Track every rival sequence that lasts more than six seconds and divide the total by your own tackles, interceptions and forced clearances inside their half. A result below 7.5 marks a side that suffocates build-up; above 12.0 signals passive coverage. Liverpool 2019-20 averaged 6.8 on this scale, the lowest in Premier League history, while relegated Watford posted 14.3 the same year.
Apply the filter only to passes attempted within 40 metres of the opposition goal; ignore square balls between centre-backs. Opta logs show teams winning the ball inside this zone create an xG of 0.37 per recovery, compared with 0.11 for deeper wins. Bayern under Nagelsmann lifted their clip from 8.2 in 2020-21 to 5.9 a season later; their goals from turnovers rose from 11 to 23.
Update the count live with a spreadsheet that refreshes every 120 seconds. If the number climbs above your pre-match target for more than five minutes, raise the defensive line by five metres and push the nearest winger three metres inside to cut central lanes. Analysts at Brentford use this trigger; it shaved 0.4 goals per game off their conceded column.
PPDA Metric: How to Measure Pressing Intensity in Football
Divide opponent passes in the attacking third by defensive duels, blocks and interceptions inside the same zone; values below 5.2 flag suffocating traps, 5.2-8.0 show selective pressure, above 8.0 reveal passive fronts. Track the ratio every 15 min to spot fatigue: Liverpool’s 2025-26 average rose from 4.1 (0-15 min) to 7.6 (75-90 min), exposing the window rivals exploited for two late goals.
Build a live dashboard:
- Python script pulls StatsBomb event data each minute
- Filters for opposition passes and defensive actions within 35 m of their goal
- Writes the rolling quotient to a Google Sheet
- Triggers a Slack alert when the 10-minute moving quotient jumps >15 %
Coaches then switch to a 4-4-2 mid-block, cutting the quotient back below 6.0 within five game minutes.
Counting Passes & Defensive Actions for PPDA
Tag every opponent ball rotation inside the attacking third until a duel, tackle, interception, foul or goalkeeper claim; stop the counter at the first backward or square pass that breaks the sequence. Exclude restarts after stoppages; count only continuous possessions. Example: Liverpool vs. City 12-Aug-23, first half, 78 opponent passes, 11 defensive actions → 7.1.
| Action Type | Count Rule | Common Mistake |
|---|---|---|
| Pass | Any controlled ball movement by foot, head, chest | Counting clearances as passes |
| Tackle+ | Only if possession changes | Adding failed tackles |
| Interception | Ball directly taken | Counting blocks |
| Foul | whistle stops sequence | Ignoring advantage played |
Split the field into six vertical lanes; log coordinates for each event. Scripts in R using data from StatsBomb open set: filter team_id != possess_team & event_type %in% c('Pass','Carry') & x > 60; join with defensive events; group by possession_chain; summarise ratio. For live coding, run this every 15 min; if ratio climbs above 8.0, trigger voice cue to drop line 5 m and jump the pivot. Bundesliga 22-23 median: 6.4; elite benchmark: 4.9.
Setting the 40-Meter Cut-Off for PPDA Zones
Clip every sequence at 39.9 m from the goal line; anything closer contaminates block-data with emergency defending and scrambles the counter-attack map.
Across 312 Bundesliga matches, sequences starting inside this 40 m pocket averaged 3.2 opponent passes before a challenge, while those between 40-50 m averaged 6.1; the jump is sharp enough to treat 40 m as a binary switch.
- Import Wyscout x,y event dump
- Filter team_in_possession = 0
- Delete rows where event_y < 6000 or event_y > 84000 to remove touchline noise
- Compute distance = sqrt((100-x)^2 + (50-y)^2)*1.05
- Tag zone = 1 if distance ≤ 40, else zone = 0
Goalkeepers launch 62 % of their passes beyond this line; exclude them or your coefficient balloons by 0.8.
Forty metres equals 8.3 seconds of sprint time for a centre-back retreating at 4.8 m/s; that is the safety margin before a through-ball turns into a breakaway.
- Split-season stability check: split the season in half; if the 40 m coefficient drifts more than 0.15, raise the bar to 42 m
- Opponent adjustment: multiply each sequence by the rival’s seasonal average pass length; shorter-passing sides (Barcelona 2019: 14.7 m) push the effective cut-off to 38 m
- Pitch tilt correction: when the ball spends > 55 % of the previous 30 s in the attacking third, drop the cut-off to 37 m to cancel fake pressing stats during siege phases
Export two CSVs: one with the 40 m flag, one without; run a logistic regression predicting goal probability inside 15 seconds; the 40 m version raises AUC from 0.71 to 0.78.
Python Code to Automate PPDA from Event Data
Feed StatsBomb-style JSON straight into a 30-line parser: load only Pass and Defensive rows, tag the first five seconds after each turnover, count rival passes and defensive actions inside that window, then divide; 1.7 on average flags a side that forces the issue. Drop rows where x, y coordinates sit outside [0, 120, 0, 80] and clip stoppage time > 15 s to keep outliers from bloating the ratio.
For 180-match Premier League set on 8-core laptop the full run finishes in 11 s; pandas vectorisation plus numba.jit on the sliding-window loop squeeze another 40 %. Cache results in Parquet and append new rounds with append mode to avoid re-processing whole seasons. One user landed a front-office interview after tweeting the 2025 Chelsea drop from 1.9 to 2.4 https://chinesewhispers.club/articles/rams-urged-to-release-super-bowl-cb.html; the code repo linked there carries the exact notebook.
Edge case: if opponent restarts with a throw-in, extend the 5-second search to 8 s; Opta’s qualifiers mark Throw_in in field type_name. When tracking data is present, tighten the radius to 20 m around ball carrier; otherwise fall back to full-pitch counts and flag uncertainty column loose=True so analysts can filter. Bundesliga data provider omits defensive duels won as separate event; merge duel with outcome=success to keep denominator consistent across leagues.
Export mini-dashboard: seaborn regplot maps weekly rolling average against goals conceded; color by opponent strength (Elo) to expose pattern that low-block visitors drag the ratio upward without hurting the scoreboard. Automate the graphic with plt.savefig(ppda_roll.png, dpi=130, bbox=tight) and email to staff every Monday 06:00 through celery beat; coaching group cuts clips where the value spikes above 3.0 to rehearse counter-press triggers on Tuesday morning.
Benchmarking PPDA Against League Averages
Divide your club’s 2026-24 seasonal average (7.3 opponent passes before an intervention) by the league’s mean (9.8) to get a 0.74 ratio; anything below 0.80 flags a side that provokes turnovers 25 % sooner than the competition, a threshold crossed by Brighton, Leverkusen and Union SG last year. Store each match-day’s ratio in a rolling five-game window: a drop from 0.78 to 0.55 inside a month signals coach-triggered tactical tweaks, while a drift above 0.90 exposes fatigue or fixture congestion and predicts an xG against rise of 0.17 per game over the next four rounds.
Overlay these micro-trends with salary-mass context: clubs in Europe’s top-seven leagues whose wage bills sit inside the bottom quartile yet keep the ratio beneath 0.75 average 0.11 goals conceded fewer than spendthrift peers, translating to 4-5 extra table points-enough for relegation escape or Europa Conference entry-while outliers above 1.15 ship 0.22 goals more and face a 38 % drop in clean-sheet frequency.
FAQ:
Can I trust PPDA if my team plays a low block for only ten minutes after scoring, or does the number get ruined?
It can be skewed. PPDA averages everything over the whole game, so a short spell of deep defending will raise the value and make the pressing look softer than it was. Clubs now slice the data into 5-minute windows or filter only the minutes when the score is level; that keeps the single tactical shift from hiding the real pressing intent.
Why do some coaches ignore PPDA and look at passes per entry instead?
Passes per entry counts how many passes the opponent can string together each time they cross the halfway line. It ignores territory deep in their own half, so a team that presses only in the final third gets a clearer signal. PPDA still works for full-pitch pressing, but passes per entry is handy when the press is triggered only after the ball enters a certain zone.
My amateur club logs games on video but has no tracking data; can we still get a useful PPDA?
Yes. Mark every opponent pass with a timestamp and tag the four defensive actions. A free tool like LongoMatch lets you export those events to a spreadsheet; PPDA is then a simple formula. Without GPS you lose distance metrics, but the ratio itself is still valid for comparing your own matches week-to-week.
Does a low PPDA always mean more goals from turnovers, or can pressing too high hurt us?
Not always. A low PPDA creates more possessions high up the pitch, but conversion depends on how quickly you move the ball once you win it. Press with a high line and slow recovery runs, and a single long ball behind the defence can flip the odds the other way. The best sides pair low PPDA with fast counter-attacks and narrow spacing so that if the press is beaten, a recovery sprint is short.