The allure of predicting football, or soccer, is a captivating one. It blends statistical analysis with the raw passion of sport, promising a way to find order in the beautiful game’s inherent chaos. For many, it starts not as a data science problem, but as an intuitive guessing game, a childhood fascination with tournament brackets and the thrill of correctly calling an upset. That simple act of forecasting, whether it’s for a World Cup or a European Championship, plants a seed. It’s a desire to move from pure intuition to an informed methodology, a journey that many data professionals embark on to test their skills against one of the most unpredictable sports on the planet.
This series will explore a comprehensive project aimed at predicting match outcomes, specifically for a major tournament like the EUROs. The primary objectives are twofold: first, to predict the categorical result of a match (a win, a draw, or a loss) and second, to predict the specific number of goals scored by each team. This is not just a quest for a correct prediction; it is a deep dive into data pipelines, feature engineering, and the nuanced application of machine learning. We will dissect the process from start to finish, highlighting the unique challenges, the strategies employed, and the inevitable lessons learned when data meets the world’s most popular sport.
The Statistical Anomaly of Football
At the heart of the prediction challenge lies the statistical nature of football itself. It is famously a “low-scoring” game, a characteristic that separates it from sports like basketball or even handball. In those sports, the high frequency of scoring events—baskets, goals—provides a large sample of data points within a single match. This large sample size means that the final score is more likely to be a true reflection of the teams’ relative quality and performance over the course of the game. The law of large numbers takes hold, and the team that creates more and better chances generally wins.
Football defies this. A typical match might see only two or three goals, and many matches are decided by a single goal or end in a draw. This low number of “success” events (goals) means that randomness, or luck, plays a disproportionately large role. A single deflected shot, a controversial refereeing decision, or a moment of individual brilliance can decide the outcome, completely overriding the statistical dominance one team may have held for ninety minutes. This makes football outcomes notoriously variable and difficult to model. The “better” team, by any statistical measure, does not always win, a fact that models must struggle to account for.
The “Low-Scoring” Problem and Its Impact on Modeling
The low-scoring nature of football has profound implications for predictive modeling. Statistically, goal scoring in football is often compared to a Poisson distribution, a model for rare events. This variability means that while one team might generate many high-quality scoring opportunities (high “expected goals,” or xG), they may still fail to score due to excellent goalkeeping, poor finishing, or simple bad luck. Conversely, an opposing team might have only one significant counterattack in the entire match and score, “stealing” a win. This disparity between process (chance creation) and outcome (goals) is a core problem.
For a machine learning model, this creates a very “noisy” dataset. The “signal” (the true quality and performance of a team) is often obscured by the “noise” (the randomness of individual match outcomes). This makes it difficult for a model to learn consistent patterns. While in handball, frequent scoring chances tend to average out and produce more predictable results, football operates differently. A team can “park the bus,” deploying a deeply defensive strategy, and hope for one decisive counterattack. This tactic, while perhaps not statistically dominant, can be highly effective, and it further complicates the task of predicting a match based on historical performance data.
Path Dependency and the First Goal
Football matches are also characterized by high path dependency. This means that the course of the match, and its eventual outcome, is heavily dependent on the sequence of events within it—most notably, the first goal. The timing and identity of the first goalscorer can dramatically alter the entire dynamic of the game. A team that scores first can fall back into a defensive posture, protecting their lead and forcing the other team to take risks. The trailing team, now needing to chase the game, must open up, which in turn leaves them more vulnerable to counterattacks.
This first goal fundamentally changes the “state” of the game. A model trained on pre-match statistics has no way of knowing when or if this state change will occur. A team that is statistically inferior but manages to score an early goal against the run of play may see its probability of winning skyrocket, not because its underlying quality has changed, but because the game’s context has. This dynamic, a cascade of cause and effect, is incredibly difficult to capture with static, pre-match features. Small, seemingly insignificant actions can lead to a goal or a red card, and these events can completely invalidate the initial pre-match predictions.
The Unpredictable Human Element: Form and Mentality
Beyond the statistics, football is a game played by humans, and this introduces a level of psychological unpredictability that is almost impossible to quantify. There is the elusive concept of “form of the day,” a factor that every fan knows and dreads. A team can be notoriously unpredictable, capable of defeating a world-class opponent one week and losing to a relegation-bound team the next. This “moody diva” syndrome, as fans of some clubs might call it, is a very real phenomenon. Players and teams can have off days, or conversely, enter a state of “flow” where everything they try succeeds.
This human element extends to mentality, pressure, and team cohesion. Are there key player injuries? Is there discord in the dressing room? Is it a high-stakes knockout match versus a low-pressure group stage game? These factors, which are often invisible in standard datasets, can have a massive impact on performance. While a model can be fed data on recent wins or losses to approximate “form,” it cannot truly capture the psychological state, confidence, or motivation of a squad on match day. These unmeasured variables contribute significantly to the model’s error margin.
The VAR Effect: A New Layer of Unpredictability
In recent years, the introduction of the Video Assistant Referee (VAR) has added yet another layer of complexity and, some would argue, unpredictability. While VAR was designed to correct clear and obvious errors and make the game “fairer,” it has also changed its flow. Crucial decisions—goals, penalties, red cards—can be overturned minutes after the event. This system can reverse the very path-dependent events that shape a match. A goal that sends a stadium into elation and changes the game’s dynamic can be abruptly chalked off, resetting the match’s state.
For predictive modeling, VAR introduces another variable. It can interrupt the momentum of a team, and its decisions, while often correct by the letter of the law, can feel arbitrary in the moment. It changes the risk-reward calculation for defenders and attackers, and it adds time and psychological tension to the game. While in theory it should lead to “more correct” outcomes, in practice it adds another element of variance that a pre-match statistical model simply cannot account for, as it operates on the micro-level of individual plays and refereeing interpretations.
The Challenge of Data Limitations
Finally, any predictive model is only as good as the data it is fed, and football models often face significant data limitations. The ideal model would have detailed, player-specific data. It would know which key players are available, their current performance metrics, and if they are carrying an injury. National teams, while not subject to the transfer market like clubs, still change composition over time, and the model should ideally reflect the quality of the current squad, not just the team’s historical name. However, this granular player data is often proprietary, expensive, or difficult to obtain and integrate.
Similarly, the lack of in-game data for historical matches is a major hurdle. Statistics like real-time possession, pass completion rates, or the minute of the first goal could help a model learn the patterns of path dependency. Without these, the model is left “blind,” working only with pre-match information and the final score. These data limitations force the modeler to find creative solutions, such as feature engineering and data imputation, to try and approximate these missing, critical factors. These strategies are often a compromise, an attempt to find a proxy for the information the model truly needs.
The Data Pipeline’s Critical Role
Having established the immense challenges of football prediction, the practical journey begins. The backbone of any machine learning project, and especially one of this complexity, is a robust data pipeline. This is the infrastructure that turns raw, disparate data from various sources into a clean, structured, and feature-rich dataset ready for modeling. This process is often the most time-consuming and least glamorous part of data science, yet it is arguably the most critical. A sophisticated model fed with poor, unrepresentative data will produce poor, unreliable predictions.
This part of the series will detail the process of building that foundation. We will explore the strategies for data collection, the critical steps of data preprocessing and cleaning, and the methods used to address the data limitations discussed in Part 1. This involves not only gathering historical match data but also creatively engineering features to stand in for missing player-specific or in-game statistics. This is the data engineering work that makes the subsequent analysis and prediction possible, turning a collection of raw facts into a dataset that holds predictive power.
The Quest for Data: Scraping and Aggregation
The very first step is data collection. This project requires comprehensive information about past matches. The data must be scraped from various online sources, such as specialized football data websites and online sports encyclopedias. This process involves building automated scripts to navigate these websites, identify the relevant data—like match dates, teams, scores, and tactical formations—and extract it into a structured format. This is a delicate operation, requiring careful handling of website structures, request rates, and potential changes in page layouts.
Once collected, the raw data is often fragmented. One source might provide basic match results, while another provides tactical data, and a third might offer playstyle metrics. The next step is to combine and aggregate this information. For instance, data from one source might be combined with data from a different analytics platform to include playstyle features like average ball possession or team efficiency metrics. The goal is to create a single, comprehensive DataFrame where one row corresponds to one match from the an in-depth, programmatic exploration of this data collection process, one can refer to the project’s data scraping notebook.
Confronting the Gaps: Strategies for Missing Data
No real-world dataset is perfect, and data scraped from various sources is almost guaranteed to have gaps. This project is no exception and must employ several strategies to handle missing data to ensure the final dataset is both comprehensive and reliable. The choice of strategy depends entirely on the nature of the missing data and the importance of the feature. Simply ignoring missing data is not an option, as it can bias the model or cause it to fail entirely.
The primary strategies fall into three categories: imputing the missing data, excluding or replacing the feature, and removing the observations. Each approach has trade-offs. Imputation can save valuable data but risks introducing artificial patterns. Exclusion simplifies the model but can mean losing predictive power. Removal is the cleanest option but is only viable if the amount of missing data is very small. A successful project requires a thoughtful application of all three techniques, guided by domain knowledge and statistical analysis.
Imputation: The Art of the Educated Guess
Imputation is the process of filling in missing data points with substituted, estimated values. For this project, this was a key strategy. For example, tactical formations for upcoming, unplayed matches are unknown. However, most national teams train and test a specific formation and tend to stick with it. A reasonable imputation strategy is to assume that a team will play in the formation they used most frequently in their last five matches. This assumption allows the model to make predictions before the official line-up is released, which typically happens only an hour before the match.
This is an “educated guess” based on domain knowledge. Other, simpler imputation methods might involve filling missing numerical values with the mean, median, or mode of the column. While less sophisticated, these methods can be effective for maintaining the overall statistical properties of the dataset. The key is to ensure that the imputation process does not fundamentally distort the data or introduce biases that the model will mistakenly learn as a true pattern.
Feature Exclusion and Replacement: When to Fold
Sometimes, a feature is too difficult to impute or its source data is too unreliable. In these cases, the best strategy is to either exclude the feature entirely or replace it with a simpler, more robust proxy. For example, an initial idea for this project was to implement a complex, two-level prediction. This would involve using primary features (like team rankings) to predict secondary, in-game features (like ball possession and expected goals), and then using those predicted secondary features to predict the final match outcome.
This approach, while academically interesting, adds significant complexity and potential for error propagation. A simpler, more practical decision was made to abandon this two-level procedure. Instead, the concept was simplified. Each team was assigned two features based on their match average statistics to represent their playstyle. This replacement captures the spirit of the original idea—modeling a team’s style—but in a more direct and less error-prone way, ensuring that the model is not compromised by the instability of a complex, multi-stage prediction.
Data Removal: The Last Resort
The final strategy for handling missing data is the most drastic: removing the data entirely. This can mean removing an entire row (an observation) or an entire column (a feature). If a particular feature is missing for the vast majority of matches, it is likely useless and should be removed. If a small number of matches are missing several critical features, it may be better to remove those rows to maintain the integrity of the dataset.
In this project, fortunately, the need for data removal was minimal. After imputation and feature exclusion strategies were applied, only a small number of observations had to be dropped. This was a positive outcome, as it meant the dataset remained large and comprehensive, containing over 3,000 matches. This large sample size is crucial for training a machine learning model, as it provides enough examples for the model to learn the subtle and complex patterns of football outcomes.
The Player-Specific Data Void
One of the most significant data limitations that must be addressed is the lack of player-specific data. Without detailed information on the players in each squad, the model is missing critical factors. The presence or absence of a single “key player”—a star striker or a world-class defender—can dramatically influence a match outcome. Furthermore, individual performance metrics, injuries, or suspensions are all vital pieces of information that are not captured in a simple team-level dataset.
To work around this, the model must rely on proxy features. Team “form” or “team essence” features are an attempt to capture this. The assumption is that the recent performance of the team (average goals scored, etc.) implicitly reflects the current health and performance of its key players. While this is a far cry from true player-level modeling, it is a necessary compromise when faced with the limitations of publicly available data.
The In-Game Data Void
Similarly, the lack of historical in-game data is a major gap. As discussed in Part 1, path dependency is a huge factor in football. Knowing the minute of the first goal, which team scored it, or how possession percentages shifted after a red card would provide invaluable information for a model. This data would help trace the patterns of momentum and how matches unfold, making it easier to predict the final direction a match might take.
Without this data, the model is restricted to a pre-match perspective. It can only predict the outcome based on the state of the teams before the whistle blows. It cannot adapt its prediction based on how the game is actually progressing. This is a fundamental limitation of this type of model. Addressing it would require access to rich, event-based “in-play” data for thousands of historical matches, which is often behind expensive paywalls and reserved for professional betting syndicates and clubs.
Beyond Raw Data
With a clean and preprocessed dataset, the next and perhaps most creative step begins: feature engineering. This is the process of using domain knowledge to transform raw data into features that better represent the underlying problem for the machine learning model. A model, especially a statistical one, rarely learns effectively from raw, unprocessed data. It needs to be “taught” what to look for. Feature engineering is this teaching process. It involves extracting, transforming, and creating new variables (features) that capture the tactical, psychological, and historical nuances of a football match.
This phase is where a data scientist’s intuition and understanding of the sport become a critical asset. A well-engineered feature can be the difference between a mediocre model and a highly accurate one. In this project, several key features were meticulously crafted to encapsulate the complex dynamics of a football match, from tactical setups and team form to abstract concepts like “team essence.” This part will explore the “why” and “how” of these crucial features.
Capturing Tactical Nuance: Formations
A team’s tactical formation is a fundamental piece of information. It dictates their shape on the field, the balance between offense and defense, and how they match up against an opponent. The model was fed features representing the formation of both teams, including specific counts of strikers and defenders. The goal was to allow the model to identify patterns of outcomes between various popular formations. For example, does a 4-3-3 formation (typically offensive) have a statistical advantage over a 5-3-2 formation (typically defensive)?
By including the formations of both teams, the model can also learn about “matchup” dynamics. Perhaps a certain offensive formation is highly effective against most teams but struggles against a specific defensive setup. These kinds of complex, interactive patterns are exactly what a machine learning model is designed to find. This feature attempts to codify the strategic “chess match” that happens between the two managers before the game even begins, providing a quantitative proxy for tactical intent.
Quantifying the Favorite and Underdog Dynamic
In any given match, there is almost always a “favorite” and an “underdog.” This status, whether formal or informal, affects the psychology, pressure, and expectations for both teams. To measure this dynamic simply and objectively, a feature was derived from official rankings. For European teams, their level in the UEFA Nations League could be used, while for non-European teams, their FIFA World Ranking range could serve as a proxy. This feature provides a clear, numerical indication of which team is expected to perform better based on their long-term, demonstrated quality.
This feature is powerful because it condenses a vast amount of historical performance data into a single, comparative metric. It helps the model understand the “expected” outcome, which it can then compare against other features. For instance, if a high-ranked favorite is playing against a low-ranked underdog, the model will expect a win for the favorite. It can then learn from “surprises” by seeing what other features (like poor “form” for the favorite) were present when the expected outcome did not materialize.
Modeling “Form”: The Recent Performance Window
A team’s ranking represents its long-term quality, but its “form” represents its short-term performance. A top-ranked team can be in a period of poor form, and a-mid-ranked team can be on a “hot streak.” To capture this, features were engineered based on the team’s last five matches. Specifically, the average number of goals scored and goals conceded in this recent window provided a snapshot of the team’s current offensive potency and defensive solidity. The choice of a five-match window is a heuristic, an attempt to find a balance between being too noisy (one or two matches) and too stale (ten or twenty matches).
These “form” features are often highly right-skewed; a team might score zero goals in three matches and then seven in their fourth. To handle this, the logarithm of these features was taken. This transformation compresses the range of the values, making the feature more normally distributed and preventing extreme outliers from having an undue influence on the model. This is a standard preprocessing step that helps stabilize the model’s learning process.
Defining “Style of Play” with Data
Beyond tactics and form, teams have an inherent “style of play.” Some teams dominate possession, patiently passing the ball, while others are comfortable without the ball, preferring to strike on the counterattack. To capture this, the model used features like average ball possession percentages. However, possession alone is not enough; a team can have high possession and still be inefficient. To get a deeper insight, a more sophisticated feature was created by clustering teams based on their efficiency versus their vulnerability.
Efficiency was calculated as the ratio of actual goals scored to “expected goals” (xG), a metric that quantred teams were clustered based on these two dimensions. This created categorical labels (e.g., “High Efficiency, Low Vulnerability,” “Low Efficiency, High Vulnerability”). These clusters were then used as a feature, allowing the model to understand a team’s fundamental playstyle and how that style might fare against an opponent’s style.
The Psychology of the Pitch: Tournament Stage
The context of a match matters immensely. A group stage match where a draw is good enough for both teams is a very different affair from a “win or go home” knockout phase final. The pressure, stakes, and strategic incentives change completely. To account for this, the tournament stage (group stage or knockout phase) was included as a feature. The idea was to allow the model to discover patterns in performances depending on these external pressures.
For example, the model might learn that certain teams or tactical styles thrive in high-stakes knockout rounds, while others tend to “choke.” It might also learn that draws become more or less likely at different stages. This feature provides critical context, modifying the interpretation of all other features. A team’s “form” or “style” might have a different predictive impact in a group stage match compared to a semi-final.
“Team Essence” and Long-Term Trends
After accounting for tactics, form, rank, style, and stage, there is often some inexplicable “rest” variance. This is the idea that there is some intangible “essence” to a team, a national footballing culture, or a “golden generation” of players that is not captured by the other features. To represent this, a “team essence” feature was created, which was essentially a unique identifier for each team.
When combined with a time variable, this feature allows the model to learn long-term trends for each specific team. For example, the model could learn that “Team X” has been on a general upward trajectory over the last few years, even if their short-term “form” fluctuates. This provides a comprehensive view of the team’s overall performance trend, capturing elements not covered by any other feature and serving as a catch-all for these unmeasured, long-term dynamics.
Final Preparation: Scaling and Encoding
Before this rich dataset can be fed into a model, one final preprocessing step is required. Machine learning models work with numbers, not with “Germany” or “4-4-2.” Categorical features like team names and formations must be converted into a numerical format. This was done using one-hot encoding, which creates binary columns for each category, preventing the model from assuming a false ordinal relationship (e.g., it prevents the model from thinking “Team C” is somehow “greater” than “Team A”).
Furthermore, numerical features often exist on different scales. A team’s ranking might be a number between 1 and 200, while average goals scored might be between 0.5 and 3.0. Many models, especially those based on distance (like K-Nearest Neighbors) or optimization (like Support Vector Machines), are sensitive to these different scales. To fix this, the features were normalized. A MinMaxScaler was used for the team-specific variables to maintain the scale of zero-representing matches (which is important for one-hot encoded features), while a StandardScaler was used for all other numerical features to give them a mean of zero and a standard deviation of one. This ensures all features contribute equally to the model’s learning process.
Choosing the Right Tool
With a meticulously engineered and preprocessed dataset, the project moves from data preparation to the predictive phase. The central question now becomes: which machine learning model is best suited for this complex task? The project’s goal is twofold—a classification task (predicting win, draw, or loss) and a regression task (predicting the number of goals). Given the noise and complexity of football data, there is no single, obvious “best” model. A “silver bullet” algorithm for sports prediction does not exist.
Therefore, the most robust strategy is to adopt a “black box” approach. This means, rather than committing to a single model based on theory, we will test a wide array of different models from various families of machine learning. By evaluating a broad selection, we can empirically discover which algorithms are best at finding the signal within this specific dataset. This “survival of the fittest” approach helps to understand the strengths and weaknesses of different techniques and ultimately leads to a more optimized and reliable final prediction system.
The Models Under Consideration
The selection of models to test was broad, covering the most powerful and popular algorithms in the modern machine learning toolkit. For each conceptual model, both its classification version (for outcome prediction) and its regression version (for goal prediction) were tested. This comprehensive trial included tree-based ensembles, sophisticated boosting machines, and classic statistical models.
The list of contenders included: Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extra Trees, Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and, for the regression task, a baseline Linear Regression. Each of these models “learns” in a fundamentally different way, bringing a unique perspective to the data. Testing them all ensures that no stone is left unturned in the search for predictive accuracy.
Tree-Based Ensembles: Random Forest and Extra Trees
Random Forest is a classic “ensemble” model. Its core idea is “wisdom of the crowd.” It builds a large number of individual decision trees during training. Each tree is trained on a random subset of the data and a random subset of the features. This randomness ensures that the individual trees are all different and “uncorrelated.” To make a prediction, the Random Forest model polls all the trees in its “forest” and goes with the majority vote (for classification) or the average prediction (for regression). This approach makes it highly robust against overfitting and very effective at capturing complex, non-linear relationships in the data.
Extra Trees, or Extremely Randomized Trees, takes this concept a step further. Like Random Forest, it builds a forest of trees. However, it adds another layer of randomness: when splitting a node in a tree, it doesn’t search for the best possible split among features; it selects a split threshold at random. This makes the model even faster to train and can sometimes lead to better generalization by reducing variance even more.
Classic Boosting: Gradient Boosting and AdaBoost
While Random Forest builds its trees in parallel, “boosting” models build their trees sequentially. This is a “teamwork” approach, where each new tree is built to correct the errors made by the previous trees. Gradient Boosting is the most prominent example. It starts with a simple, weak tree and then iteratively adds new trees. Each new tree is trained to predict the “residual error” of the ensemble of trees that came before it. This step-by-step refinement allows the model to “boost” its performance, often leading to state-of-the-art results on structured data. It is a powerful, high-performance algorithm but can be sensitive to its parameters and prone to overfitting if not tuned carefully.
Adaptive Boosting (AdaBoost) is one of the earliest and most influential boosting algorithms. Its approach is slightly different. Instead of training on the residual errors, AdaBoost iteratively adjusts the “weights” of the training data. In each iteration, it pays more attention to the data points that the previous models got wrong. This forces the algorithm to focus on the “hard” cases, and the final prediction is a weighted vote from all the weak learners, with the more accurate learners getting a bigger say.
Advanced Boosting Machines: XGBoost, LightGBM, and CatBoost
The original Gradient Boosting algorithm was powerful but slow. This led to the development of a new generation of highly optimized boosting libraries. Extreme Gradient Boosting (XGBoost) is perhaps the most famous. It is an implementation of Gradient Boosting that has been “weaponized” for speed and performance. It includes advanced features like “regularization” (to prevent overfitting), parallel processing capabilities, and the ability to handle missing values natively. It became famous for winning countless data science competitions and is often the first model data scientists turn to for tabular data.
Light Gradient Boosting Machine (LightGBM) is another competitor, developed to be even faster and more memory-efficient than XGBoost. Its key innovation is “leaf-wise” tree growth, which allows it to converge on a good solution more quickly, making it ideal for very large datasets. Categorical Boosting (CatBoost) is the third major player. Its unique strength, as its name suggests, is its sophisticated, built-in handling of categorical features (like “Team Name” or “Formation”). It uses a special technique to encode these features that avoids the pitfalls of standard one-hot encoding, often leading to better results and simplifying the feature engineering pipeline.
Other Contenders: SVM and K-Nearest Neighbors
Not all powerful models are based on trees. The Support Vector Machine (SVM) operates on a completely different principle. It attempts to find the “best” possible hyperplane (a line in two dimensions, a plane in three, etc.) that separates the data points into their different classes. The “best” hyperplane is the one that has the maximum possible “margin,” or distance, from the nearest data points of each class. This “maximum-margin” approach makes it very robust. For complex, non-linear data, SVMs use a “kernel trick” to project the data into a higher-dimensional space where a linear separation becomes possible.
K-Nearest Neighbors (KNN) is one of the simplest machine learning algorithms. It is an “instance-based” learner, meaning it doesn’t really “learn” a model at all. Instead, it just memorizes the entire training dataset. To make a new prediction, it looks at the “K” (a number, e.g., 5 or 10) data points in the training set that are “nearest” (most similar) to the new, unseen data point. It then makes a prediction based on a majority vote (for classification) or an average (for regression) of these neighbors. It’s simple and interpretable but can be slow for large datasets and suffers from the “curse of dimensionality.”
Establishing the Baseline: Cross-Validation
To compare this diverse set of models, a rigorous evaluation strategy is needed. It’s not enough to just train a model on all the data and see how well it did; this would lead to overfitting. The standard approach is Cross-Validation (CV). In k-fold cross-validation, the dataset is shuffled and split into “k” equal-sized folds (e.g., k=10). The model is then trained “k” times. In each run, one fold is held out as the “test set,” and the model is trained on the other “k-1” folds. The model’s performance is then measured on the held-out fold.
This process is repeated “k” times, with each fold getting a chance to be the test set. The final performance of the model is the average performance across all “k” folds. This technique provides a much more robust and reliable estimate of how the model will perform on new, unseen data. All the models in this project were instantiated, fitted, and evaluated using this cross-validation framework to establish a fair and reliable performance baseline for each one.
Optimizing the Best: Tuning and Feature Selection
Once the initial baseline models are evaluated, the best-performing ones are selected for further optimization. This involves two key steps. The first is hyperparameter tuning. Most models have “hyperparameters,” which are settings that control how the model learns (e.g., the number of trees in a Random Forest, or the “learning rate” in Gradient Boosting). This step involves systematically searching for the combination of these parameters that yields the best performance, fine-tuning the model to the specific data.
The second step is recursive feature selection. Just because we created a lot of features doesn’t mean they are all useful. Some might be redundant or even add noise. This technique iteratively builds the model, identifying the most relevant features and discarding the least important ones. This “pruning” process can lead to a simpler, faster, and more accurate model by focusing only on the features with the most significant impact. However, in some cases, the model performed best using all features, and in those instances, the full-featured version was used.
The Nuance of Model Application
Selecting and tuning the best-performing models is a major milestone, but it is not the end of the journey. A trained model outputs raw numbers—probabilities for classification, and continuous float values for regression. These raw outputs are not the final prediction. They must be intelligently processed, combined, and interpreted to become actionable insights. This “model application” phase is a critical step that translates the model’s statistical language into the language of football: clear outcomes, goal counts, and real-world betting options.
This part of the series will detail the workflow for taking the outputs from the chosen models and turning them into the final predictions. This includes a specialized strategy for handling draws, a method for converting regression-based goal predictions into probabilities, and a system for normalizing and combining predictions from different models to ensure the final results are coherent, logical, and ready for analysis.
The Two-Model Approach: Why Draws Are Different
During the model selection phase, a key observation was made: predicting draws is a fundamentally different and more difficult problem than predicting a win or a loss. In the training data, draws are a minority class, accounting for only about 20% of all matches. Most standard classification models, which are optimized for overall accuracy, will learn to be risk-averse. They will favor predicting the majority classes (win or loss) and often underestimate the chance of a draw, sometimes even failing to predict a single one. This leads to a high accuracy (the model is correct 80% of the time just by never predicting a draw) but a precision of zero for draws (it never correctly identifies a draw).
To solve this, a specialized, two-model approach was adopted. Instead of using a single multi-class model to predict (Win, Draw, Loss), two separate models were built. The first model was a highly-tuned Support Vector Classifier (SVC) whose sole job was to answer the binary question: “Will this match be a draw?” The second model, using a different algorithm like CatBoost or AdaBoost, was trained to predict the win/loss outcome. This distinction was necessary because the patterns and features that influence a draw (e.g., two evenly matched teams playing defensively) are different from those that lead to a decisive win.
Normalizing Probabilities for a Coherent Outcome
This two-model system creates a new challenge: its outputs are not a single, coherent probability distribution. The “draw” model might output a probability for a draw, and the “win/loss” model might output its own probabilities. These must be combined and normalized to ensure the final probabilities for all three outcomes (Team A Win, Draw, Team B Loss) add up to 100%.
After applying the chosen models to a match, we get separate probabilities. A normalization step is applied to these raw outputs. This mathematical process scales the three “scores” (the draw model’s score, the win/loss model’s score for Team A, and the win/loss model’s score for Team B) so that they sum neatly to 1.0. This gives a clear, interpretable set of probabilities for the match outcome. For example, the final normalized output might be: Team A Win: 45%, Draw: 30%, Team B Win: 25%. This is a crucial step for making the model’s output logical and usable for any downstream application.
The Challenge of Duality: Averaging Predictions
The dataset was structured such that for every match to be predicted, there are two rows. One row represents the match from the perspective of “Team A vs. Team B,” and the other represents “Team B vs. Team A.” This is necessary for training, but in prediction, it means the model produces two sets of outputs for the same game. For example, the first row’s output might predict a 1.8 goal count for Team A, and the second row’s output might predict a 0.9 goal count for Team A (as the “away” team).
These values should not differ significantly from each other, and in most cases, they do not. However, when they do, it serves as a valuable red flag, signifying that the model is uncertain about this particular matchup and the prediction should be treated with caution. To consolidate these two outputs into a single, definitive prediction for the match, a simple averaging step is used. The predicted values from both rows are averaged to produce one final value for each label (e.g., one probability for a Team A win, and one float number for Team A’s predicted goals).
Predicting Goals: From Regression to Poisson Distribution
The goal prediction models (like CatBoost and Gradient Boosting) were trained as regression models. This means their output for a given team is a single, continuous float number, like 1.76. This is the model’s best “point estimate” for the number of goals. However, a team cannot score 1.76 goals. We need a way to convert this float number into probabilities for the discrete, real-world outcomes: 0 goals, 1 goal, 2 goals, 3 goals, and so on.
This is a perfect use case for the Poisson distribution. The Poisson distribution is a statistical tool used to model the probability of a given number of events occurring in a fixed interval, given the average rate of that event. In this context, the “event” is a goal, the “interval” is the 90-minute match, and the “average rate” is the 1.76 goals predicted by our regression model. By plugging this “lambda” (the average rate) into the Poisson formula, we can instantiate a full probability distribution and ask, “Given an average of 1.76, what is the probability of scoring exactly 0 goals? Exactly 1 goal? Exactly 2 goals?” This transforms the vague regression output into a rich set of actionable probabilities.
Deriving Betting Options and Final Selections
With this full set of probabilities, we can now derive any number of popular betting options. By summing the probabilities from the Poisson distributions of both teams, we can calculate the probability that the total match goals will be “Over 2.5” or “Under 2.5.” By combining the outcome probabilities, we can create “Double Chance” bets (e.g., the probability of “Team A Win or Draw”). This makes the model’s output incredibly flexible.
For the project’s own final “tipping” predictions (e.g., for an office pool), a simple decision rule was used. For the match outcome, the single outcome with the highest normalized probability was chosen. For the goal predictions, the most likely number of goals for each team was selected from its Poisson distribution (the “peak” of the distribution). This provides a single, concrete prediction, like “Team A wins 2-1.”
Handling Conflicting Predictions
Because the outcome model and the goal models are trained separately, their predictions can sometimes conflict. For example, the outcome model might predict a “Team A Win” with high confidence, but the Poisson distributions for the goal models might suggest the most likely score is “Team A: 1, Team B: 1,” which is a draw. This is a common problem when using specialized models.
A hierarchy must be established to resolve these conflicts. Given that the analysis showed the categorical outcome predictions were generally more reliable (higher accuracy) than the goal predictions (lower R-squared), a decision was made to prioritize the outcome. In case of a mismatch, the goal predictions are adjusted to align with the predicted outcome. For example, if the model predicts a “Team A Win” but the most likely score is 1-1, the goal prediction would be adjusted to the next most likely score that is a win, such as 1-0 or 2-1.
The Tipping vs. Betting Dilemma
It is crucial to understand the difference between making predictions for a “tipping” competition versus “sports betting.” In a tipping competition, the goal is to maximize accuracy. You want to be correct as often as possible to accumulate the most points. This means you should almost always pick the “favorite” or the most probable outcome.
Sports betting is a completely different game. The goal is not accuracy; it is profitability. This means you are looking for “value bets,” where you believe the true probability of an outcome is higher than the probability implied by the bookmaker’s odds. Precision, or the correctness of your positive predictions, is more important. You might have a model that is only 50% accurate overall, but if it’s 70% precise when it identifies a high-value underdog bet, it could be extremely profitable. This project’s models are primarily built for the “tipping” or “accuracy” paradigm.
The Accuracy vs. Precision Trap: The Draw Example
The importance of this distinction is perfectly illustrated by the problem of predicting draws. As mentioned, a model could achieve 80% accuracy simply by never predicting a draw (since 80% of matches are not draws). However, its precision for draws would be 0% (it never correctly identifies one). This model would be “accurate” but useless.
The specialized SVC model for draws was built to solve this. It achieved a lower overall accuracy of 79%, but its precision was 47%. This is a massive improvement, starting from zero. It means that when this model does predict a draw, it is correct almost half the time. This is a much more useful model, as it has learned the actual patterns that lead to a draw, even if it’s not perfect. This trade-off—sacrificing a tiny amount of overall accuracy to gain a huge amount of precision on a specific, difficult-to-predict class—is a key part of building an intelligent and useful predictive system.
Evaluating the Final Product
The data has been gathered, the features engineered, and the models built, tuned, and applied. The final step is to dispassionately evaluate the results. How well do the models actually perform? What are their strengths and, more importantly, their weaknesses? This part of the series will analyze the final performance metrics, discuss how these models are applied to simulate a real-world tournament, and reflect on the key learnings and a path forward for future improvements. This is where the theoretical work of data science meets the practical, often humbling, reality of sports prediction.
The journey from a simple childhood bet to a complex machine learning pipeline is one of continuous learning. The project, while successful in creating a predictive framework, also illuminates the vast room for improvement and the inherent challenges that will always make football the “beautiful,” unpredictable game. We will explore the performance scores, their real-world implications, and the next steps to making the model even more robust.
Model Performance: Outcome Predictions
The models for predicting the categorical match outcome (win/loss) performed reasonably well. The best models, specifically CatBoost and AdaBoost, were chosen for the final win/loss predictions. These models achieved a maximum accuracy of 72% and a precision of 70% during cross-validation. This means that when the model predicted a team would win, it was correct about 70% of the time. While this is a solid score and significantly better than chance (which would be 33%), it also means the model is wrong nearly 30% of the time, highlighting the inherent difficulty of the task.
The specialized Support Vector Classifier (SVC) for draws achieved an accuracy of 79% and, more critically, a precision of 47%. While 47% precision might not sound high, it is a significant achievement for a low-probability, hard-to-predict class. It demonstrates that the model successfully identified specific patterns for draws, providing a vast improvement over a naive model that would have 0% precision.
Model Performance: Goal Predictions
The performance of the regression models for predicting goals was more modest. The best-performing models here were again CatBoost and Gradient Boosting. These models achieved a minimum Root Mean Squared Error (RMSE) of 1.19 goals. This metric means that, on average, the model’s prediction for the number of goals a team would score was off by about 1.19 goals. Given that most teams score between 0 and 2 goals, this is a significant error margin.
Furthermore, the R-squared (R2) score, which measures how much of the variance in the data the model can explain, was a maximum of 0.23. An R2 of 1.0 would be a perfect model, while 0.0 means the model is no better than simply guessing the average. A score of 0.23 indicates that the model is only able to explain 23% of the variance in goal-scoring. This confirms that predicting the exact number of goals is far more difficult than predicting the simple outcome. This discrepancy is why, in cases of conflict, the (more reliable) outcome prediction was prioritized over the (less reliable) goal prediction.
The Goal Distribution Anomaly
An interesting quirk of the final goal prediction system was observed when looking at its outputs. The model, when its Poisson distribution was used to select the “most likely” outcome, exclusively resorted to predicting scores of zero, one, or two goals for any team in any match. While this seems overly conservative, it is actually a logical consequence of the training data. Looking at the overall goal distribution in the dataset, scores of 0, 1, and 2 are overwhelmingly the most common.
A score of 3 is less common, and scores of 4 or more are very rare. The model, trained on this reality, learns that high-scoring matches are low-probability events. As a result, it will never predict a 4-3 thriller as the most likely outcome, even if it might assign it a small non-zero probability. This is a key limitation to understand: the model is optimized for the common case and will, by design, fail to predict the exciting, high-scoring outliers.
Applying the Model: Simulating a Tournament
With the models built, they can be applied to a real tournament, such as the EUROs. For the group stage, this is straightforward. The model takes the pre-match features for all teams and generates probabilities and predicted scores for the initial slate of matches. However, predicting the knockout stages is a far more complex challenge. The matchups for the knockout rounds are not known in advance; they are entirely dependent on the results of the group stage.
This introduces a massive layer of compounding uncertainty. To predict the Round of 16, the model must first simulate the entire group stage. A single incorrect prediction in the group stage—a predicted win that becomes a draw—could change a team’s finishing position from second to third, completely altering the knockout bracket and invalidating all subsequent predictions. This is further complicated by formats where the “best third-placed teams” advance, creating a complex web of dependencies based on results from other groups. Therefore, any full-tournament simulation must be taken with a large grain of salt.
Key Learnings and Workflow
This project reinforced several key lessons. First, predicting football is as challenging as it is fun. The high path dependency and the significant impact of small, random events make it a difficult domain for machine learning. The second key learning was the critical importance of a well-structured workflow. Planning the data flow, project structure, and how different notebooks or scripts interact from the beginning is essential. A clean, logical workflow saves enormous amounts of time, simplifies troubleshooting, and enhances the overall quality and readability of the code.
For instance, separating data scraping, feature engineering, model training, and prediction application into distinct, modular components is a best practice. This allows for easier iteration and debugging. If a feature needs to be changed, only the feature engineering module needs to be run, not the entire pipeline. This structure is a vital, though often un-discussed, part of a successful data science project.
The Path Forward: More Data
To improve these predictions, the most obvious path is to incorporate more data. A larger dataset, with more historical matches, can help the model recognize more subtle patterns. For example, the current model might not have enough examples of a “4-2-3-1” formation playing against a “3-5-2” to draw a strong conclusion. More data provides a more comprehensive foundation, especially for identifying patterns between less common feature combinations. This helps to smooth out noise and improve the model’s generalization to new, unseen matchups.
The Path Forward: Richer Features
Beyond just more data, the model would benefit from better data in the form of new, more insightful features. For example, one could engineer a feature for “experience” by calculating the average number of international matches played per squad player, indicating the team’s familiarity with high-pressure tournament settings. Another powerful feature would be to incorporate pre-match betting odds. By scraping data from bookmaker websites, the model could get a real-time “market sentiment” of the favorite/underdog status, which is often more accurate than static rankings. While these odds change frequently, they provide a very strong contextual signal.
The Ultimate Goal: Player-Level and In-Game Data
The “holy grail” for this type of project would be to transcend the limitations of pre-match, team-level data. The next major evolution would be to incorporate detailed, player-specific data. Knowing the individual performance metrics, fitness levels, and even the market values of the 23 players on each squad would provide a much more granular and accurate view of team quality than a simple “team essence” feature.
Finally, integrating historical “in-game” data would allow the model to learn about path dependency. By training a model on real-time statistics—possession, shots, pass accuracy, and player movements throughout the match—it would be possible to build a dynamic prediction model. This model could update its predictions during the match, reacting to a goal or a red card, moving from simple pre-match forecasting to a true “in-play” analytics engine. This extended approach represents the future frontier of sports analytics.
Conclusion
This project provided a comprehensive overview of the challenges and methodologies involved in predicting football outcomes. It highlights the entire data science journey, from the initial spark of an idea to the detailed process of data collection, feature engineering, model selection, and critical performance evaluation. The integration of data science and sports analytics provides exciting and engaging opportunities for enthusiasts and professionals alike. While the current models provide a solid foundation, the goal remains to continuously refine them, expand their features, and apply them to new challenges, contributing to the ever-growing and fascinating field of sports analytics.