Cricket in India is a national obsession that binds millions of fans across generations. Cricket fans today want more than scorecards and commentary, they crave insights that explain momentum shifts, player contributions and real probabilities of winning.
Data analytics transforms raw numbers into stories about the match. Run rates over time, strike rate comparisons, partnership strength and bowler economy reveal the deeper texture of play. These insights are no longer confined to analysts in dressing rooms; they can be made interactive and accessible to anyone with tools like Python and Pandas and Plotly.
A sports and gaming platform like BETVIBE integrates data-driven features, offering cricket followers smart tools, live statistics and enhanced engagement that go beyond traditional betting or casual observation. Fans no longer passively consume cricket, they interact with real-time data to interpret the game in richer ways.
This article explains how to build a cricket match analytics dashboard in Python using pandas and Plotly. Readers will learn to prepare datasets, design core visualisations and create an interactive dashboard that mirrors the kind of smart insights seen on advanced platforms.
How to Set Up the Project in Python?
Find below a list of the tools you need to set up the project in Python.
- pandas. Pandas handles data manipulation and filtering. It prepares raw match datasets into structured tables that support precise analysis.
- NumPy. NumPy performs efficient mathematical operations. It processes large numeric arrays quickly and supports calculations for strike rate or win probability.
- Plotly. Plotly generates interactive browser-ready visualisations. It allows fans to explore match data with hover effects, filters and dynamic dashboards.
- Jupyter Notebook. Jupyter creates a live coding workspace. It lets analysts test calculations, preview charts and document every step.
Find below a list of the installation commands to use in Python.
- pip install pandas
- pip install numpy
- pip install plotly
- pip install jupyterlab
Python is the best language for this work because it is simple to use, highly flexible and supported by specialised libraries for sports data. Pandas makes handling match datasets easy, whether you are calculating averages, filtering by player or generating strike rates. Plotly then takes those processed numbers and turns them into interactive charts where fans can hover, zoom and filter data directly.
How to Collect and Prepare Cricket Data?
Cricket match data is widely available. CSV files from Kaggle, JSON feeds or APIs such as CricAPI provide ball-by-ball records of runs, wickets, overs and players.
Find below a list of steps to prepare cricket data.
- Remove nulls. Users remove missing values to keeps calculations consistent. Dropping null runs or overs ensures metrics such as strike rate remain accurate.
- Rename columns. Users rename columns to improve clarity. For example, “batsman_runs” becomes “runs_scored” for easier interpretation.
- Create calculated fields. Users create calculated fields like strike rate or economy rate to add valuable new dimensions. These calculated fields reveal batting efficiency and bowling control.
Find below the code needed to prepare and enrich cricket data.
df = pd.read_csv("cricket_matches.csv")
df = df.dropna(subset=["runs"])
df.rename(columns={"batsman_runs": "runs_scored"}, inplace=True)
df["strike_rate"] = (df["runs_scored"] / df["balls_faced"]) * 100
df["economy_rate"] = df["runs_conceded"] / df["overs_bowled"]
How to Build Core Visualisations with Plotly
Find below a list of key cricket analytics charts.
- Run rate over time. Run rate over time is a line chart that tracks cumulative runs across overs. It shows acceleration phases and batting slumps.
- Player performance bar chart. Player performance bar is a chart that compares batsmen by total runs or strike rates. It highlights top scorers and efficient innings.
- Bowler economy heatmap. Bowler economy heatmap is a heatmap that plots overs against runs conceded. It identifies bowlers who restricted runs at critical points.
Run rate over time example code:
run_rate = df.groupby("over")["runs_scored"].sum().cumsum().reset_index()
fig = px.line(run_rate, x="over", y="runs_scored",
title="Cumulative Runs Over Time")
fig.show()
Player performance bar charts example code:
Top_scorers = df.groupby('Batsman')['Runs'].sum().nlargest(5).reset_index()
fig = px.bar(top_scorers, x='Batsman', y='Runs', title="Top 5 Scorers")
fig.show()
Bowler Economy Heatmap example code:
fig = px.density_heatmap(df, x="Over", y="Bowler", z="RunsConceded", title="Bowler Economy Heatmap")
fig.show()
How to Design an Interactive Dashboard?
Find below a list of steps to design a cricket analytics dashboard.
- Select visualisations. Designers select line charts, bar charts and heatmaps. Selecting these visuals gives batting, bowling and momentum insights together.
- Add filters. Designers add dropdown menus for team, player and match. Adding filters allows fans to drill into specific performances.
- Layout dashboard. Designers lay out charts in a browser dashboard using Plotly Dash. Laying them out guarantees easy navigation between batting, bowling and match context.
Find below example code that creates a simple cricket analytics dashboard. A dropdown menu lets users choose a match such as India vs Australia. The layout contains three graphs: one for run rate trends, one for batsman performance and one heatmap for bowler economy. It provides the basic structure where filters and visuals can be connected to cricket data.
app.layout = html.Div([
dcc.Dropdown(options=[{"label": "India vs Australia", "value": "IND-AUS"}], id="match_filter"),
dcc.Graph(id="run_rate_chart", figure=fig),
dcc.Graph(id="batsman_chart"),
dcc.Graph(id="bowler_heatmap")
])
How to Expand the Dashboard with Advanced Metrics?
The dashboard can be expanded with advanced metrics by adding win probability curves, key partnerships and momentum shifts. Win probability curves project the likelihood of each team winning as the match progresses. Key partnerships highlight the impact of batting pairs in building innings. Momentum shifts track rapid swings in scoring that define turning points. These features move analysis from descriptive statistics to predictive insights. They provide richer context and show how games develop beyond basic runs and wickets. BETVIBE integrates similar advanced cricket tools in its sportsbook. BETVIBE enriches fan experience by combining live statistics, predictive analytics and gamified engagement features.
Find below example code for a win probability column.
df["win_prob"] = np.clip(df["runs_scored"].cumsum() / 300, 0, 1)
Final Thoughts and Next Steps
Python enables cricket fans to explore games in data-rich ways, turning simple scoreboards into interactive experiences that highlight batting, bowling and match dynamics with clarity. Tools like pandas and Plotly make it possible to build dashboards that track run rates, player strike rates and bowling economy across formats, offering fans deeper perspectives than basic commentary or summaries provide.
Future projects can go further by integrating live ball-by-ball APIs to deliver instant updates, creating mobile-ready dashboards so insights remain accessible on the go and enabling social sharing so communities can discuss and compare visualised match data in real time. These extensions shift cricket engagement from passive observation to active exploration, blending data science with sports passion. As platforms like BETVIBE cricket insights have shown, the future of sports isn’t just watching, it’s interacting with the data in real time.
