Predicting the Outcome of Soccer Matches Using Machine Learning and Statistical Analysis
Bibliographic record
Abstract
The prediction of future soccer match outcomes has been a challenging task for data scientist for years. Researchers used different features to represent soccer teams performance and players skills. These features are then used to create predictive models using machine learning algorithms. This paper presents a new hybrid approach to predict the outcome of future soccer matches. Our hybrid approach combines machine learning and statistical models to predict future match outcomes. The paper analyzes the hidden patterns within a training dataset that has the results of 205,182 soccer match outcomes, played between 2000/2001 and 2016/2017 seasons. Using feature engineering techniques, the paper explores individual leagues and teams statistics, discusses the impact of playing at home or away on winning the match, compares the effectiveness of using only recent match outcomes data versus all matches in the training set, and evaluates the prediction accuracy when creating separate models for each league versus a single model for all leagues. This paper presents two different hybrid models to predict soccer match outcomes. Our best model achieved 46.6% prediction accuracy of the test set at a ranked probability score of 0.2176.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".