In 2016-17, the men of the Big Bash League (BBL) scored 1.92 more runs per over than the women of the WBBL. By 2025-26 that gap had shrunk to 0.64. This report follows every ball bowled in both leagues to show where the women caught up, and how.
Since 2016-17, the men’s run rate has only edged up 3%, while the women’s has jumped 24%. That is why the shaded gap keeps getting thinner. The latest WBBL season (7.98 runs per over) was faster than 5 of the 15 BBL seasons ever played.
Source: Cricsheet (2026), BBL and WBBL ball-by-ball data. Run rate = total runs per six legal balls. Super overs excluded.
Splitting each innings into the powerplay, middle and death overs shows where the extra runs came from. In the WBBL, the middle overs gained the most (+1.67 runs per over since 2016-17). The tiles turning from dark purple to green show batters no longer treating overs 7 to 15 as a quiet period. The BBL’s biggest gain was also in the middle overs.
Source: Cricsheet (2026), BBL and WBBL ball-by-ball data. Both leagues share one colour scale so they can be compared directly. Blank tiles are seasons before the WBBL data begins.
Each bubble is one team in one season. Teams up and to the left hit more boundaries and faced fewer dot balls. Press Play to watch the seasons go by. The orange WBBL bubbles start in the bottom right and drift into the space the blue BBL bubbles occupy. In 2025-26, the WBBL’s boundary rate (17.2% of balls) matched the BBL’s (17%). The difference that remains is mostly dot balls (37.1% against 34.5%).
Source: Cricsheet (2026), BBL and WBBL ball-by-ball data and match results. Boundary and dot percentages are of legal balls faced by that team.
How do the two Melbourne clubs compare with the rest of their league? Above the zero line means the team scored faster than the league average that season. The Renegades men have been below average in every one of their 8 seasons since 2018-19, including the year they won the title batting 0.41 runs per over slower than average. On the women’s side, both clubs swing above and below the line from year to year.
Source: Cricsheet (2026), BBL and WBBL ball-by-ball data. League average is the run rate of all teams in that league and season.
Use the filters to pick a team, a league or a range of seasons. The chart and the table are linked, so filtering one updates both. Click a point to highlight it and double click to clear.
Source: Cricsheet (2026), BBL and WBBL ball-by-ball data and match results.
The WBBL is not a slower copy of the BBL anymore. The women have closed most of the scoring gap, and they did it mainly by hitting more boundaries through the middle overs. What still separates the two leagues is the number of dot balls, which suggests the next step for WBBL batters is rotating the strike, not hitting harder. If the trend of the last ten seasons continues, the two leagues could be scoring at close to the same rate within a few summers.
Data. Ball-by-ball CSV files for every BBL and WBBL
match available on Cricsheet were downloaded and combined in a separate
script (prep_data.R). Super overs were removed. A legal
ball is any delivery that is not a wide or a no ball. Run rate is total
runs (including extras) per six legal balls. Boundary % and dot % are
shares of legal balls. Phases are overs 1 to 6 (powerplay), 7 to 15
(middle) and 16 to 20 (death). Seasons with fewer than 20 matches in the
data were left out so a partial season cannot distort the trend, which
is why the WBBL starts in 2016-17. Match winners come from the Cricsheet
match info files. Washed out matches with no balls bowled do not
appear.
Design. Colours follow the Okabe-Ito palette (Okabe & Ito, 2008) and the heatmap uses the viridis scale, both of which stay readable for common types of colour blindness. League colour is kept the same across charts. Charts were built with ggplot2 and converted to interactive charts with plotly. The explorer uses crosstalk to link the filters, chart and table.
Match data is from Cricsheet and is used under the Open Data Commons Attribution License. The data was aggregated into summary tables for this report. This report was built with R and the packages listed in the references.
I acknowledge the use of Claude (Anthropic, 2026) for editorial assistance in reviewing spelling, grammar, and written clarity, as well as technical guidance regarding R package syntax and troubleshooting web-hosting deployment issues; all analytical concepts, data transformations, visualization designs, and interpretations were independently directed and verified by the author.
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