Forced Turnover:
Evaluating Pressing Effectiveness in Soccer

Natalie Rayce

Carnegie Mellon University

David Almona

Centre College

Turning Defense to Attack

Manchester City wins back possession seconds after losing it through pressing.

Main Question

Can the effectiveness of a press in soccer be predicted using factors such as spatial context, pressing dynamics, game context and situational factors?

Key Terms

Pressing: a defensive tactic where players apply coordinated pressure on the opponent with the ball to force mistakes, win back possession, and quickly transition to attack

Forced Turnover: when a player loses possession due to opponent pressure, resulting in the opposing team gaining control. This includes misplaced passes, interceptions, successful tackles, or losing control under pressure - all direct results of effective defensive pressure

Data

  • Dataset: 520 matches in the MLS 2023 season

  • Three data types:

    • Match information: game details (teams, pitch, referee)
    • Event data: player actions (passes, shots, tackles) with timestamps and coordinates
    • Tracking data: real-time positions of all players and ball at 10 Hz

  • Source:

Initial Pressure Zone

  • Within 6 meters of the ball carrier

Problem: Doesn’t account for direction and oversimplifies pressing

The Pressure Zone

Adopted from Andrienko et al. (2017).1

Pressing Criteria

A defending player was classified as “pressing” if they were simultaneously

  • within the oval pressure zone, AND
  • approaching the ball carrier above a velocity threshold of 1 m/s.

Pressing actions were grouped into sequences if at least one defender continued pressing within 1.5 seconds.

Measuring an Effective Press

  • Since the goal of pressing is to regain ball possession from the attacking team, the impact of pressing should extend beyond immediate ball re-possession.

  • Pressing can force the attacking team into tight positions, which may increase the likelihood of an eventual turnover in the next few seconds or actions.

Response Variable: A forced turnover within 5s of pressing initiation.

Features: 31 features were extracted and used for training our model:

  • Spatial Context: Ball carrier position, distance to boundaries, field third, etc.

  • Pressing Dynamics: Number of defenders, approach velocity, passing options, etc.

  • Game Context: Score, game state (winning/losing/drawing), time remaining, etc.

  • Situational Factors: How the ball carrier gained possession (pass reception, interception, etc.), incoming pass characteristics (distance, height, range), etc.

Modeling

252,464 pressing sequences were identified across the 502 MLS matches.

Two Models:

  • Logistic Regression

  • XGBoost

10-fold cross-validation with match-based splits to prevent data leakage.

XGBoost predicts low-risk well

Calibration plot: The XGBoost model (blue line) moves away from perfect calibration with higher turnover probabilities.

Pressing Volume vs. Effectiveness

Teams in the upper-right quadrant combine high pressing frequency with high effectiveness

Pressing After Interceptions Forces Turnovers 74% of the Time

  • The feature start_type contributed approximately 70% of total model importance.

  • This describes how the ball carrier got in possession of the ball, which could be an interception, reception, recovery, etc.

  • Looking at actual turnovers, pressing the ball carrier when they got the ball from an interception led to a turnover approximately 74% of the time.

Limitations and Future Work

Limitations:

  • 23% class imbalance in forced turnovers potentially biases models toward predicting “no turnover”.

  • MLS-only data limits generalizability to leagues with different physical demands and player quality.

  • Tracking data inaccuracies may affect player position and movement precision.

  • No individual player skills such as pace and pressing ability.

  • No pitch control modeling limits understanding of spatial dominance during pressing.

Future Work:

  • Apply class weights to handle class imbalance.

  • Extend analysis to multiple leagues.

  • Add pressing intensity calculation.

  • Add pitch control metrics to account for spatial dominance.

Thank you

Appendix

A.1: Feature Descriptions

Some features used in the model
Feature Description Type
ball_carrier_x x-coordinate of ball carrier at press start Numeric
ball_carrier_y y-coordinate of ball carrier at press start Numeric
n_pressing_defenders Number of unique defenders who were actively pressing Numeric
max_passing_options Number of available passing options for ball carrier Numeric
avg_approach_velocity Average speed of pressing defenders (m/s) Numeric
poss_third_start Pitch third where press begins Categorical
game_state Current match status (winning/drawing/losing) Categorical
start_type How player gained possession Categorical
incoming_high_pass Pass received above 1.8m height Boolean
incoming_pass_distance_received Distance of received pass (m) Numeric
incoming_pass_range_received Range category of received pass Categorical
organised_defense Defense organized at pass moment Boolean
dist_to_nearest_sideline Distance to nearest sideline (m) Numeric
dist_to_nearest_endline Distance to nearest endline (m) Numeric
dist_to_attacking_endline Distance to attacking endline (m) Numeric
dist_to_defensive_endline Distance to defensive endline (m) Numeric
dist_to_attacking_goal Distance to attacking goal center (m) Numeric
minutes_remaining_half Minutes left in current half Numeric
minutes_remaining_game Minutes left in match Numeric
ball_carrier_direction Ball carrier direction (degrees) Numeric
ball_carrier_speed Ball carrier speed (m/s) Numeric
penalty_area Press starts in penalty area Boolean
n_defenders_within_10m Defenders within 10m radius Numeric
n_defenders_within_15m Defenders within 15m radius Numeric
n_defenders_within_20m Defenders within 20m radius Numeric
n_defenders_within_25m Defenders within 25m radius Numeric

A.3: Oval Pressure Zone Formula

\[ L = D_{back} + (D_{front} - D_{back})(z^3 + 0.3z) / 1.3 \] where:

\(L\) = the maximum distance limit for effective pressure at angle \(\theta\) (the radius of the oval-shaped pressure zone at any given angle)

\(D_{back}\) = the maximum distance limit when the presser is positioned behind the ball carrier

\(D_{front}\) = the maximum distance limit when the presser is positioned in front of the ball carrier

\(z\) = \((1 - cos \theta) / 2\)

\(\theta\) = the angle between the vector from the ball carrier to the center of the attacking goal (which we determined as the threat direction) and the vector from the ball carrier to the presser

Andrienko et al. (2017) determined the distance thresholds \(D_{back}\) and \(D_{front}\) to be 3m and 9m, respectively, based on consultation with football (soccer) experts. He later performed an experiment to verify these parameters.

A.4: Pressing Pattern

Heatmap showing the pressing patterns across the MLS 2023 season. The highest concentration of pressing occurs in the middle third of the field on both sides, where teams look to win the ball back in midfield areas to create quick attacking opportunities. Note: the home team is made to always attack left-to-right, the away team goes right-to-left.

A.5: Team Pressing Performance Rankings

Positive values (blue) indicate teams forcing more turnovers than predicted, while negative values (red) show underperformance. New York Red Bulls led MLS in pressing effectiveness, while Nashville SC struggled most relative to expectations.

A.6 Pressing vs. Being Pressed