implementing the Expected Goal (xG) model to predict scores in soccer matches

Umami, Izzatul; Gautama, Deden Hardan; Hatta, Heliza Rahmania

doi:10.47738/ijiis.v4i1.76

Cited by 19 publications

(16 citation statements)

References 13 publications

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“…They use tree-based classification: XGBoost, Random Forest, Light GBM and CatBoost, also propose utilising Aggregated Profiles (AP) to demonstrate the difference in model predictions depending on a change in the value of a feature. (Umami et al, 2021) use factors such as distance and angle of shot on goal. Utilising Distance and Angle as one combined variable, this paper demonstrates that it had a greater impact on calculating the xG.…”

Section: Academic Look To the Xg Metricmentioning

confidence: 99%

A Machine Learning Approach for Player and Position Adjusted Expected Goals in Football (Soccer)

Hewitt¹,

Karakuş²

2023

Preprint

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Football is a very result-driven industry, with goals being rarer than in most sports, so having further parameters to judge the performance of teams and individuals is key. Expected Goals (xG) allow further insight than just a scoreline. To tackle the need for further analysis in football, this paper uses machine learning applications that are developed and applied to Football Event data. From the concept, a Binary Classification problem is created whereby a probabilistic valuation is outputted using Logistic Regression and Gradient Boosting based approaches. The model successfully predicts xGs probability values for football players based on 15,575 shots. The proposed solution utilises StatsBomb as the data provider and an industry benchmark to tune the models in the right direction. The proposed ML solution for xG is further used to tackle the age-old cliche of: 'the ball has fallen to the wrong guy there'. The development of the model is used to adjust and gain more realistic values of expected goals than the general models show. To achieve this, this paper tackles Positional Adjusted xG, splitting the training data into Forward, Midfield, and Defence with the aim of providing insight into player qualities based on their positional sub-group. Positional Adjusted xG successfully predicts and proves that more attacking players are better at accumulating xG. The highest value belonged to Forwards followed by Midfielders and Defenders. Finally, this study has further developments into Player Adjusted xG with the aim of proving that Messi is statistically at a higher efficiency level than the average footballer. This is achieved by using Messi subset samples to quantify his qualities in comparison to the average xG models finding that Messi xG performs 347 xG higher than the general model outcome.

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Section: Academic Look To the Xg Metricmentioning

confidence: 99%

A Machine Learning Approach for Player and Position Adjusted Expected Goals in Football (Soccer)

Hewitt¹,

Karakuş²

2023

Preprint

View full text Add to dashboard Cite

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“…Given the game's low-scoring nature, understanding and pre-processing the dataset is crucial in model training. Eggels et al [18], Anzer and Bauer [19], Haaren [14] and Umami [15] take the raw positional x and y data to train the model. Mustafa Cavus and Przemyslaw Biecek [20] utilise simple trigonometry to convert these position values to angle and distance values to the centre of the opposition's goal.…”

Section: Processing Datamentioning

confidence: 99%

“…We compared the performance of our proposed models with the models presented in the literature of Eggels et al [18], Anzer and Bauer [19], Haaren [14], Umami [15], Cavus and Biecek [20], in terms of precision, recall, accuracy, F1, AUC and Brier Loss in Table 3. The comparison uses these measures since the respective authors of these papers reported the performance of the models along the same.…”

Section: Models Comparisonmentioning

confidence: 99%

Leveraging Classification Models to Understand Football Players' and Teams' Characteristics

Rajagopalan

Srid

2023

Preprint

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The Expected Goals (xG) is a performance metric used to evaluate a football team's or a player's performance. Simply put, it represents the probability of a scoring opportunity that may result in a goal. This metric suits the low-scoring nature of sports such as football. The score of a match involves randomness and inexplicable factors that skew the data represented by standard metrics and often may not represent the actual performance of an individual or a team; therefore, it would be of more significant benefit to individuals trying to analyse a player or a team to use alternative statistics rather than shots on target, ball possessions percentage, and sprints completed. The xG Model is trained on several key metrics derived from on-field events, corroborating with the historical to measure the probability of a shot being a goal by the common goal. The selection of these features, the size and date of the data, and the model used as the parameters that may affect the model's performance. Using machine learning models to increase the model's predictive performance decreases the vagueness caused by subjective interpretation. This paper proposes an accurate expected goal model trained on a compiled dataset containing data from the FIFA World Cup 2018 and 2022, and the UEFA Champions League 2018-2022, with a total of 768,744 shots taken by the top players take when representing their country and club on the biggest stage. Moreover, this model is explained by using data visualisation tools to obtain an explainable expected goal representation for evaluating a team or player's performance. Moreover, these methods can be generalised to other sports.

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“…It is not clear when the expected goals statistic was first developed and who conceived it, with most [ 1 , 9 , 10 ] stating that Macdonald’s [ 11 ] study into shot outcome in ice hockey originated the term, whilst others [ 3 ] have attributed it to Green’s [ 12 ] article. At its core, the concept of expected goals can be thought of as a classification problem (due to it being a probability of a shot being on target) this is why, in order to calculate these probabilities, machine learning and statistical methods are applied.…”

Section: Introductionmentioning

confidence: 99%

Expected goals in football: Improving model performance and demonstrating value

2023

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Recently, football has seen the creation of various novel, ubiquitous metrics used throughout clubs’ analytics departments. These can influence many of their day-to-day operations ranging from financial decisions on player transfers, to evaluation of team performance. At the forefront of this scientific movement is the metric expected goals, a measure which allows analysts to quantify how likely a given shot is to result in a goal however, xG models have not until this point considered using important features, e.g., player/team ability and psychological effects, and is not widely trusted by everyone in the wider football community. This study aims to solve both these issues through the implementation of machine learning techniques by, modelling expected goals values using previously untested features and comparing the predictive ability of traditional statistics against this newly developed metric. Error values from the expected goals models built in this work were shown to be competitive with optimal values from other papers, and some of the features added in this study were revealed to have a significant impact on expected goals model outputs. Secondly, not only was expected goals found to be a superior predictor of a football team’s future success when compared to traditional statistics, but also our results outperformed those collected from an industry leader in the same area.

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implementing the Expected Goal (xG) model to predict scores in soccer matches

Cited by 19 publications

References 13 publications

A Machine Learning Approach for Player and Position Adjusted Expected Goals in Football (Soccer)

A Machine Learning Approach for Player and Position Adjusted Expected Goals in Football (Soccer)

Leveraging Classification Models to Understand Football Players' and Teams' Characteristics

Expected goals in football: Improving model performance and demonstrating value

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