SNSAI NIHAL KONDUTILet’s talk ↗

Data / Visual storytelling

Untold Stories of IPL

Beyond the score.
Into the story.

FOCUSData analysis · Visualization · Report writing
STACKPython · Pandas · Seaborn · Plotly · Matplotlib
PROJECTPublic GitHub repository
Two team-versus-opponent win percentage heatmaps from the published IPL report
Rivalry heatmaps · Figures 13–14 from the published team report View full size ↗
2008–2024dataset coverage
7visualization approaches
3project contributors

An exploratory study of IPL history that connects player consistency, playoff pressure, rivalry patterns, and franchise brand value.

01 / THE CHALLENGE

The scoreboard is only the beginning.

Aggregate wins and title counts show what happened, but leave out the patterns behind long-term dominance, pressure, and fan loyalty.

02 / THE SOLUTION

A deliberate path through the problem.

Match-level records, ball-by-ball data, and franchise brand values are explored through several visual forms. Each chart is chosen for a specific question rather than repeating the same ranking.

03 / BUILD DETAILS

The choices that shape the work.

01

Consistency over time

Rose charts explore repeat top scorers; retention visualizations place continuity alongside team success.

02

Pressure and rivalries

Box plots compare league and playoff scoring, while heatmaps and chord diagrams reveal head-to-head patterns.

03

Performance and popularity

Line and bubble charts place brand value alongside competitive results without treating correlation as causation.

04 / OUTCOME

What the project delivers.

The project publishes a Jupyter notebook and a detailed report. Sai Nihal’s credited contributions are data analysis, visualization, and report writing.

05 / DESIGN & ENGINEERING TAKEAWAY

Start with a better question.

The project’s strongest shift is from leaderboard reporting to question-led analysis: a rivalry, a pressure situation, or a consistency pattern gives the data a narrative.