FSFootyScout
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FootyScout · Football scouting and analytics

Based on available StatsBomb 2023/24 Bundesliga event data.

Player profile

Jamal Musiala

Bayern Munich · Left Attacking Midfield · MID

Matches observed2
Pass attempts52
Limited sample

Actual completion

88.5%

46 completed passes

Pass difficulty

83.8%

How difficult a pass was to complete.

Actual vs. expected passing

+4.7 pp

+2.4 completions vs model expectation

Actual pass completion compared with the model's expected completion.

Player intelligence

Position profile

MID context · Each percentile is an empirical rank among eligible players in the same broad position group. Percentiles are not ratings.

Insufficient eligible position metrics for a meaningful radar.

Style

Tendencies describe what a player attempts; higher is not automatically better.

Pass difficulty

83.8%

Unavailable among MID players

n = 13 eligible peers · sample 52

requires 100 passes

Under-pressure pass rate

25.0%

Unavailable among MID players

n = 13 eligible peers · sample 52

requires 100 passes

Progressive pass rate

15.4%

Unavailable among MID players

n = 13 eligible peers · sample 52

requires 100 passes

Long-pass rate

5.8%

Unavailable among MID players

n = 13 eligible peers · sample 52

requires 100 passes

Average forward distance

1.2 units

Unavailable among MID players

n = 13 eligible peers · sample 52

requires 100 passes

Positive forward distance / 100 passes

484.0 units

Unavailable among MID players

n = 13 eligible peers · sample 52

requires 100 passes

Final-third entries / 100 passes

1.923

Unavailable among MID players

n = 13 eligible peers · sample 52

requires 100 passes

Carry share of actions

53.3%

98th percentile among MID players

n = 61 eligible peers · sample 107

Progressive carry rate

3.5%

54th percentile among MID players

n = 66 eligible peers · sample 57

Progressive action rate

8.4%

53rd percentile among MID players

n = 61 eligible peers · sample 107

Under-pressure action rate

41.1%

95th percentile among MID players

n = 61 eligible peers · sample 107

Shots / observed match

0.50 / match

Unavailable among MID players

n = 5 eligible peers · sample 1

requires 20 shots

xG / shot

4.0%

Unavailable among MID players

n = 5 eligible peers · sample 1

requires 20 shots

Performance

Observed execution or model-derived output relative to opportunities.

Actual vs. expected passing

+4.7 pp

Unavailable among MID players

n = 13 eligible peers · sample 52

requires 100 passes

Under-pressure completion above expected

+8.0 pp

Unavailable among MID players

n = 10 eligible peers · sample 13

requires 20 pressure passes

Progressive completion above expected

+2.1 pp

Unavailable among MID players

n = 9 eligible peers · sample 8

requires 20 progressive passes

Long-pass completion above expected

-1.8 pp

Unavailable among MID players

n = 9 eligible peers · sample 3

requires 20 long passes

Goals above expected

-0.040

Unavailable among MID players

n = 5 eligible peers · sample 1

requires 20 shots

Overall impact / 100 actions

0.079

85th percentile among MID players

n = 61 eligible peers · sample 107

Weak split-half stability in the current 34-match product cohort.

Passing impact / 100 passes

0.007

81st percentile among MID players

n = 58 eligible peers · sample 50

Weak split-half stability in the current 34-match product cohort.

Carrying impact / 100 carries

0.143

68th percentile among MID players

n = 66 eligible peers · sample 57

Moderate split-half stability; descriptive rather than latent ability.

Progressive-action impact / 100 actions

-0.008

28th percentile among MID players

n = 61 eligible peers · sample 107

Weak split-half stability in the current 34-match product cohort.

Under-pressure impact / 100 actions

0.092

88th percentile among MID players

n = 61 eligible peers · sample 107

Weak split-half stability in the current 34-match product cohort.

Playing style

Safe Circulator

Position-relative style among eligible MID players.

Style separation0.287

↓Progressive-pass rate (lower)

↑Pass difficulty (higher)

↓Positive forward distance / 100 passes (lower)

↓Long-pass rate (lower)

Pass difficulty-0.51 z
Under-pressure pass rate+1.45 z
Progressive-pass rate+0.44 z
Long-pass rate-1.24 z
Positive forward distance / 100 passes-0.86 z
Carry share of actions+2.17 z

Bars are z-scores relative to eligible positional peers, not percentiles or ratings. Distance-based style separation, not a probability. Smaller values mean the player lies nearer the boundary between the two archetypes.

Leverkusen Role Fit

MID role

How closely a player's playing style matches this role. Compared with Bayer Leverkusen's observed MID role.

Role distance · lower is closer1.223
CloserFarther

Strongest alignment

  • Progressive Pass Rate
  • Positive Forward Distance Per 100 Passes
  • Long Pass Rate

Largest difference

Carry Share Of Actions

limited sampleObserved across 2 matchesRole: 34 matches · 10 contributors

Role Fit measures how closely a player's observed playing style resembles Bayer Leverkusen's observed positional-role style. It does not predict transfer success or future performance.

Expected Goals

Non-penalty shooting

Goals above expected is descriptive for this observed sample, not a definitive finishing-skill rating.

Limited sample

Shots

1

1 matches observed

Goals

0

Total xG

0.0

Goals above expected

-0.0

xG / shot

0.0

0.0% goals / shot

Limited sample: fewer than 20 non-penalty shots.

Campos visualization

Shot map

Non-penalty xG is encoded by marker size and color. Preserved penalties have no modeled xG.

Shots1
Goals0
xG0.04
xG0.00 – 1.00
Green outline = GoalNormal outline = Non-goal

Attacking impact

Impact of passes and carries

Measures how much each pass or carry changed the expected attacking value of a possession. Positive values improved the attack; negative values reduced it.

Reliable sample

Overall impact / 100 actions

+0.079

Passing impact / 100 passes

+0.007

Carrying impact / 100 carries

+0.143

Progressive-action impact / 100 actions

-0.008

Under-pressure impact / 100 actions

+0.092

107 actions · 50 passes · 57 carries · 56.1% increased attacking value

Campos visualization

Carry value map

Exact StatsBomb carry start/end locations. Green increases modeled possession value; coral decreases it.

Situational execution

Under pressure

13 attempts

Limited sample
Actual
92.3%
Pass difficulty
84.3%
Actual vs. expected passing
+8.0 pp

Limited sample — interpret this split cautiously.

Progressive passing

8 attempts

Limited sample
Actual
50.0%
Pass difficulty
47.9%
Actual vs. expected passing
+2.1 pp

Limited sample — interpret this split cautiously.

Long passing

3 attempts

Limited sample
Actual
33.3%
Pass difficulty
35.1%

Progression profile

Progressive-pass rate

15.4%

Pressure-pass rate

25.0%

Average forward distance

1.2 m

Positive forward distance / 100

484.0 m

Final-third entries / 100

1.9

Campos visualization

Pass map

Attacking left to right. Switch between xPass execution difficulty and possession attacking impact.

Showing 1–52 of 52 passes

Pass subset

CompletedIncompleteStronger line = greater pass difficultyThicker line = progressive or under pressure
Pass difficulty
99.7%
Outcome
Completed
Attacking impact
0.0021
Value before
0.0091
Value after
0.0112
Length
33.0 m
Context
Under pressure
Event clocks are not retained in the pass API.

Similar playing styles

Style similarity compares position-relative playing style across passing and carrying tendencies. Scores are not ability ratings.

100 means identical observed profiles; 50 is about the median same-position playing style match. Not a probability.

Limited sample: this player's style profile is based on 2 observed matches, so nearest-neighbor rankings may be less stable.

#1 · Same position group

Adam Hložek

Bayer Leverkusen · Right Attacking Midfield

Style similarity

70.6 / 100

Expected Completion RateCarry Share Of ActionsPositive Forward Distance Per 100 Passes

Sample support: Limited · 2 limiting matches

Observed: 2 vs 19 matches

#2 · Same position group

Florian Wirtz

Bayer Leverkusen · Left Attacking Midfield

Style similarity

67.7 / 100

Actual vs. expected passing
-1.8 pp

Limited sample — interpret this split cautiously.

Expected Completion RatePositive Forward Distance Per 100 PassesLong Pass Rate

Sample support: Limited · 2 limiting matches

Observed: 2 vs 32 matches

#3 · Same position group

Amine Adli

Bayer Leverkusen · Left Attacking Midfield

Style similarity

61.4 / 100

Long Pass RateProgressive Pass RateExpected Completion Rate

Sample support: Limited · 2 limiting matches

Observed: 2 vs 23 matches

#4 · Same position group

Nadiem Amiri

Bayer Leverkusen · Left Defensive Midfield

Style similarity

59.3 / 100

Progressive Pass RateExpected Completion RatePressure Pass Rate

Sample support: Limited · 2 limiting matches

Observed: 2 vs 8 matches

#5 · Same position group

Julian Brandt

Borussia Dortmund · Center Attacking Midfield

Style similarity

55.4 / 100

Expected Completion RatePositive Forward Distance Per 100 PassesLong Pass Rate

Sample support: Limited · 2 limiting matches

Observed: 2 vs 2 matches

#6 · Same position group

Patrick Osterhage

Bochum · Center Defensive Midfield

Style similarity

55.2 / 100

Long Pass RatePositive Forward Distance Per 100 PassesProgressive Pass Rate

Sample support: Limited · 2 limiting matches

Observed: 2 vs 2 matches

Sample support uses the lower observed-match count in each pair. Lower coverage means a measured profile may vary more with additional matches; it never changes the style similarity or ranking.