Introduction

The Australian Women’s Cricket team have wrapped up their very long international season for 2024/2025 with another T20 series sweep, this time winning 3-0 over the White Ferns.

And who was at the centre of the action? Beth Mooney piling on the runs. Naturally.

New year, same run scoring machine.

Cricket Australia reported that, in 2025, Moons has scored “575 runs at an average of 72”. Let’s dig into the details of this scoring spree from the ever-metronomic Mooney.

Set up and retrieve data

We’ll use ball-by-ball data provided by Cricsheet.org, across the matches played by Australia in 2025 (T20Is, ODIs, and Test):

# Load libraries ---------------------------------------------------------------

library(cricketdata) # for retrieving cricket data from ESPNCricinfo and Cricsheet

## Warning: package 'cricketdata' was built under R version 4.4.2

library(dplyr)       # for tidying data
library(gt)          # for visualising data tables

## Warning: package 'gt' was built under R version 4.4.2

library(showtext)    # for plotting
library(ggplot2)     # for plotting

# Retrieve ball-by-ball data ---------------------------------------------------

# Women's T20Is
t20s_bbb <- fetch_cricsheet(
  type = "bbb",
  competition = "t20s",
  gender = "female")

# Women's ODIs
odis_bbb <- fetch_cricsheet(
  type = "bbb",
  competition = "odis",
  gender = "female")

# Women's Tests
tests_bbb <- fetch_cricsheet(
  type = "bbb",
  competition = "tests",
  gender = "female")

Tidy the data

Let’s get the retrieved data sets into more focused subsets, focusing on balls where Beth Mooney was on strike:

# Filter to matches played in 2025 and Beth Mooney on strike -------------------

# T20s
t20s_bbb_2025_mooney <- t20s_bbb %>%
  filter(
    start_date > "2025-01-01" & striker == "BL Mooney") %>%
  mutate(
    competition = "T20I",
    start_date = as.character(start_date) # For binding purposes in next step
    )

# ODIs
odis_bbb_2025_mooney <- odis_bbb %>%
  filter(
    start_date > "2025-01-01" & striker == "BL Mooney") %>%
  mutate(competition = "ODI")

# Test
tests_bbb_2025_mooney <- tests_bbb %>%
  filter(
    start_date > "2025-01-01" & striker == "BL Mooney") %>%
  mutate(competition = "Test")

# Bind subsets into one data frame ---------------------------------------------

mooney_in_2025 <- bind_rows(
  t20s_bbb_2025_mooney, odis_bbb_2025_mooney, tests_bbb_2025_mooney)

A glance off the pads: Batting stats by format

mooney_in_2025_summary <- mooney_in_2025 %>%
  mutate(
    match_innings_id = paste0(match_id, innings, sep = "_"),
    is_ball_faced = case_when(
      wides == 1 ~ 0, # Wides do not count as a ball faced by the batter
      TRUE       ~ 1),
    wicket_counting_helper = case_when(
      player_dismissed == striker ~ 1,
      TRUE                        ~ 0)) %>%
  group_by(competition) %>%
  summarise(
    runs_off_bat = sum(runs_off_bat),
    balls_faced = sum(is_ball_faced),
    innings_batted = length(unique(match_innings_id)),
    wickets_lost = sum(wicket_counting_helper),
    .groups = "drop"
  ) %>%
  mutate(
    average_in_2025 = round(runs_off_bat / wickets_lost, 2),
    strike_rate = round(runs_off_bat / balls_faced * 100, 2))

mooney_in_2025_summary_table <- mooney_in_2025_summary %>%
  gt(rowname_col = "competition") %>%
  tab_header(
    title = md("**Piling on the runs to see out the 2024/25 season**"),
    subtitle = "Mooney's batting stats from internationals - January to March 2025") %>%
  cols_label(
    runs_off_bat = "Runs",
    balls_faced = "Balls faced",
    innings_batted = "Innings",
    wickets_lost = "Wickets lost",
    average_in_2025 = "Avg. in 2025",
    strike_rate = "Strike rate") %>%
  tab_style(
    style = cell_fill(color = "#9AD4D6"),
    locations = cells_body(
      rows = competition == "T20I")) %>%
  tab_source_note(
    source_note = "Data source: Cricsheet.org")

When we look at Mooney’s batting separated by format, we can see her dominance in recent T20s more clearly, where she averaged 95 runs with a strike rate of 154, accumulating nearly 400 runs across only 6 innings!

When does Moons hit the gas?

I was interested in whether there has been a pattern in when Moons tends to accelerate with her batting. Focusing on T20I innings, I decided to plot her cumulative runs scored against cumulative balls faced in each innings:

t20s_bbb_2025_mooney_tidy <- t20s_bbb_2025_mooney %>%
  mutate(
    is_ball_faced = case_when(
      wides == 1 ~ 0, # Wides do not count as a ball faced by the batter
      TRUE       ~ 1)) %>%
  # Filter out wides
  filter(is_ball_faced == 1) %>%
  group_by(match_id) %>%
  mutate(
    balls_faced_cumulative = cumsum(is_ball_faced),
    runs_scored_cumulative = cumsum(runs_off_bat)) %>%
  ungroup() %>%
  mutate(
    balls_faced_grouping = case_when(
      balls_faced_cumulative <= 10   ~ "1-10",
      balls_faced_cumulative > 10 &
        balls_faced_cumulative <= 20 ~ "11-20",
      balls_faced_cumulative > 20 &
        balls_faced_cumulative <= 30 ~ "21-30",
      balls_faced_cumulative > 30 &
        balls_faced_cumulative <= 40 ~ "31-40",
      balls_faced_cumulative > 40 ~ "41+"))

# Prep to plot -----------------------------------------------------------------

# Turn on {showtext} functionality
showtext_auto()

# Add Google Fonts
font_add_google("IBM Plex Sans Condensed", "plexc")

# Build plot -------------------------------------------------------------------

plot_t20_scoring_rate <- ggplot() +
  geom_point(
    data = t20s_bbb_2025_mooney_tidy,
    aes(
      x = balls_faced_cumulative, y = runs_scored_cumulative,
      group = match_id),
    size = 3, shape = 16, alpha = 0.2) +
  geom_smooth(
    data = t20s_bbb_2025_mooney_tidy,
    aes(
      x = balls_faced_cumulative, y = runs_scored_cumulative,
      color = factor(balls_faced_grouping)),
    method = "lm", se = F, linewidth = 2) +
  labs(
    title = "When does Beth Mooney hit the gas? From start to finish!",
    subtitle = "Beth Mooney's batting in T20Is - January to March 2025",
    x = "Balls faced (cumulative)",
    y = "Runs scored (cumulative)",
    caption = "Data source: Cricsheet.org") +
  scale_colour_brewer(palette = "Dark2") +
  theme_minimal() +
  theme(
    legend.position = "none",
    text = element_text(family = "plexc"),
    plot.title = element_text(size = rel(3), face = "bold"),
    plot.subtitle = element_text(size = rel(2.5)),
    plot.caption = element_text(
      size = rel(2), margin = margin(t = 10)),
    axis.title = element_text(size = rel(2.5)),
    axis.text = element_text(size = rel(2))
  )

I genuinely laughed out loud when I generated this plot:

I plotted separate linear trendlines for balls 1-10, 11-20, 21-30, 31-40, and 40+. The slope of each trendline is pretty much the same! Which tells us that Moons’ nickname of “Ms Consistent” is well-deserved in many ways - her rate of scoring in recent T20Is has been remarkably even right through an innings.

There’s only 6 T20I innings represented here, so it’d be worth including more innings to see if a more defined “acceleration phase” is evident or not. I’d also be interested to see what this plot looks like for other batters.