Useful for MS Excel, Power BI or Tableau

The Dataset IPL 2008 – 2024 is Available Here : Download from Kaggle

Power BI and Player Info File Available Here : https://github.com/slidescope/ipl-data-analysis

The IPL deliveries and matches datasets are commonly used datasets for analyzing Indian Premier League (IPL) cricket statistics. Here’s a short explanation of each:

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About IPL Matches Dataset

This dataset provides summary-level information about each IPL match.

Columns

1. match_id: Unique identifier for each match.
2. season: Year of the IPL season.
3. date: The date the match was played.
4. team1: Name of the first team.
5. team2: Name of the second team.
6. venue: The stadium where the match was played.
7. toss_winner: Team that won the toss.
8. toss_decision: Decision made by the toss winner (bat or field).
9. winner: The team that won the match.
10. result: Type of result (e.g., win by runs, wickets, or a tie).
11. player_of_match: Best-performing player in the match.
12. umpires: Names of the umpires officiating the match.

Use Cases

• Analyzing match outcomes, toss decisions, and team performances.
• Studying trends in IPL seasons over the years.

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IPL Deliveries Dataset

This dataset provides ball-by-ball details of each IPL match.

Columns

1. match_id: Identifier linking to the matches dataset.
2. inning: The inning number (1 or 2).
3. batting_team: Name of the batting team.
4. bowling_team: Name of the bowling team.
5. over: Over number (1 to 20).
6. ball: Ball number within the over (1 to 6).
7. batter: Name of the batsman facing the ball.
8. non_striker: Name of the batsman at the non-striker’s end.
9. bowler: Name of the bowler delivering the ball.
10. batsman_runs: Runs scored by the batsman off the delivery.
11. extra_runs: Runs due to extras (wide, no-ball, etc.).
12. total_runs: Total runs scored on the ball (batsman + extras).
13. dismissal_kind: Type of dismissal, if any (e.g., bowled, caught).
14. player_dismissed: Name of the dismissed player (if any).

Use Cases

• Ball-by-ball performance analysis of players and teams.
• Studying batsman and bowler stats.
• Calculating strike rates, economy rates, and player contributions.

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Relationship Between the Two Datasets

• The match_id column connects the matches dataset and the deliveries dataset.
• The matches dataset provides high-level match details, while the deliveries dataset offers granular ball-by-ball data.

These datasets are great for cricket enthusiasts, data analysts, and machine learning practitioners who want to analyze trends, make predictions, or derive insights from IPL data.

How to find Match Wise Bowling Performance

MatchTotalRuns = 
SUMMARIZE(
    deliveries,
    deliveries[match_id],               -- Group by match_id
    deliveries[bowler], 
    "TotalRuns", SUM(deliveries[total_runs]), -- Calculate sum of total_runs for each match
    "Batting Team", MAX(deliveries[batting_team]),
    "Wickets", SUM(deliveries[is_wicket]),
    "Overs", DISTINCTCOUNT(deliveries[over])
)