
stat.homeRuns, stat.rbi, stat.runs, stat.totalBases,
stat.avg, stat.obp, stat.slg, stat.ops, stat.babip, ISO, BB_rate, K_rate) %>%
summary()
n_players; n_cols## [1] 7## [1] 30na_counts## rank season player_id
## 0 0 0
## player pos_code pos_name
## 0 0 0
## team_name stat.age stat.gamesPlayed
## 0 0 0
## stat.plateAppearances stat.atBats stat.hits
## 0 0 0
## stat.doubles stat.triples stat.homeRuns
## 0 0 0
## stat.rbi stat.runs stat.baseOnBalls
## 0 0 0
## stat.strikeOuts stat.stolenBases stat.caughtStealing
## 0 0 0
## stat.totalBases stat.avg stat.obp
## 0 0 0
## stat.slg stat.ops stat.babip
## 0 0 0
## ISO BB_rate K_rate
## 0 0 0num_summary## stat.plateAppearances stat.atBats stat.hits stat.doubles
## Min. :592.0 Min. :500.0 Min. :133.0 Min. :24.0
## 1st Qu.:621.0 1st Qu.:546.5 1st Qu.:137.5 1st Qu.:25.0
## Median :647.0 Median :571.0 Median :140.0 Median :29.0
## Mean :634.9 Mean :563.4 Mean :145.1 Mean :29.0
## 3rd Qu.:650.0 3rd Qu.:590.5 3rd Qu.:145.0 3rd Qu.:31.5
## Max. :663.0 Max. :599.0 Max. :178.0 Max. :37.0
## stat.triples stat.homeRuns stat.rbi stat.runs stat.totalBases
## Min. :0.000 Min. : 7.00 Min. : 61.00 Min. :75 Min. :232.0
## 1st Qu.:3.000 1st Qu.:22.50 1st Qu.: 75.00 1st Qu.:81 1st Qu.:237.5
## Median :4.000 Median :24.00 Median : 79.00 Median :87 Median :246.0
## Mean :3.286 Mean :24.71 Mean : 82.57 Mean :85 Mean :254.9
## 3rd Qu.:4.000 3rd Qu.:31.50 3rd Qu.: 92.50 3rd Qu.:90 3rd Qu.:273.5
## Max. :5.000 Max. :34.00 Max. :103.00 Max. :91 Max. :284.0
## stat.avg stat.obp stat.slg stat.ops
## Min. :0.2430 Min. :0.2870 Min. :0.3940 Min. :0.7170
## 1st Qu.:0.2445 1st Qu.:0.3130 1st Qu.:0.4185 1st Qu.:0.7505
## Median :0.2470 Median :0.3420 Median :0.4640 Median :0.7680
## Mean :0.2576 Mean :0.3314 Mean :0.4539 Mean :0.7853
## 3rd Qu.:0.2635 3rd Qu.:0.3440 3rd Qu.:0.4795 3rd Qu.:0.8225
## Max. :0.2970 Max. :0.3770 Max. :0.5230 Max. :0.8660
## stat.babip ISO BB_rate K_rate
## Min. :0.2760 Min. :0.0970 Min. :0.04482 Min. :0.0755
## 1st Qu.:0.2820 1st Qu.:0.1750 1st Qu.:0.06648 1st Qu.:0.1876
## Median :0.2880 Median :0.1980 Median :0.09459 Median :0.2348
## Mean :0.2901 Mean :0.1963 Mean :0.09406 Mean :0.2053
## 3rd Qu.:0.2950 3rd Qu.:0.2335 3rd Qu.:0.12014 3rd Qu.:0.2457
## Max. :0.3130 Max. :0.2620 Max. :0.14573 Max. :0.2605# Top 10 by OPS
top_ops <- cubs_hit_2025 %>%
filter(!is.na(stat.ops), stat.plateAppearances >= 50) %>%
arrange(desc(stat.ops)) %>%
select(player, pos_code, stat.plateAppearances, stat.avg, stat.obp, stat.slg, stat.ops) %>%
head(10)
# Top 10 by HR
top_hr <- cubs_hit_2025 %>%
arrange(desc(stat.homeRuns)) %>%
select(player, pos_code, stat.plateAppearances, stat.homeRuns, stat.rbi, stat.totalBases) %>%
head(10)
top_ops## # A tibble: 7 × 7
## player pos_code stat.plateAppearances stat.avg stat.obp stat.slg stat.ops
## <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Michael Bu… 1B 592 0.261 0.343 0.523 0.866
## 2 Kyle Tucker RF 597 0.266 0.377 0.464 0.841
## 3 Seiya Suzu… DH 651 0.245 0.326 0.478 0.804
## 4 Pete Crow-… CF 647 0.247 0.287 0.481 0.768
## 5 Ian Happ LF 663 0.243 0.342 0.42 0.762
## 6 Nico Hoern… 2B 649 0.297 0.345 0.394 0.739
## 7 Dansby Swa… SS 645 0.244 0.3 0.417 0.717top_hr## # A tibble: 7 × 6
## player pos_code stat.plateAppearances stat.homeRuns stat.rbi stat.totalBases
## <chr> <chr> <int> <int> <int> <int>
## 1 Michael… 1B 592 34 90 274
## 2 Seiya S… DH 651 32 103 273
## 3 Pete Cr… CF 647 31 95 284
## 4 Dansby … SS 645 24 77 246
## 5 Ian Happ LF 663 23 79 239
## 6 Kyle Tu… RF 597 22 73 232
## 7 Nico Ho… 2B 649 7 61 236Visualization
# Order players by HR for a nicer bar chart
plot_df_hr <- cubs_hit_2025 %>%
mutate(player = reorder(player, stat.homeRuns))
ggplot(plot_df_hr, aes(x = player, y = stat.homeRuns)) +
geom_col() +
coord_flip() +
labs(
title = "Chicago Cubs 2025 — Home Runs",
x = "Player",
y = "Home Runs"
) +
theme_minimal()
# ISO by player bar chart
plot_df_iso <- cubs_hit_2025 %>%
filter(!is.na(ISO)) %>%
mutate(player = reorder(player, ISO))
ggplot(plot_df_iso, aes(x = player, y = ISO)) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(
title = "Chicago Cubs 2025 — Isolated Power (ISO) by Player",
x = "Player",
y = "ISO (Slugging - Batting Average)"
) +
theme_minimal(base_size = 13)