Our model that we create in Part 1 of this project, is already strong enough to calculate batsman performance accurately. Below are the exact DAX measures and setup you need for:

1. Top run scorers per season
2. Batting average per season
3. Most sixes & most boundaries
4. Strike rate of top 10 batsmen

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

• match_id
• batter or batsman
• batter_runs or batsman_runs
• ball_number
• is_four (optional)
• is_six (optional)

And this relationship is active:
ball_by_ball_data[match_id] → ipl_matches_data[match_id]

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1. TOTAL RUNS BY BATSMAN (BASE MEASURE)

Create this first — everything else depends on it.

Total Runs =
SUM(ball_by_ball_data[batter_runs])

Use this with:

• Axis → batter
• Values → Total Runs
• Filter → Top N (Top 10 by Total Runs)

This gives you Top Run Scorers.

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2. TOTAL RUNS PER SEASON (FOR SEASON-WISE ANALYSIS)

Because season is in ipl_matches_data, relationship will propagate automatically:

Season Runs =
CALCULATE(
    [Total Runs],
    ALLEXCEPT(
        ipl_matches_data,
        ipl_matches_data[season]
    )
)

For Top Scorers Per Season, use:

• Axis → batter
• Legend → season
• Values → Total Runs
• Visual Filter → Top N (10 by Total Runs)

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3. BALLS FACED BY BATSMAN

This is required for average and strike rate.

Balls Faced =
COUNTROWS(
    FILTER(
        ball_by_ball_data,
        ball_by_ball_data[batter_runs] >= 0
    )
)

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4. DISMISSALS (OUT COUNT)

If you have player_dismissed column:

Times Out =
COUNTROWS(
    FILTER(
        ball_by_ball_data,
        NOT ISBLANK(ball_by_ball_data[player_dismissed])
            && ball_by_ball_data[player_dismissed] = SELECTEDVALUE(ball_by_ball_data[batter])
    )
)

If you do not have dismissal data, tell me — I’ll give you an alternate safely.

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5. BATTING AVERAGE PER SEASON

Batting Average =
DIVIDE(
    [Total Runs],
    [Times Out],
    0
)

Use this in a table with:

• season
• batter
• Batting Average

Add a Top N filter if needed.

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6. TOTAL SIXES

If is_six column exists:

Total Sixes =
SUM(ball_by_ball_data[is_six])

If not, use run-based detection:

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

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7. TOTAL BOUNDARIES (4s + 6s)

Total Boundaries =
COUNTROWS(
    FILTER(
        ball_by_ball_data,
        ball_by_ball_data[batter_runs] = 4
            || ball_by_ball_data[batter_runs] = 6
    )
)

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8. STRIKE RATE (CRITICAL METRIC)

Strike Rate = (Runs / Balls) × 100

Strike Rate =
DIVIDE(
    [Total Runs],
    [Balls Faced],
    0
) * 100

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9. TOP 10 BATSMEN BY STRIKE RATE (PROPER METHOD)

1. Create a table visual:
   • Rows → batter
   • Values → Strike Rate, Total Runs, Balls Faced
2. Add a visual-level filter:
   • Filter on batter
   • Set Top N → 10
   • By value → Total Runs
   • Apply filter

This ensures:

• You do NOT get random tailenders
• Only genuine top run getters by strike rate appear

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10. MOST SIXES PER SEASON (VISUAL READY)

Use:

• Axis → season
• Values → Total Sixes
• Legend → batter
• Filter → Top N = 5 by Total Sixes

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11. MOST BOUNDARIES PER SEASON

Use:

• Axis → season
• Values → Total Boundaries
• Legend → batter

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12. OPTIONAL – PERFORMANCE SUMMARY TABLE

Use this in one table visual:

Column
batter
Total Runs
Balls Faced
Strike Rate
Total Sixes
Total Boundaries
Batting Average

This becomes your Master Batsman Performance Table.

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13. DATA QUALITY SAFETY (VERY IMPORTANT)

If you see inflated strike rates:

• Exclude extras-only deliveries if column exists:

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

Tell me if you want this refined.

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14. FINAL KPI CARDS YOU CAN ADD

• Highest Individual Total Runs:

Max Runs by Batsman =
MAXX(
    VALUES(ball_by_ball_data[batter]),
    [Total Runs]
)

• Best Strike Rate:

Best Strike Rate =
MAXX(
    VALUES(ball_by_ball_data[batter]),
    [Strike Rate]
)