Win probability

The importance of win probability in football

In football, win probability is the statistical estimation of a team’s likelihood to win a match at any given point, shown as a percentage ranging from 0% (no chance of winning) to 100% (guaranteed victory). This metric is dynamic, adjusting in real-time as the game progresses and various factors come into play.

The significance of win probability goes across various stakeholders in the football ecosystem:

– Fans: It improves the viewing experience by giving insights into how specific events, like a goal or red card, impact the game.
– Analysts: It offers a quantitative tool to assess team performance and game dynamics, facilitating deeper tactical analyses.
– Coaches: Real-time win probability can inform strategic decisions, such as substitutions or formation changes, to optimise the chances of winning.
– Bettors: Understanding win probability aids in making informed betting choices, identifying value bets where the odds may not accurately reflect a team’s true chances.

Historical context and evolution

The idea of win probability first became popular in sports like baseball and American football. These sports involve clear, separate events and frequent scoring, which made it easier to create models that could predict which team was likely to win. For example, in baseball, the “Pythagorean expectation” was used to estimate a team’s winning chances based on the number of runs they scored and allowed. In American football, models used game details like the number of downs, distance to the next down, and field position to figure out win probabilities.

Football (also called soccer), however, proved more difficult. It’s a low-scoring game with many draws and a continuous flow, which makes it hard to apply the same methods used in other sports. Early models weren’t very accurate, showing that football needed its own approach.

A big step forward came in 2002 when Henry Stott created the Glover Automated Results Indicator (GARI) during the World Cup. This model used data from qualifying matches and ran thousands of simulations to predict match outcomes. GARI even correctly predicted Senegal’s surprise win against France, showing that it was possible to model football’s unpredictability.

Later, the introduction of the Expected Goals (xG) model was a game-changer. xG measures the chance of a shot resulting in a goal, based on things like where the shot was taken and how it was made. It helped teams and analysts better understand performance beyond just goals scored.

Further improvements were made when researchers like Pieter Robberechts developed win probability models that worked in real time. These models use current match situations like the score, time left, and who has possession to predict the chances of each possible result (win, draw, or loss). This new approach gave more accurate and meaningful insights.

The development of win probability in football is part of a bigger shift toward data-driven thinking in the sport. As data tools and technology improve, win probability models are becoming more powerful and useful, helping teams, analysts, and fans understand the game better.

Pre-match win probability models

Before the kickoff of a football match, analysts and enthusiasts often seek to estimate the likelihood of each possible outcome, home win, draw, or away win. Several statistical and computational models have been developed to predict these probabilities, each with its unique methodologies and applications.

Statistical approaches

1. Poisson distribution models

The Poisson distribution is a foundational statistical tool used to model the number of goals a team is expected to score in a match. By assuming that goals occur independently and at a constant average rate, the model calculates the probability of various scorelines. This approach requires estimating each team’s offensive and defensive strengths, often derived from historical match data. While the Poisson model provides a solid baseline, it may not account for the correlation between teams’ performances or the increased likelihood of draws in football matches.

2. Elo rating systems

Originally developed for ranking chess players, the Elo rating system has been adapted to assess football teams’ relative strengths. Each team is assigned a rating that adjusts based on match outcomes, considering factors like the opponent’s strength and match importance. The difference in Elo ratings between two teams can be transformed into win probabilities using a logistic function. This method captures the dynamic nature of team performance over time.

3. Bayesian models

Bayesian approaches incorporate prior knowledge and update predictions as new information becomes available. In the context of football, Bayesian models can combine historical data with current season statistics to estimate match outcomes. These models are particularly useful when dealing with limited data, as they allow for the integration of expert opinions or other relevant information into the predictive framework.

Machine learning techniques

Advancements in computational power and data availability have led to the application of machine learning (ML) algorithms in predicting football match outcomes. Techniques such as logistic regression, random forests, and neural networks can process vast datasets, including player statistics, team formations, and historical results. ML models can identify complex patterns and interactions between variables, potentially leading to more accurate predictions. However, they require large amounts of data and careful tuning to avoid overfitting.

Role of bookmakers

Bookmakers play a significant role in shaping public perceptions of match probabilities through the odds they offer. These odds reflect the bookmakers’ assessments of the likely outcomes, adjusted to ensure a profit margin. By converting decimal odds into implied probabilities using the formula:

Implied probability (%) = (1 / decimal odds) × 100

bettors can gauge the bookmaker’s view of an event’s likelihood. Comparing these implied probabilities with one’s own assessments can help identify potential value bets.

In-game win probability models

In-game win probability models offer live insights into a team’s chances of winning, drawing, or losing as the match progresses. Unlike pre-match predictions that set expectations based on team strength and form, these models update continuously during the game, responding to events on the pitch in real time. This makes them highly useful for tracking momentum shifts, guiding tactical choices, and enhancing the fan experience.

Real-time modeling

These models use live data to recalculate probabilities throughout the match. Several factors influence these real-time estimates. The current score is the most influential; a team in the lead will naturally have a higher chance of winning. The amount of time left in the match also matters, a late goal can dramatically swing the prediction. Red cards are another key factor, as playing with fewer players significantly affects a team’s performance and win probability. Other match events like goals, injuries, and substitutions are also considered to keep the model’s output accurate.

Notable models and research

A major development in this area is the Bayesian in-game win probability model created by Pieter Robberechts, Jan Van Haaren, and Jesse Davis. Their model is tailored to football’s unique features, such as its low-scoring nature and the common occurrence of draws, which make modelling more complex than in other sports.

This model incorporates a combination of pre-game and in-game variables. Pre-match data like team ratings are used alongside real-time game conditions such as scoreline and time remaining. Additional context, like the number of attacking passes or chances created recently, is also included. The model was trained on data from top European leagues and showed better accuracy than models borrowed from other sports, thanks to its football-specific design.

Visualisation and interpretation

To make these models more accessible and insightful, they are often paired with visual tools. One common example is a win probability graph, which charts each team’s chances over the course of a match. These visuals clearly show key turning points, for example, a team’s probability might spike sharply after scoring or drop after a sending-off.

These graphs do more than just inform, they tell the story of the game. For fans, they offer a clearer picture of how a match unfolded. For analysts and coaches, they highlight the moments that made the biggest impact, helping in post-match reviews or live tactical adjustments. By visualising match momentum and key events, in-game win probability models provide a deeper layer of insight into football performance and strategy.

Applications of win probability models

Win probability models are now widely used across different areas of football, from helping coaches make better decisions during matches to enhancing live broadcasts and supporting smarter betting. These models bring a layer of analytical insight that improves understanding and strategy for professionals and fans alike.

Coaching and tactical decisions

Coaches rely on win probability models to guide in-game tactical decisions. These models help quantify how certain choices, such as making a substitution, switching formations, or increasing pressing intensity can affect the chance of winning. For example, research into NCAA Division I American football showed how methods like logistic regression and decision trees could assess the effectiveness of coaching moves during overtime, identifying which plays had the most impact on the outcome.

In football, similar models are used to simulate possible scenarios in real-time. This allows coaching staff to see how different strategies might play out before putting them into action. It’s a powerful tool for making informed decisions under pressure and improving match-day tactics.

Broadcasting and fan engagement

Broadcasters have started using win probability models during live matches to make coverage more engaging and insightful. By showing real-time updates on a team’s chances of winning, broadcasters help viewers better understand the importance of specific events. Amazon Prime Video, for example, used Opta’s Live Win Probability model, which simulates the rest of a match 100,000 times to update the likelihood of different outcomes after every key moment.

These real-time visuals add context to the game, making broadcasts more interactive and appealing. They also cater to a wide audience, from casual fans to those who enjoy diving deeper into the numbers, by clearly showing the impact of goals, red cards, or substitutions on the match.

Sports betting

In sports betting, win probability models are key tools for spotting value and managing risk. Bettors use these models to compare their own estimated chances of an outcome with the odds offered by bookmakers. If the model gives a team a 55% chance of winning but the bookmaker’s odds reflect just 40%, the difference points to a possible value bet.

These models also help with more advanced betting strategies, like determining how much to stake on each bet to maximise long-term gains while keeping risk under control. By relying on data rather than gut feeling, bettors can take a more structured and profitable approach to wagering.

Challenges and limitations

Despite the growing use of win probability models in football, several challenges and limitations still affect their accuracy and reliability. These issues span data quality, unpredictable match events, and the technical complexities of model development and evaluation.

Data quality and availability

One of the biggest hurdles is the uneven availability and quality of data. Top professional leagues typically have access to detailed tracking and event data, which are essential for building accurate models. However, this level of data is not always available in lower-tier leagues or amateur competitions. As a result, it becomes difficult to apply or generalise win probability models across all levels of football, limiting their usefulness in broader contexts.

Unpredictable events and external factors

Football is a dynamic sport influenced by countless unpredictable elements. Injuries, red cards, weather changes, and controversial refereeing decisions can dramatically shift a match’s direction. These events are difficult to predict and even harder to represent in a model. Because such occurrences are not easily quantifiable, they introduce uncertainty into win probability estimates, potentially reducing the accuracy of even the most advanced models.

Model overfitting and generalisation

Another technical challenge is avoiding model overfitting, where the model learns specific details from the training data that don’t apply to new situations. In a sport like football, where matches are low-scoring and highly variable, this is a real concern. When a model is overfitted, it performs poorly when applied to different datasets or real-world scenarios. To counter this, developers use techniques like cross-validation, regularisation, and early stopping to ensure the model focuses on general patterns rather than anomalies.

Evaluation metrics and calibration

To determine how good a win probability model is, analysts rely on evaluation metrics like the Brier score and log loss, which assess the accuracy of predicted probabilities. However, these metrics don’t always capture the full complexity of a football match, especially when rare but significant events occur. Moreover, well-calibrated probabilities, where predicted outcomes match observed frequencies are essential for trust in the model. Without good calibration, a model may appear statistically sound but offer misleading guidance.

Temporal dynamics and contextual factors

Football is not static, team strategies shift throughout a match, player fatigue sets in, and tactical decisions are made in response to evolving situations. Capturing these time-based and contextual changes in a model is extremely difficult. Factors like team morale, momentum, or recent performance trends often remain outside the scope of most win probability models. Developing models that can adjust in real-time and incorporate these shifting dynamics is one of the ongoing challenges in football analytics.

Future directions

As football analytics grows more advanced, win probability models are expected to become more precise and useful. These models will likely benefit from richer data sources, smarter algorithms, and applications tailored for coaches, players, and fans alike.

Integration of player tracking data

Adding player tracking data allows models to account for detailed player movement, spacing, and interactions during a match. This level of detail helps capture how off-the-ball runs create space or how defensive shape limits scoring chances. By incorporating such spatial awareness, win probability models can better reflect the true influence of in-game behaviours that aren’t captured by traditional statistics.

Advanced machine learning techniques

Recent innovations in machine learning such as using deep learning methods like Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks are improving the accuracy of win probability predictions. These methods can learn both where and when events occur during a match, revealing complex patterns. Some models also use attention mechanisms to identify key actions or moments that have the biggest effect on win likelihood, allowing for clearer interpretation of the results.

Real-time decision support systems

Win probability models are also moving into the realm of live match analysis. Real-time decision support tools are being developed to give coaching staff actionable insights during a game. By continuously updating probabilities based on match events, these tools can guide substitutions, tactical shifts, or defensive changes. This live feedback can help managers make quicker and better-informed decisions as the match unfolds.

Personalised player impact metrics

Future win probability models may also focus more on individual contributions. This involves calculating how specific actions like a crucial interception or through-ball, change the team’s chances of winning. Tools such as VAEP (Valuing Actions by Estimating Probabilities) already measure the impact of passes and shots. Future developments could expand this to include more actions, helping analysts and scouts better understand each player’s influence on match outcomes.

Enhanced fan engagement tools

Finally, as these models improve, they offer exciting opportunities for fans. Broadcasters might include real-time win probability charts in match coverage, showing how goals, red cards, or missed chances affect the odds. Interactive dashboards and visualisations could give fans a deeper understanding of the match story, making watching football more immersive and data-informed. These tools not only entertain but also educate viewers about the game’s hidden dynamics.

Sportmonks and its role in win probability modeling

In today’s football analytics, Sportmonks plays a key role by providing extensive data that improves win probability models. Our football predictions API gives advanced insights, powered by machine learning, for everyone involved in football.

Advanced prediction capabilities

Sportmonks’ Prediction API provides detailed forecasts for a range of betting markets, including match outcomes (home win, draw, away win), correct scores, over/under goals, and both teams to score (BTTS). These predictions are generated using machine learning algorithms that process a wide range of inputs, such as past results, team form, player stats, and more. What sets it apart is its forward-looking nature, the API delivers predictions up to 21 days before kick-off and refreshes them daily to ensure they reflect the latest data. This gives users a clear, timely understanding of likely match outcomes.

Integration and accessibility

Built with developers in mind, Sportmonks’ API is simple to use and easy to integrate into various platforms. Whether it’s a live score website, a fantasy sports app, or a football analytics tool, developers can quickly incorporate Sportmonks’ win probability data. This accessibility not only speeds up development but also improves the end-user experience by adding valuable insights to digital football platforms.

Value bet identification

In addition to basic predictions, Sportmonks offers a Value Bet API that compares model-generated probabilities with bookmaker odds to spot potential betting opportunities. By reviewing thousands of past odds and identifying where the model sees a better chance than the market suggests, users can pinpoint bets that offer strong value. This feature is especially useful for bettors who want to take a data-informed approach to their wagering.

Broad coverage and reliability

Sportmonks provides data across more than 2,500 football leagues worldwide, covering both major competitions and smaller regional tournaments. This wide reach ensures that users can access win probability predictions regardless of the match or league they’re following. Combined with its focus on data accuracy and consistent updates, Sportmonks has earned a reputation as a reliable partner for those looking to deepen their understanding of football outcomes.

Predict match outcomes with confidence using Sportmonks

Understanding win probability adds a whole new layer to how we watch and analyse football and Sportmonks makes it easy to access that insight. Whether you’re developing betting tools, enhancing fan experiences, or running your own football analysis platform, our Predictions API gives you accurate, data-driven probabilities up to 21 days in advance.

Get started with Sportmonks today and bring smarter forecasting, value bet spotting, and real-time win modelling into your football projects.