Aitana Bonmati vs Alexia Putellas Midfield Influence Calculator

The Aitana Bonmati vs Alexia Putellas Midfield Influence Calculator compares their passing, pressing, chance creation and positional impact to quantify overall midfield dominance per match.

What Is a Aitana Bonmati vs Alexia Putellas Midfield Influence Calculator?

This calculator turns match metrics into a single influence index for each player, then compares the two. It focuses on midfield actions that drive possession, chance creation, and defensive balance. Instead of relying on highlights or raw totals, it weights actions by how they shape a team’s attack and control. The result is a fair, repeatable way to evaluate midfield impact across matches or seasons.

Both Aitana Bonmatí and Alexia Putellas excel in different phases of play. One may tilt a match with progressive carries and press resistance. The other may swing it with final-third passing and timing between the lines. The calculator captures these patterns by blending creative output, ball retention, and defensive work into one score.

The Mechanics Behind Aitana Bonmati vs Alexia Putellas Midfield Influence

The calculator aggregates many match events into three dimensions: Creative Influence, Control and Retention, and Defensive Impact. Each dimension becomes a sub-score, then the tool combines them into a single Influence Index. Normalization keeps the results comparable across different match tempos and opponents.

• Creative Influence tracks how often a player advances play and creates chances, using progressive passes, key passes, and expected assists.
• Control and Retention measures possession stability with pass completion, carry retention, and turnover rate.
• Defensive Impact evaluates pressures, tackles, interceptions, and recoveries, adjusted to a per-90-minute basis.
• Normalization converts raw numbers into standardized values so a high-possession match does not skew the result.
• Weighting assigns more value to actions that move the ball into dangerous zones or prevent transitions.

Once each sub-score is set, the calculator blends them into a 0–100 Influence Index for each player. A head-to-head comparison shows the score gap and a probability estimate of who had greater influence in the selected sample.

Formulas for Aitana Bonmati vs Alexia Putellas Midfield Influence

The calculator relies on transparent, additive formulas. You can adjust weights if your match model values specific actions more. Normalization uses z-scores or min–max scaling to keep metrics on the same scale.

• Normalization (z-score): z(X) = (X − mean) / standard deviation. Use per-90 rates to align minutes.
• Creative Influence Score (CIS): CIS = 0.35·z(xA90) + 0.25·z(KP90) + 0.20·z(ProgP90) + 0.20·z(F3E90).
• Control and Retention Score (CRS): CRS = 0.40·z(Pass%) + 0.25·z(CarryRet%) + 0.20·z(TouchesMid90) − 0.15·z(Turnovers90).
• Defensive Impact Score (DIS): DIS = 0.40·z(PressSucc90) + 0.35·z(TI90) + 0.25·z(Rec90).
• Influence Index (II): II = 100 · minmax( 0.5·CIS + 0.3·CRS + 0.2·DIS ). Min–max rescales to 0–1 before multiplying by 100.
• Head-to-Head Probability: P(Aitana higher) = 1 / (1 + e^(−k·(II_Aitana − II_Alexia))), with k = 0.25 by default.

Variables: xA90 (expected assists per 90), KP90 (key passes per 90), ProgP90 (progressive passes per 90), F3E90 (final-third entries per 90), Pass% (completed passes divided by attempts), CarryRet% (carries that keep possession), TouchesMid90 (touches in middle third per 90), Turnovers90 (dispossessed + miscontrols per 90), PressSucc90 (successful pressures per 90), TI90 (tackles + interceptions per 90), Rec90 (ball recoveries per 90). You can swap z-scores for percentile ranks if you prefer.

Inputs and Assumptions for Aitana Bonmati vs Alexia Putellas Midfield Influence

Collect inputs from match reports or event data tools. Use per-90-minute rates to standardize comparisons. For small samples, include context like opponent strength and team possession share.

• xA90, KP90, ProgP90, and F3E90 to capture creative output and territory gain.
• Pass%, CarryRet%, TouchesMid90, and Turnovers90 for control and retention.
• PressSucc90, TI90, and Rec90 for defensive actions and transition control.
• Minutes played and team possession % to support normalization and context.
• Match pace indicators (passes per team per 90) to adjust for tempo differences, if available.

Ranges: Most midfield rates fall within 0–8 per 90 for key progressive actions and 70–95% for pass completion. If any metric is missing, the calculator substitutes a team-positional average with a confidence penalty. For extreme outliers or very short appearances, it narrows weight on volatile metrics to reduce noise.

How to Use the Aitana Bonmati vs Alexia Putellas Midfield Influence Calculator (Steps)

Here’s a concise overview before we dive into the key points:

1. Choose the sample window: single match, a five-match stretch, or a full season.
2. Enter per-90 values for creative, control, and defensive metrics for each player.
3. Select the normalization method: z-score (recommended) or percentile ranks.
4. Review or adjust weights for Creative, Control, and Defensive components.
5. Click Calculate to generate each player’s Influence Index and the score gap.
6. Check the head-to-head probability and read the notes on confidence.

These points provide quick orientation—use them alongside the full explanations in this page.

Example Scenarios

Derby under high press: The opponent blocks central lanes, and possession stays near 52%. Aitana posts xA90 0.18, KP90 2.2, ProgP90 9.1, F3E90 7.0, Pass% 90, CarryRet% 84, TouchesMid90 54, Turnovers90 1.2, PressSucc90 6.8, TI90 3.1, Rec90 7.4. Alexia records xA90 0.25, KP90 3.1, ProgP90 6.7, F3E90 6.0, Pass% 88, CarryRet% 80, TouchesMid90 46, Turnovers90 1.5, PressSucc90 5.2, TI90 2.4, Rec90 6.1. After normalization, Aitana leads CRS and DIS, while Alexia leads CIS slightly; blended II comes to 78.4 vs 75.3 with P(Aitana higher) ≈ 59%. What this means: Aitana’s control and defensive activity edge a creative advantage from Alexia in a high-press game.

Low block opposition with heavy possession: The team holds 66% possession against a compact defense. Aitana records xA90 0.35, KP90 4.2, ProgP90 8.4, F3E90 8.1, Pass% 91, CarryRet% 86, TouchesMid90 62, Turnovers90 1.0, PressSucc90 5.1, TI90 2.2, Rec90 5.3. Alexia posts xA90 0.40, KP90 4.8, ProgP90 7.2, F3E90 9.0, Pass% 92, CarryRet% 88, TouchesMid90 58, Turnovers90 0.9, PressSucc90 4.0, TI90 1.9, Rec90 4.7. Normalization boosts both creative sub-scores; Alexia leads CIS and CRS narrowly; DIS differences are small in a low-transition match. II yields 83.1 for Alexia and 81.6 for Aitana with P(Alexia higher) ≈ 54%. What this means: In a slower, possession-heavy match, Alexia’s final-third passing and retention nudge the head-to-head.

Limits of the Aitana Bonmati vs Alexia Putellas Midfield Influence Approach

No single index captures every tactical nuance. Positional roles, instructions, and match context shape what a midfielder can attempt. Some actions matter more because of timing, not volume, and can evade event data.

• Role bias: An advanced 8 will post different profiles than a deeper playmaker, even with similar influence.
• Teammate effects: Quality runs, spacing, and finishing affect xA and key pass payoffs.
• Opponent style: Deep blocks depress defensive actions, while open games inflate them.
• Sample size risk: Short stints or single matches can swing due to randomness.
• Data coverage: Carry retention and pressure success definitions vary by provider.

Use the calculator as a guide, not a verdict. Pair it with video, tactical notes, and opponent analysis. Adjust weights to reflect the match plan and the role each player filled on the day.

Units & Conversions

Consistent units keep comparisons fair across competitions and data sources. Most inputs should be per 90 minutes, with distances and speeds standardized. Use the table below to convert common football metrics quickly.

Read the table left to right. Identify your current unit, then apply the conversion. Keep all event counts as per-90 rates before normalization to make head-to-heads reliable.

Common Issues & Fixes

Analysts often face mixed data sources, missing fields, or uneven minutes. These gaps can distort comparisons if not handled carefully.

• Issue: One player has fewer minutes. Fix: Always use per-90 rates and show confidence notes.
• Issue: Missing CarryRet%. Fix: Replace with team-position average and reduce control weight by 10%.
• Issue: Extreme outlier match pace. Fix: Use z-scores computed within the same competition or sample.
• Issue: Different event definitions. Fix: Align provider definitions or adjust thresholds to match.

Before finalizing a report, spot-check a few events on video. Confirm that high scores reflect real influence, not just noisy counts or skewed tempo.

FAQ about Aitana Bonmati vs Alexia Putellas Midfield Influence Calculator

Does the calculator work for a single match?

Yes, but expect higher volatility. Consider a rolling three to five match window for steadier reads.

Can I change weights for different tactical plans?

Absolutely. If control matters more than chance creation for a specific game, increase the Control and Retention weight.

What if my data provider lacks progressive carries?

Use progressive passes and final-third entries as proxies. Note the limitation and lower the creative weight slightly.

How do I compare across leagues?

Compute z-scores within each league or normalize against a combined cross-league baseline to avoid pace and style bias.

Glossary for Aitana Bonmati vs Alexia Putellas Midfield Influence

Progressive Passes (ProgP90)

Forward passes that move the ball significantly closer to goal, averaged per 90 minutes.

Key Passes (KP90)

Passes that directly create a shot, averaged per 90 minutes.

Expected Assists (xA90)

The probability a pass becomes an assist based on shot quality, averaged per 90 minutes.

Final-Third Entries (F3E90)

Passes or carries that move the ball into the attacking third, averaged per 90 minutes.

Carry Retention Rate (CarryRet%)

The percentage of carries where the player’s team keeps possession after the action.

Pressures Successful (PressSucc90)

Pressing actions that lead to a turnover or disrupted possession within five seconds, per 90 minutes.

Tackles + Interceptions (TI90)

Defensive actions that stop the opponent’s move or regain the ball, averaged per 90 minutes.

Influence Index (II)

A 0–100 score combining creative, control, and defensive sub-scores to reflect total midfield impact.

References

Here’s a concise overview before we dive into the key points:

• Stats Perform: Understanding Expected Assists (xA)
• The Analyst: What Are Pressures in Football?
• FBref: Advanced football statistics and per-90 data
• StatsBomb: Possession Value Models Explained
• Global Football Data: Possession and tempo trends
• Journal of Sports Sciences: Effects of match congestion on technical performance

These points provide quick orientation—use them alongside the full explanations in this page.