Team form analytics

Definition of team form analytics

Team form analytics is the method of assessing a football team’s recent performance and current momentum. It looks at more than just wins and losses by considering key performance indicators over a specific time. The goal is to measure how well a team is playing, spot patterns in their performance, and understand the factors affecting their current state.

Why team form analytics is important

Analysing a team’s form isn’t just for research; it provides important insights that help make better decisions in football. Here are some key reasons why it’s important:

– Predicting future performance: A team’s current form can show how they’re likely to perform in upcoming matches. Teams on a winning streak are more likely to keep winning, while teams struggling are likely to continue struggling, no matter their past achievements or league position.
– Helping with match planning and tactics: Coaches and analysts use form analytics to spot an opponent’s strengths and weaknesses. For example, if an opponent’s defence is weak, it might change the way a team attacks. If a key player is in good form, specific defensive plans might be needed.
– Choosing players and managing rotations: Understanding team form helps managers pick the best-performing players and decide who might need a rest or a new role. A player who is contributing to a team’s good form may continue to be selected.
– Assisting with betting and fantasy sports: For betting companies and fantasy sports fans, team form analytics helps set odds, place bets, and choose players who are more likely to score points based on their current form. For instance, Sportmonks’ API provides xG and fixture data, enabling betting platforms to set odds based on recent team form.
– Reducing fatigue and injury risk: A drop in performance could mean the team is tired or has minor injuries. Tracking performance and physical data can alert the staff to potential issues before they become problems.
– Improving media commentary and fan engagement: Journalists, pundits, and fans use team form to explain results, highlight surprises, and create stories about teams and players. Analytics gives objective data to support these discussions.
– Spotting trends and patterns: Form analytics helps identify wider patterns, such as a team improving after a change in manager, peaking at certain times of the season, or consistently struggling in certain areas.

Key metrics and indicators of team form

Assessing team form goes beyond just looking at wins and losses. It includes various metrics that give a clearer picture of a team’s current performance. These indicators can be grouped into the following categories:

Results-based metrics (Traditional Indicators)

– Points per game (PPG) in recent matches: This shows how many points a team has earned over a recent period, like the last 5, 8, or 10 matches.
– Win/loss/draw record: The simple tally of how many games a team has won, lost, or drawn recently.
– Goal difference in recent matches: The total number of goals scored minus goals conceded in recent games, showing both attacking and defensive performance.
– Clean sheets: The number of games a team has played without conceding a goal, reflecting defensive strength.

Performance-based metrics (Underlying indicators)

– Possession percentage: The average amount of time a team controls the ball in recent games, showing their style of play.
– Shots total / shots on target ratio: The number of attempts a team makes and how many are accurate. A high number of shots with low accuracy could suggest they are not creating quality chances.
– Passing accuracy: The percentage of successful passes, showing how well the team keeps possession and builds attacks.
– Defensive actions (tackles, interceptions, clearances): These numbers show how well a team defends and regains possession.
– Disciplinary record (fouls committed, cards received): A rising number of fouls or yellow/red cards can signal frustration or changes in defensive tactics.

Advanced statistical indicators (Contextual & predictive)

– Expected goals (xG) / Expected goals conceded (xGC): These models estimate the likelihood of a goal based on factors like shot location and body part used. Looking at xG difference (xG – xGC) can show a team’s true attacking and defensive performance and is often a better predictor of future results than actual goal difference.
– Expected assists (xA): This measures the chance that a pass will become a goal assist, regardless of whether the receiver scores.
– Pressures per defensive action (PPDA): This metric measures pressing intensity, showing how many passes an opponent can make before the team takes defensive action. A low PPDA suggests a strong pressing game.
– Momentum metrics: Some models look at sequences of events, shot clusters, or periods of dominance to measure a team’s momentum.

Methods and approaches in analysing team form

To analyse team form effectively, it’s important to use specific methods that interpret data over time and in context, focusing on identifying trends, patterns, and hidden influences.

Rolling averages and moving windows

Instead of looking at a single match’s stats, analysts calculate averages of chosen metrics (like xG difference or shots on target) over a “rolling” window of recent matches (e.g., the last 5, 8, or 10 games). As each new match is played, the oldest game drops off, and the new one is added. This smooths out any unusual results and reveals the team’s consistent performance level, giving a clearer view of their form. For example, Sportmonks’ API allows retrieval of recent match data to calculate rolling averages of metrics like xG difference.

Weighting recent results

Some models give more importance to the most recent matches, recognising that recent performances are a better reflection of a team’s current form. This method allows the analysis to respond more quickly to changes in form, whether positive or negative, so that a win in the latest match will weigh more than a win from several games ago.

Contextual analysis (opposition strength & home/away)

Raw statistics can be misleading if the strength of the opponent or the location of the game isn’t considered. For example, a close loss to a top team could show better form than a comfortable win over a weaker team. By adjusting metrics for opposition strength and distinguishing between home and away games, analysts can provide a more accurate assessment of a team’s true performance.

Visual trend analysis (charting)

Plotting key metrics over time, such as a team’s PPDA over the last 15 matches or a player’s high-speed running distance, helps to visually identify trends, periods of consistency, or sudden drops in performance. This makes complex data easier to understand and allows analysts to spot patterns that might not be obvious from raw numbers alone.

Performance vs. results discrepancy

This approach looks at the difference between a team’s performance metrics, such as xG difference, and their actual results, like goal difference. A team might be performing well based on xG but be unlucky with results, or vice versa. This method helps to identify teams that are likely to experience a shift in results, providing insights into future performance beyond just current league standings.

 Applications of team form analytics

The insights gained from team form analytics are practical and applied in many important areas of football, influencing decisions at different levels.

– Match prediction and betting: Sports betting operators and professional bettors use team form analytics to predict match outcomes. By combining recent performance trends, like xG difference and scoring efficiency, with historical head-to-head results and other factors, they can set more accurate odds and spot potential value bets. This helps create more informed betting strategies and improves risk management for betting platforms. Sportmonks’ Predictions API uses team form data to forecast match outcomes, aiding betting platforms.
– Tactical planning and opponent scouting: Coaches and performance analysts rely on form analytics to prepare for matches. They study an opponent’s recent tactical patterns, attacking strategies, defensive weaknesses, and key player form. This allows them to develop specific game plans, choose the right formations, and prepare players to counter the opponent’s strengths.
– Player selection and rotation: Managers use form analytics to guide their team selection. If the team’s form is slipping, changes in personnel or formation may be needed. Players who are performing well, contributing to positive team metrics, are more likely to be picked. It also helps with player rotation to avoid fatigue-related dips in form, ensuring the best possible starting XI and substitutes for each match.
– Risk management (injury, fatigue): Sports science and medical teams track team and individual player form, focusing on physical indicators like high-speed running, acceleration, and recovery rates. A drop in these areas could signal fatigue or increased injury risk, prompting adjustments in training or rest periods. This helps manage player welfare, reduce injury risk, and maintain peak physical condition.
– Fan engagement and media commentary: Media outlets, pundits, and content creators use form analytics to enhance match previews, post-match analysis, and ongoing discussions. They can explain why a team is performing above or below expectations, highlight emerging trends, and provide data-backed narratives for fans.
– Transfer market decisions: Recruitment teams use form analytics to assess a player’s consistency and current performance level. A player showing strong form over time becomes a more attractive target, while a sudden drop in form may raise concerns. This supports more informed, data-driven recruitment strategies, reducing the risk of signing players who are only temporarily overperforming and ensuring valuable players aren’t overlooked despite temporary dips.

Challenges and considerations in team form analysis

While team form analytics provides valuable insights, it comes with challenges and important factors that analysts must consider:

– Defining the “window” of form: There is no universal agreement on the ideal period to assess form. Should it be the last 3, 5, 8, or 10 games? A short window might be too sensitive to random events, while a longer window might overlook recent changes like a new manager or key injuries. The best window often depends on the league, match frequency, and the type of analysis needed, such as short-term momentum or longer-term trends.
– Contextual factors and nuances: Raw statistics can be misleading. A team’s form might look poor, but they may have faced top opponents eg (Crystal Palace facing Man City, Liverpool, Newcastle and Bournemouth successively), suffered key injuries, or been affected by bad refereeing decisions. On the other hand, a winning streak might be against weaker teams. Analysts must consider factors like opponent strength, home or away games, player availability, and external elements like fixture congestion or travel.
– Causality vs. correlation: Noticing that a team’s xG difference has improved is descriptive, but understanding why it has improved, such as better pressing, a new player, or an opponent’s tactical mistake is the true insight. Establishing direct causality is difficult, so form analysis should encourage further qualitative analysis and video review to understand the reasons behind observed trends.
– Luck and variance: Football often involves luck. A team might perform well but lose due to bad luck, such as deflections or missed chances. On the other hand, a team may get lucky with results despite poor performance. Analysts must distinguish between real performance trends and short-term luck, using metrics like xG to account for some of this, but acknowledging that luck always plays a role.
– Lagging vs. leading indicators: Most performance metrics are lagging, meaning they describe past events. Predicting future form requires identifying leading indicators, such as changes in training, player morale, or tactical adjustments, which are often harder to measure. Combining form analysis with qualitative input from coaches, scouts, and journalists can help bridge this gap.
– Data availability and granularity: Access to detailed data, like tracking or physiological metrics, is often expensive and limited, preventing in-depth analysis for many people. Public data may not have the depth needed for advanced insights.
– Subjectivity in interpretation: Even with objective data, interpreting what counts as “good form” or the significance of a trend often involves personal judgment and experience. Presenting insights clearly and allowing for discussion among coaches can help ensure decisions are based on sound reasoning.

Sportmonk’s approach to team form analytics

Sportmonks’ football API offers a comprehensive suite of tools designed to empower analysts, coaches, and developers in evaluating and leveraging team form analytics. By providing access to over 2,500 leagues worldwide, the API facilitates in-depth analysis of team performance, enabling data-driven decisions across various applications.

Key features for team form analysis

1. Team statistics endpoint

The /teams/{team_id} endpoint allows users to retrieve detailed statistics for a specific team. By including the statistics parameter, analysts can access a wide range of performance metrics, including:

– Total wins, losses, and draws
– Goals scored and conceded
– Goal difference
– Clean sheets
– Disciplinary records

This data is essential for assessing a team’s recent form and identifying trends over a specified period. 

https://api.sportmonks.com/v3/football/teams/{team_id}?api_token=YOUR_TOKEN&include=statistics

2. Expected goals (xG) metrics

Sportmonks provides advanced metrics such as expected goals (xG) and expected assists (xA) through the /expected endpoints. These metrics estimate the quality of scoring opportunities and assist potential, offering a deeper understanding of a team’s attacking efficiency and creativity. For team-level xG data, the endpoint is

https://api.sportmonks.com/v3/football/expected/team/{team_id}?api_token=YOUR_TOKEN

These insights are invaluable for predicting future performance and identifying underlying patterns in team form.

3. Fixture and schedule data

The /schedules/teams/{team_id} endpoint gives detailed information about a team’s past and upcoming matches. This includes match dates, opponents, and venues, which are important for understanding performance and how the schedule affects a team’s form.

4. Advanced filtering and data customisation

Sportmonks’ API offers advanced filtering options, letting users customise data retrieval to suit their needs. For example, analysts can filter stats by season, competition, or specific performance metrics, making it easier to focus on team form over the selected period.

5. Integration with sports media and fantasy platforms

Sportmonks’ football API is built to work smoothly with sports media outlets and fantasy football platforms. By using real-time data, like player stats, match events, and team performance, developers can create engaging experiences for users. This integration helps fans get more involved and gives valuable insights for fantasy sports players.

Unlock team performance insights with Sportmonks

Analysing team form is key to making smarter football decisions, from tactical planning to betting predictions. With Sportmonks football API, you can easily access comprehensive team statistics, advanced metrics like expected goals (xG), and detailed fixture information from over 2,500 leagues worldwide.

Whether you’re developing match prediction tools, enhancing your tactical analysis, or creating engaging fan experiences, Sportmonks provides reliable and accurate data tailored to your analytical needs.

Start your free trial today and turn team performance data into strategic insights.