Menu
A sports media company wanted deeper insight into player and team performance trends across multiple Indian Premier League (IPL) seasons. Their historical match data—batting, bowling, and team results—was stored in scattered CSV files without a central system for comparison. Management needed an interactive dashboard to highlight key performers, team dominance patterns, and match-winning factors such as toss decisions and venues.
As a data analyst, my objective was to design and develop a Power BI dashboard that would:
Consolidate multi-season IPL data into a single analytical model.
Enable dynamic exploration of player batting and bowling statistics.
Reveal team and venue-based winning patterns.
Support decision-making for commentators, analysts, and fantasy-league strategists.
I made sure to;
Data Preparation & Modeling
Combined datasets for players, matches, and venues using Power Query.
Cleaned null values, standardized player names, and built relationships among fact tables (Matches, Batting, Bowling) and dimension tables (Teams, Venues, Seasons).
Created calculated measures with DAX for Total Runs, Strike Rate, Economy, and Average.
Dashboard Design
Built interactive filters (season, batsman, bowler) to allow detailed comparisons.
Added key performance visuals:
Title Winner, Orange Cap, and Purple Cap indicators.
Batting & Bowling Stats Cards for individual player metrics.
Donut charts for Matches Won by Toss Decision and Result Type.
Bar charts for Team Wins per Season and Matches Won by Venue.
Applied color-coded themes (team colors, pitch backgrounds) for visual engagement.
Analytical Enhancements
Implemented dynamic DAX formulas to compute strike rates, economy rates, and average runs automatically when a player or season is selected.
Designed tooltip summaries showing comparative performance across seasons.
Validated results with ESPN Cricinfo datasets for accuracy.
The dashboard delivered a comprehensive and interactive overview of IPL performance trends, helping analysts quickly uncover performance insights and audience-driven storylines.
Impact:
Empowered analysts to generate player and match insights in minutes instead of hours.
Provided visual evidence for commentators’ narratives during live coverage.
Served as a data-driven framework for future predictive analysis of player and team performance.
Observation:
Virat Kohli leads the batting leaderboard with 6,634 runs, earning the Orange Cap, while D.J. Bravo dominates bowling with 183 wickets, securing the Purple Cap.
Batsmen like Abdul Samad show a strike rate of 139.02, indicating high impact in limited opportunities.
Interpretation:
Consistency and longevity remain key differentiators for elite players like Kohli and Bravo, while younger players such as Samad bring aggressive acceleration at critical moments.
Actionable Steps:
Team selectors can use this insight to identify players for specific match situations (e.g., powerplay hitters vs. finishers).
Coaches can design bowling matchups based on player strike rates and economy correlations visible in the dashboard.
Observation:
Mumbai Indians lead with 131 wins, followed by Chennai Super Kings (121) and Kolkata Knight Riders (114).
Newer teams like Gujarat Titans, despite being recent entrants, already show competitive performance (12 wins).
Interpretation:
Historical dominance stems from stable leadership and well-balanced squad compositions.
Teams with longer participation have a strategic edge due to experience and continuity.
Actionable Steps:
Use this trend for predictive modeling of match outcomes, emphasizing legacy team consistency.
Broadcasters can highlight historic rivalry data to improve audience engagement in pre-match analyses.
Observation:
Teams that chose to field after winning the toss achieved victory in 67.48% of matches.
Wins by wickets (53.58%) outnumber wins by runs (44.53%), indicating successful run chases are more common.
Interpretation:
Toss-winning teams increasingly prefer to field due to dew factors and chasing advantages in night matches.
Batting second allows teams to optimize strategy using real-time run rate targets.
Actionable Steps:
Teams should consider adopting chase-oriented strategies in venues with high humidity or under lights.
Analysts can develop venue-specific toss-decision playbooks using this dashboard’s data filters.
Observation:
Eden Gardens, Wankhede Stadium, and M. Chinnaswamy Stadium show the highest match win counts (45+, 37+, 36+ respectively).
These venues also produce more runs per match on average.
Interpretation:
These grounds favor batting conditions and have consistent high-scoring patterns, making them ideal for marquee or televised matches.
Actionable Steps:
Tournament planners can schedule high-profile games at these venues to maximize viewership.
Teams can adjust bowling lineups (e.g., fewer spinners at Wankhede) based on the venue’s historical match type data.
Observation:
Across seasons, the tournament recorded 25.49K total sixes and 10.66K sixes in the latest season, reflecting increasingly aggressive batting styles.
Interpretation:
Modern gameplay trends show a shift towards power-hitting and shorter formats influencing T20 strategy.
Actionable Steps:
Franchises should prioritize boundary-hitting capabilities during player auctions.
Marketing and sponsorship teams can leverage high-scoring data to promote “Entertainment Index” metrics to attract advertisers.
Observation:
Teams with deeper batting orders consistently outperform those relying on a few top performers.
Economical bowlers (Economy < 8.0) often dictate match outcomes, especially in smaller venues.
Interpretation:
Balanced team construction between middle-order stability and efficient death bowling remains the winning formula.
Actionable Steps:
Data models can simulate “optimal team composition” based on dashboard insights.
Training focus should shift toward middle-over control, where matches typically swing in T20 cricket.
Observation:
The Gujarat Titans’ championship victory underlines the success of strategic recruitment and match adaptability.
Overall tournament stats show increasing parity among teams, suggesting the league’s competitive depth.
Actionable Steps:
Teams can use this data to benchmark their performance against league-wide averages.
Analysts can develop predictive metrics (e.g., Win Probability Index) based on similar aggregated dashboards.
| Focus Area | Key Insight | Recommended Action |
|---|---|---|
| Batting Performance | Power hitters dominate strike rates | Prioritize aggressive openers and finishers |
| Team Wins | Legacy teams retain dominance | Build player retention strategies |
| Toss Decisions | Fielding first yields better results | Optimize chase strategies |
| Venues | Certain stadiums produce higher win rates | Plan venue-specific team compositions |
| Bowling Impact | Economy < 8 leads to match control | Train death-over specialists |
| League Trend | More sixes every season | Focus on power-hitting recruitment |
Users are encouraged to make full use of the embedded dashboard to explore and interact with its features. The dashboard has been designed for hands-on testing, allowing users to apply filters, use slicers, reset selections with the “Clear All” button, and navigate using bookmarks. This interactive experience helps users better understand the data, test various scenarios, and evaluate the dashboard’s functionality in a real-time environment.
ipl_matches (Fact Table)This table serves as the central fact table containing match-level data. It includes attributes such as:
match_date: Date on which the match was played.
id: Unique match identifier used to link with ball-by-ball data.
margin: Victory margin (by runs or wickets).
season: IPL season year.
superover: Indicator if a super over occurred.
player_of_match: Player awarded for best performance.
This table is linked to both the ball-by-ball data and the Date table, enabling aggregated insights at the match and season level.
ipl_ball_by_ball (Detail/Transaction Table)This table holds the granular-level ball-by-ball events for each match. It includes:
ball_number, batter, bowler: Key play-by-play identifiers.
batting_team, dismissal_kind, extra_type: Contextual information about each delivery.
batsman_run, extras_run: Numerical fields for run calculations.
A many-to-one relationship is established between this table and the ipl_matches table via the id field (match ID). This allows aggregation of statistics like total runs, 6s, 4s, and wickets at the match or player level.
Date (Dimension Table)This is a classic date dimension table, which helps perform time-based analysis. It includes:
Date
Day, Month, Quarter, and Year fields
A relationship exists between Date[Date] and ipl_matches[match_date], enabling flexible filtering and time-series visualizations like season trends, yearly performance comparisons, and match frequency analysis.
A one-to-many relationship connects ipl_matches to ipl_ball_by_ball via id.
A one-to-many relationship connects the Date table to ipl_matches via match_date.
These relationships follow a star schema, which ensures optimal performance and simplifies DAX calculations. The model supports filtering and slicing across matches, players, seasons, and dates, aligning well with the dashboard’s objectives of seasonal insights, performance tracking, and outcome analysis.
Data Cleaning: Using Power Query in Power BI, raw data was cleaned to remove duplicates, handle missing values, and standardize data formats (e.g., date/time, player/team names).
Data Transformation: The dataset was transformed to align with analysis requirements. This involved:
Splitting and merging columns.
Creating calculated columns for metrics like boundaries, total runs, and win margins.
Extracting key fields like venue, toss decision, and match result.
Table Relationships: Fact and dimension tables were created and relationships were established to form a star schema. This allowed for efficient filtering and aggregation.
Measures and Calculations: DAX (Data Analysis Expressions) was used to develop custom measures such as:
Total 6s and 4s per tournament.
Win percentages based on toss decisions.
Player-wise batting and bowling averages.
Wins per team per season.
Theme and Layout: A consistent design layout was created using Power BI’s formatting tools. Tabs or pages were structured based on the three key requirement areas:
Seasonal Achievements
Player Performance
Match Outcomes
Visualizations Used:
Bar and column charts for seasonal award winners.
Cards and KPIs to display IPL titles, Orange/Purple Cap winners.
Slicers and filters for selecting individual players or teams.
Pie and stacked bar charts for venue-wise and toss decision analysis.
Matrix/Table visuals for detailed player performance insights.
Slicers were added for dynamic filtering based on year, team, player, and venue.
Drill-through functionality was implemented to enable users to click on a player or team and view detailed performance metrics.
Cross-checked KPIs and visual summaries against source data to ensure accuracy.
Validated slicer interactions and drill-down features for usability.
Performance testing was carried out to ensure visual responsiveness and efficient data loading.
The final dashboard was published to the Power BI Service for online access.
Access permissions were configured for stakeholders.
A user guide was prepared to explain dashboard navigation and interpretation of visuals.
Potential future enhancements include real-time data updates through API integration, predictive analysis using machine learning models, and mobile optimization for better accessibility.
Emmanuel Olorunda
Copyright © 2025 | All Rights Reserved
Every project starts with a chat, Emmanuel is keen to conversation and open to discussing your project. he will pull in the right expertise when needed