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IPL 2008-2025 – Bowlers Performance Dashboard – Part 4 – Power BI

Below are DAX measures for bowler analytics, designed for our existing model that we created in First Part. These cover the most important bowler-related visuals used in IPL-style dashboards.

All formulas assume these columns exist in ball_by_ball_data (adjust names if needed):

  • bowler
  • ball_number
  • batter_runs
  • extras
  • is_wicket (or player_dismissed)
  • match_id

Relationship assumed:
ball_by_ball_data[match_id] → ipl_matches_data[match_id] (ACTIVE)

Part 1 : https://colorstech.net/power-bi/ipl-2008-2025-season-wise-highlights-part-1-power-bi-tutorial/

1. TOTAL BALLS BOWLED (BASE MEASURE)

Balls Bowled =
COUNTROWS(ball_by_ball_data)

If wides/no-balls should be excluded:

Legal Balls Bowled =
COUNTROWS(
    FILTER(
        ball_by_ball_data,
        ball_by_ball_data[extras] = 0
    )
)

2. OVERS BOWLED

Overs Bowled =
DIVIDE([Legal Balls Bowled], 6)

3. RUNS CONCEDED

Exclude byes and leg byes if column exists:

Runs Conceded =
SUM(ball_by_ball_data[batter_runs]) + SUM(ball_by_ball_data[extras])

If you want stricter bowling runs:

Runs Conceded =
SUM(ball_by_ball_data[batter_runs]) +
SUMX(
    FILTER(
        ball_by_ball_data,
        ball_by_ball_data[extras] <> "byes"
            && ball_by_ball_data[extras] <> "legbyes"
    ),
    ball_by_ball_data[extras]
)

4. WICKETS TAKEN

If is_wicket flag exists:

Wickets Taken =
SUM(ball_by_ball_data[is_wicket])

If using dismissal column:

Wickets Taken =
COUNTROWS(
    FILTER(
        ball_by_ball_data,
        NOT ISBLANK(ball_by_ball_data[player_dismissed])
    )
)

5. BOWLING AVERAGE

Bowling Average =
DIVIDE(
    [Runs Conceded],
    [Wickets Taken],
    BLANK()
)

Lower is better.

6. ECONOMY RATE

Economy Rate =
DIVIDE(
    [Runs Conceded],
    [Overs Bowled],
    BLANK()
)

7. STRIKE RATE (BOWLING)

Bowling Strike Rate =
DIVIDE(
    [Legal Balls Bowled],
    [Wickets Taken],
    BLANK()
)

Lower is better.

8. DOT BALLS

Dot Balls =
COUNTROWS(
    FILTER(
        ball_by_ball_data,
        ball_by_ball_data[batter_runs] = 0
            && ball_by_ball_data[extras] = 0
    )
)

9. DOT BALL PERCENTAGE

Dot Ball % =
DIVIDE(
    [Dot Balls],
    [Legal Balls Bowled],
    0
) * 100

10. FOURs & SIXES CONCEDED

Fours Conceded =
COUNTROWS(
    FILTER(
        ball_by_ball_data,
        ball_by_ball_data[batter_runs] = 4
    )
)

Sixes Conceded =
COUNTROWS(
    FILTER(
        ball_by_ball_data,
        ball_by_ball_data[batter_runs] = 6
    )
)

11. BOUNDARIES CONCEDED

Boundaries Conceded =
[Fours Conceded] + [Sixes Conceded]

12. MAIDEN OVERS (ADVANCED – OPTIONAL)

Requires over_number column.

Maiden Overs =
COUNTROWS(
    FILTER(
        SUMMARIZE(
            ball_by_ball_data,
            ball_by_ball_data[match_id],
            ball_by_ball_data[bowler],
            ball_by_ball_data[over_number],
            "RunsInOver", SUM(ball_by_ball_data[batter_runs] + ball_by_ball_data[extras])
        ),
        [RunsInOver] = 0
    )
)

13. TOP WICKET TAKERS (VISUAL SETUP)

Use:

  • Axis → bowler
  • Values → Wickets Taken
  • Visual filter → Top N → 10 by Wickets Taken

14. BEST ECONOMY RATE (MIN BALLS FILTER)

To avoid part-time bowlers:

Qualified Bowlers =
IF([Legal Balls Bowled] >= 120, 1, 0)

Apply filter:

  • Qualified Bowlers = 1

Then show:

  • Economy Rate
  • Wickets Taken

15. BOWLER PERFORMANCE SUMMARY TABLE (RECOMMENDED)

Use these fields:

Column
bowler
Overs Bowled
Runs Conceded
Wickets Taken
Economy Rate
Bowling Average
Bowling Strike Rate
Dot Ball %

This becomes your Bowler Analytics Master Table.

16. SEASON-WISE BOWLER ANALYSIS

All above measures automatically become season-wise when you add:

  • season from ipl_matches_data to the visual

Thanks to the active relationship.

17. FINAL KPI CARDS YOU CAN ADD

  • Best Economy:
Best Economy =
MINX(
    VALUES(ball_by_ball_data[bowler]),
    [Economy Rate]
)
  • Highest Wickets:
Most Wickets =
MAXX(
    VALUES(ball_by_ball_data[bowler]),
    [Wickets Taken]
)

IMPORTANT MODELLING TIP

Do NOT create calculated columns for these metrics.
Always use measures for bowling performance.

Below is a detailed, structured explanation of each important bowler-related DAX metric, written for learning and teaching purposes. This is aligned with your IPL Power BI model and suitable for documentation, blogs, or video narration.

1. Balls Bowled

What it measures

  • Counts the total number of deliveries bowled by a bowler.
  • Includes all balls recorded in the ball-by-ball table.

Why it matters

  • Forms the base for almost every bowling metric.
  • Without balls bowled, you cannot calculate overs, strike rate, or economy.

How it is used

  • As a raw workload indicator.
  • To filter part-time bowlers from specialist bowlers.
  • Used in combination with wickets to calculate bowling strike rate.

Interpretation

  • Higher value indicates greater bowling involvement.
  • Needs context; high balls without wickets may indicate poor effectiveness.

2. Legal Balls Bowled

What it measures

  • Counts only legal deliveries.
  • Excludes wides and no-balls if extras logic is applied.

Why it matters

  • Cricket overs consist only of legal balls.
  • Prevents overestimation of overs bowled.

How it is used

  • Essential for calculating overs bowled correctly.
  • Used in strike rate and dot ball percentage.

Interpretation

  • More accurate than total balls for performance analysis.
  • Reflects actual workload under match rules.

3. Overs Bowled

What it measures

  • Converts legal balls into overs.

Why it matters

  • Overs are the standard unit for evaluating bowlers.
  • Used directly in economy rate calculations.

How it is used

  • Displayed in scorecards and bowling summaries.
  • Used to compare bowlers with different workloads.

Interpretation

  • Higher overs generally mean higher trust from the captain.
  • Must be evaluated alongside economy and wickets.

4. Runs Conceded

What it measures

  • Total runs given away by a bowler.
  • Includes batter runs and applicable extras.

Why it matters

  • Direct indicator of how expensive a bowler is.
  • Forms the numerator for economy rate and bowling average.

How it is used

  • Used in economy rate calculations.
  • Compared across bowlers in the same match or season.

Interpretation

  • Lower runs conceded for the same overs indicate better control.
  • Needs wickets context; attacking bowlers may concede more runs.

5. Wickets Taken

What it measures

  • Number of dismissals credited to the bowler.

Why it matters

  • Wickets are the most impactful outcome of bowling.
  • Determines bowling average and strike rate.

How it is used

  • Used to rank bowlers.
  • Primary metric for awards like Purple Cap.

Interpretation

  • Higher wickets indicate greater match-winning ability.
  • Should be evaluated with balls bowled for fairness.

6. Bowling Average

What it measures

  • Average number of runs conceded per wicket taken.

Why it matters

  • Shows how costly each wicket is.
  • One of the oldest bowling performance metrics.

How it is used

  • Used to compare wicket-taking efficiency.
  • Useful for evaluating strike bowlers.

Interpretation

  • Lower bowling average is better.
  • Values below 25 are considered excellent in T20 cricket.

7. Economy Rate

What it measures

  • Average runs conceded per over.

Why it matters

  • Measures run control.
  • Extremely important in T20 formats like IPL.

How it is used

  • Used to identify economical bowlers.
  • Used in death over analysis.

Interpretation

  • Economy below 7.5 is excellent in IPL context.
  • Needs to be balanced with wickets taken.

8. Bowling Strike Rate

What it measures

  • Number of balls bowled per wicket.

Why it matters

  • Indicates how frequently a bowler takes wickets.

How it is used

  • Used to identify attacking bowlers.
  • Complements bowling average.

Interpretation

  • Lower strike rate is better.
  • Strike rate below 20 is elite in T20s.

9. Dot Balls

What it measures

  • Number of deliveries where no run is scored.
  • Excludes extras.

Why it matters

  • Dot balls build pressure.
  • Often lead to wickets in subsequent deliveries.

How it is used

  • Used to evaluate control and discipline.
  • Key metric in modern T20 analysis.

Interpretation

  • Higher dot balls indicate sustained pressure.
  • Particularly important in powerplay overs.

10. Dot Ball Percentage

What it measures

  • Percentage of legal deliveries that are dot balls.

Why it matters

  • Normalizes dot balls across bowlers with different workloads.

How it is used

  • Used to compare bowlers fairly.
  • Helpful in selecting economical bowlers.

Interpretation

  • Dot ball percentage above 40% is excellent.
  • Strong indicator of pressure creation.

11. Fours Conceded

What it measures

  • Number of boundary fours conceded.

Why it matters

  • Indicates leakage through gaps.
  • Useful for field placement analysis.

How it is used

  • Used with sixes conceded to evaluate boundary control.

Interpretation

  • Lower values suggest better line and length.
  • Contextual with pitch conditions.

12. Sixes Conceded

What it measures

  • Number of sixes hit off a bowler.

Why it matters

  • Sixes dramatically impact economy.
  • Crucial in death overs evaluation.

How it is used

  • Used to identify bowlers vulnerable at the death.

Interpretation

  • Lower sixes conceded indicate effective execution.
  • Needs overs context.

13. Boundaries Conceded

What it measures

  • Total fours and sixes conceded.

Why it matters

  • Quick indicator of boundary leakage.

How it is used

  • Used in performance summaries.
  • Helps identify defensive specialists.

Interpretation

  • Lower boundary count indicates tighter bowling.
  • Useful in matchups analysis.

14. Maiden Overs

What it measures

  • Overs where no runs are conceded.

Why it matters

  • Extremely rare and valuable in T20 cricket.

How it is used

  • Highlighted as special achievements.
  • Shows exceptional control.

Interpretation

  • Even one maiden can change match momentum.
  • Strong indicator of dominance.

15. Qualified Bowlers Filter

What it measures

  • Ensures bowlers have bowled a minimum number of balls.

Why it matters

  • Prevents misleading rankings.
  • Removes part-time bowlers.

How it is used

  • Applied as a visual-level filter.

Interpretation

  • Improves analytical accuracy.
  • Makes comparisons fair.

16. Top Wicket Takers Visual

What it measures

  • Ranks bowlers by wickets taken.

Why it matters

  • Identifies season leaders.
  • Popular fan and analyst metric.

How it is used

  • Top N visuals in Power BI.

Interpretation

  • Highlights consistent wicket-takers.
  • Should be paired with economy.

17. Season-wise Bowling Analysis

What it measures

  • Bowling metrics split by IPL season.

Why it matters

  • Shows performance trends over time.
  • Helps evaluate consistency.

How it is used

  • With season slicers and line charts.

Interpretation

  • Rising trends indicate improvement.
  • Useful for retention decisions.

Final Note

These bowling metrics together create a complete bowler performance framework. No single metric defines a great bowler; the true value emerges when wickets, economy, strike rate, and pressure metrics are analyzed together.

If you want, I can next:

  • Create narration-ready explanations
  • Convert this into a blog or PDF
  • Design the exact visuals layout in Power BI