Trends
Trends are time-based data points that describe how certain metrics evolve over the course of a match. Instead of giving you a single aggregated value (for example, total shots or total pressure), trends let you follow minute-by-minute (or period-by-period) developments for each team.
This makes trends ideal for:
Visual timelines and graphs (momentum, pressure, xG over time, etc.)
In-depth match analysis and storytelling
Advanced models that care about when something happened, not just how much
A key example of a trend-based feature in the Football API is the Pressure Index, which exposes pressure values as a series of time-stamped data points.
How trends are structured
Trend data is exposed as lists of records attached to a fixture, usually via a feature-specific include (for example, pressure). Each record typically contains:
A reference to the fixture
A reference to the participant (team)
The time within the match (minute, and in API 3 also the period)
The value of the trend at that moment (for example, a pressure score)
Conceptually, a single trend record answers the question:
“At this point in the match, for this team, what was the value of this metric?”
Period and minute separation (API 3)
In API 3, trends are explicitly identifiable using:
Period – which part of the match the value belongs to (for example, 1st half, 2nd half, extra time, etc.)
Minute – the minute within that period
Participant (team) – which team the record belongs to
This prevents ambiguity between, for example:
A data point at 45+2 in the first half
A data point at 47:00 in the second half
In addition, when a value is recorded during injury time, minute and extra minute are added together, so you can still plot or process it on a continuous time axis if needed.
Example: Pressure Index as a trend
For the Pressure Index, trends are returned as an array under the pressure key on a fixture. A simplified example looks like this:
Each record is a single point you can plot on a chart or use in your own models.
Requesting trends
Trends are not retrieved via a generic /trends endpoint. Instead, they are exposed through feature-specific includes on fixtures.
For example, to retrieve Pressure Index trends for a fixture, you add the pressure include to a GET Fixture by ID request:
The response will contain all standard fixture fields, plus a pressure array with the trend points, as shown above.
Always check the documentation of a specific feature (such as Pressure Index) to see which include name exposes its trends and how the payload is structured.
Working with trends in your application
1. Building graphs and timelines
Trends are perfect for visualising how a match develops. Common use cases:
Pressure graphs – plot the Pressure Index per team over time
Momentum charts – show when one team took control of the match
Storytelling widgets – highlight key swings in dominance
A typical workflow:
Request the fixture with the relevant trend include (for example,
pressure).Group records by
participant_id(home vs. away team).Sort each group by time (minute / period).
Plot the values on a line chart.
2. Handling injury time and extra periods
Because API 3 clearly separates periods and minutes and correctly handles injury time, you can:
Avoid overlapping values between halves
Correctly mark data points that occur in added time
Build accurate visual timelines that match the actual match flow
If you want a single continuous timeline (0–90+ minutes), you can:
Use the minute plus added time as your x-axis value
Combine this with the period information to distinguish first half, second half, and extra time where needed
3. Combining trends with other data
Trends become even more powerful when combined with:
Events (goals, cards, substitutions)
Statistics (shots, possession, xG)
Odds or predictions
For example, you can annotate a pressure graph with goal events, or compare pressure swings to your pre-match probabilities.
Possession Trends
Possession trends illustrate the flow and control of the match over time. A team's ability to maintain possession reflects its dominance in controlling the game's rhythm and dictating play.
A steady increase in possession percentages typically indicates that a team is asserting control, dominating midfield, and forcing the opponent to defend deeper.
Fluctuating possession values may suggest a more balanced or transitional match, where both teams share spells of control.
Sudden spikes or drops in possession can highlight momentary shifts in momentum, often due to tactical changes, goals, or red cards.
Passing Accuracy and Volume Trends
Passing trends help to understand the efficiency and approach of teams in ball distribution.
High passing accuracy combined with high volume suggests methodical, possession-based play, focusing on ball retention and patient buildup.
Low passing accuracy with high volume may indicate forced or rushed play, possibly due to pressing or chaotic match phases.
Gradual improvements in passing accuracy over the fixture reflect growing composure and tactical adjustments.
Differences in successful passes and total passes between teams highlight contrasting styles, e.g., possession-focused versus direct play.
Attacking and Dangerous Attacks Trends
Attack trends provide insight into offensive intent and territorial advantage.
Rising numbers of attacks and dangerous attacks over time show a team's dominance in creating threats in the attacking third.
A gap between total attacks and dangerous attacks can reveal ineffectiveness in converting attacks into clear chances.
Sharp increases in dangerous attacks in specific periods may indicate tactical surges or momentum shifts, often after substitutions, set-pieces, or opponent fatigue.
Defensive Trends
Defensive statistics such as tackles, interceptions, duels won, and fouls committed reflect the defensive workload and style.
High tackle counts and fouls can suggest a physically aggressive defensive approach, aimed at disrupting the opponent’s rhythm.
High interceptions and clearances with low fouls reflect a disciplined, organized defensive strategy, prioritizing positioning over physical confrontations.
Variations in these stats over time can reveal tactical changes, such as dropping deeper to defend a lead or pushing higher to regain control.
Duels and Dribbling Trends
Trends in duels won and successful dribbles help assess individual battles and creative attempts to break lines.
High dribble success rates indicate technical superiority in 1v1 situations, often seen in wide areas or against compact defences.
Frequent duels with low success rates may highlight inefficiencies or strong opposition defence.
Changes in dribble attempts and duels won over time reflect adjustments in approach, such as shifting to more direct attacking play or increasing defensive aggressiveness.
Shooting and Conversion Trends
Shooting trends measure attacking effectiveness and the conversion of possession into tangible goal threats.
A high shot count with low shots on target suggests ineffective finishing or poor shot selection.
Low shot volume with high efficiency (shots on target or goals) indicates a clinical, opportunistic approach.
Key metrics such as big chances created, missed chances, and expected goals (if available) provide deeper context on chance quality and conversion efficiency.
Momentum Shifts and Tactical Patterns
Analyzing the sequence and progression of key events helps uncover momentum shifts throughout the match.
Periods with overlapping high possession, increased passes, and dangerous attacks typically mark phases of dominance.
Conversely, spikes in fouls, clearances, and defensive duels might indicate periods of defensive strain or tactical retreat.
Recognizing these patterns enables better understanding of tactical shifts, match phases (e.g., opening control, mid-game surges, late defensive blocks), and psychological moments (after goals or cards).
Conclusion on Fixture Trend Analysis
Statistical trends in a fixture offer a layered, time-based understanding of how the match evolved, beyond just the scoreline. They allow the observer to spot tactical approaches, adaptations, momentum swings, and efficiency levels in attack and defence.
By combining trends in possession, passing, attacking, defending, and shooting, one can build a comprehensive narrative of the fixture’s flow, key moments, and the factors that influenced the result.
API 2 vs API 3: what changed for trends?
The "Trends" behaviour was refined in API 3 to make it easier and safer to use in applications.
In API 2:
Trend values for each team were identifiable only by minute.
There was no clear separation between periods, so:
Values in injury time of the first half could overlap with values at the start of the second half.
This could result in abnormal or misleading lines if you plotted trends directly.
In API 3:
All trend entities for a fixture are listed in a single list.
Each trend record is identifiable using period, minute and participant (team).
When a value is recorded during injury time, minute and extra minute are added together.
This leads to:
Clearer, more consistent data
No overlapping trend points across periods
Smoother, more accurate graphs and analyses
For a high-level summary of this change, see the Trends section on the API Changes page.
When should I use trends?
Use trends whenever you need to answer questions like:
“When did Team A start dominating the match?”
“How did the pressure build up before the goal?”
“Was this match a constant one-way attack or a game of swings?”
If you only need final totals (for example, total shots or average possession), regular statistics may be enough. But if you care about how things changed over time, trends are the right tool.
For a concrete, end-to-end example of a trend-based feature, check out the Pressure Index documentation.
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