# Forecasting Goal Performance for Top League Football Players: A Comparative Study

Nikolaos Giannakoulas, George Papageorgiou, Christos Tjortjis

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Nikolaos Giannakoulas, George Papageorgiou, Christos Tjortjis. Forecasting Goal Performance for Top League Football Players: A Comparative Study. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.304-315, 10.1007/978-3-031-34107-6_24. hal-05498081

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# Forecasting Goal Performance for Top League Football Players: A Comparative Study

Nikolaos Giannakoulas, George Papageorgiou, and Christos Tjortjis

The Data Mining and Analytics Research Group, School of Science and Technology, International Hellenic University, Thessaloniki, Greece
c.tjortjis@ihu.edu.gr

**Abstract.** In this paper, we review the literature on Sports Analytics (SA) and predict football players’ scoring performance. Based on previous years’ performance, we predict the number of goals that players scored during the 2021–22 season. To achieve this, we collected advanced statistics for players from five major European Leagues: the English Premier League, the Spanish La Liga, the German Bundesliga, the French Ligue 1 and the Italian Serie A, for seasons from 2017–18 up to 2021–22. Additionally, we used one-season lag features, and three supervised Machine Learning (ML) algorithms for experimental benchmarking: Linear Regression (LR), Random Forest (RF) and Multilayer Perceptron (MLP). Furthermore, we compared these models based on their performance. All models’ results are auspicious and comparable to each other. LR was the best performing model with Mean Absolute Error (MAE) 1.60, Mean Squared Error (MSE) 7.06 and Root Mean Square Error (RMSE) 2.66. Based on feature importance analysis, we established that every player’s upcoming scoring performance is strongly associated with previous season’s goals (Gls) and expected goals (xG).

**Keywords:** Sports Analytics (SA) · Performance Prediction · Machine Learning (ML)

## 1 Introduction

Sports Analytics (SA) is a rapidly growing field, and its applications are very useful to sports clubs. This paper focuses on football, and it aims to investigate which Machine Learning (ML) approach performs better in seasonal performance forecasting for top League football players’ goals, which predictors are most significant for enhancing model predictive performance and finally, if performance prediction using just open data can be valid and accurate.

Currently, many football games take place every week and they produce numerous statistics. SA is used for collecting these statistics, analyzing them, and providing conclusions to sports clubs about their players’ performance. Over the last few years, SA has become a vital part of sports. Football is the most famous sport with millions of fans worldwide, and it produces numerous statistics including goals, assists and cards. Such
data about football generate interest for scientists who are keen on understanding this
domain.

SA provides useful information that can be used by coaches and managers to bet-
ter understand their teams. Sports organizations collect this information and make the
appropriate decisions about their players and related tactics. Moreover, SA has many
applications such as performance prediction, ticket prizes, betting, and a team's strategy
planning. One of the most important applications is the ability to predict player injuries
and the time required for cure. All these applications are very important for clubs because
they can improve their performance year by year and consequently increase their value
[22].

This research aims to predict player performance in terms of goals scored by utilizing their statistics from previous years. Data validity is one of the most important issues in SA, especially in football, because there are numerous sources with statistics that are invalid, and therefore could introduce errors and bias into results. For this purpose, we collected advanced football statistics from the Sports Reference organization, for seasons 2017-18 up to 2021-22 for players from all positions on the field [24]. The initial database created regards over 2.500 football players from five major leagues in the world, namely the English Premier League, the Spanish LaLiga, the German Bundesliga, the French Ligue1 and the Italian Serie A. Moreover, the database includes over 30 features about them.

The next step after data collection was cleansing, to remove data that do not add value for the purpose of this research [10]. Then, during feature engineering, we transformed the data into historical by creating one season's-lag features. After that, we used three different types of ML algorithms for the research's last but most significant stage, which was prediction. Linear Regression (LR), Random Forest (RF) and Multilayer Perceptron (MLP) were selected. Finally, we evaluated the models and the predictions they generated using Mean Absolute Error (MAE), Mean Squares Error (MSE) and Root Mean Square Error (RMSE) [6, 28]. Additionally, we investigated feature importance for each model, aiming to determine the most important factors that lead the forecasting, and draw conclusions based on our results.

The structure of the remainder of this paper follows. Background reviews SA back-
ground and previous work in football performance prediction. The Methodology section
presents the problem and the methodology we followed. In the Findings, we illustrate
results and compare algorithmic performance. The Discussion section discusses our
findings. In the Conclusion, we conclude and propose future work, and lastly, in the
Appendix, we illustrate the advanced football statistics we used.

2 Background

Nowadays, researchers are more likely to conduct research on football than in previous
years, because of the numerous statistics in this field. More and more clubs worldwide
hire data scientists to collect statistics and extract valuable information about their per-
formance, because this abundance of statistics helps coaches and managers to improve
their decision-making [4]. We live in an era with many available sources with regards
to previous research in the field of football performance prediction, so we can take
advantage of them to expand our knowledge.
In 2018, Pariath et al. predicted a player's overall performance and market value. For this purpose, they created two LR models for both cases. Using data from a famous video game, FIFA, they achieved good accuracy for both experiments. The first model, regarding general performance, achieved 84% accuracy, 2.67 RMSE and 2.01 MAE. The second model regarding market value achieved 91% accuracy, 0.41 RMSE and 0.30 MAE. Moreover, they concluded that the best performance indicator is the player's overall value [21].

Apostolou and Tjortjis, in 2018, conducted research in football with three experiments. In the first one, they predicted a player's position on the field based on his characteristics, and they achieved 81.5% accuracy using RF and Sequential Minimal Optimization. In the second one, they predicted the number of goals that Lionel Messi and Luis Suarez scored. RF, Logistic Regression, MLP and Linear Support Vector Classification were used, with RF being the most accurate. The last experiment focused on predicting the number of shots by a player during a match. Again, RF was the algorithm with the highest accuracy [3].

In 2017, Shrinivasan predicted the number of passes among players in a football match, using networks. He created passing networks for teams, based on their passing distributions, which measure how well the players pass the ball among them before a goal. Players are nodes and passes among them are edges in the networks created. He used Support Vector Machine, RF and Gradient Boosting (GB), and achieved MSE of around 6.88 [25].

Further research in this field was conducted by Manish et al. in 2021. In their research, they used different algorithms to predict football player statistics based on their position on the field in previous seasons. They found that the position of a player has a high dependence on his performance. Finally, the Multiple Regression algorithm was the most efficient one, with 0.88 MAE, 1.16 MSE and 1.077 RMSE [19].

Moreover, in 2019, Pantzalis and Tjortjis conducted two experiments. The first was team performance prediction, while the second was player performance prediction. During the first experiment, they used two different approaches. The former separated football teams into those whose final position was better than the previous year and these whose final position was worse than the previous year, achieving 70% accuracy. The latter simulates every match and classifies it based on the result as home/away win and draw. In that case, the accuracy was higher than that of the former approach. During the second experiment, they examined which attributes can influence a defender's rating during a game. They used Multiple LR, and the results were very good as the R-squared was 0.9 and the adjusted R-squared was 0.88. Finally, they found that 30 features can influence a defender's performance, including clearances and interceptions [20].

Moreover, in 2022 Al-Asadi and Tasdemir estimated players' market value through four different ML algorithms and identified the most significant features that determine this value. These algorithms were LR, Multiple LR, RFs and Decision Trees. RF had the best performance with 1.64 RMSE and 0.95 R2. Finally, they concluded that many factors could affect player value, like potential and international reputation [1].

Currently, more and more clubs use wearable devices, cameras, and magnetic jackets to collect statistics that can provide valuable information about their players [7]. For example, these statistics include heart condition, player movements, shooting habits and
general player's physique. All these statistics are considered confidential, and gaining access is challenging [11]. Therefore, there is limited research involving statistics from these devices regarding football players' physique.

Finally, in 2019, Frey et al. predicted the player's position on the field with the use of three different ML algorithms, RF, GB and Convolutional Neural Network. In this research, they collected data from wearable devices. They achieved accuracy around 80% for all models, and they found that there was limited misclassification in positions like wingers and midfielders, in which players share common characteristics [8].

# 3 Methodology

This section presents our Research Questions and elaborates on our data preparation and modeling approach.

## 3.1 Research Questions

As motivated by the literature review presented in Sect. 2, we attempted to address two key research questions, presented here.

1. Which ML approach performs best for football players' goals forecasting, and which predictors are significant for enhancing model predictive performance?

2. Could football players' performance forecasting be valid and accurate using just open data?

## 3.2 Data Preparation and Modeling

The process we followed was divided into six phases. These phases are data collection, in which data was scraped, pre-processing to remove noise as well as errors, feature engineering, in which data were transformed to historical data, modeling, and evaluation, in which we compared the results to find the best performing model.

First, the appropriate dataset should be collected. The dataset was scraped from the Sports Reference organization [24]. We extracted statistics for football players for the last five seasons, from season 2017–18 up to season 2021–22. We ended up with a database with almost 2.500 football players from five major leagues in the world, which are Premier League, LaLiga, Bundesliga, Ligue1 and Serie A. Moreover, the database included 32 features in total, including more than 15 advanced features, listed in the Appendix.

The next phase was pre-processing which is divided into data cleaning, player removal, feature removal and feature encoding. Starting with null and missing values removal, we then removed features that do not add value to our forecasting approach, such as nationality, competition, and squad. Then, we implemented feature encoding [14] transforming string values into numerical. Finally, we filtered the players who started their career after 2017–18 or finished their career before 2021–22. We ended up with a database of over 800 football players.

After that, the feature engineering phase follows. We used four out of five seasons of statistics (from 2017–18 up to 2020–21) for training and the last season (2021–22) for
testing. Next, we converted data to historical by creating season's lag-features. Season's lag-features are variables that contain data from prior time steps. In our approach, we transformed our initial dataset's features to one-season-lag-features for each player's record containing data from his last season's performance.

The algorithms used were LR, RF and MLP. Their selection was targeted, as all three models are fundamentally different. LR is the basic linear based algorithm, RF is tree based and MLP is a type of Artificial Neural Network (ANN) [27, 17]. In more detail:

**LR:** is a supervised ML algorithm and one of the most common models for predictions. LR models the relationship between dependent and independent variables. Moreover, it produces a straight line, known as regression line, which depicts the relationship among variables. This line can be positive or negative [2]. Generally, LR is a good tool for predictions because it is simple and easy to understand. On the other hand, it is very sensitive to outliers and missing values, which means that it can deteriorate performance [13].

**RF:** is a supervised ML algorithm that constructs different decision trees to solve a problem. RF creates decision trees based on the dataset, it aggregates every tree's prediction, and outputs the optimal result. This technique of using different models to achieve better performance is called model ensembling. Generally, it is very good at handling numerical data and capturing non-linear interactions between features and the target [2]. RF is robust to outliers and can handle many input variables. On the other hand, it can be slow to train and make predictions on large datasets [23].

**MLP:** is a feed forward ANN, consisting of at least three layers of nodes. These layers are known as the input, hidden and output layers. Each node in every layer is connected with a specific weight with all the nodes from the next layer. Besides the input node, the others are neurons that use a non-linear activation function, which defines the output of that node [26]. MLP can capture complex non-linear relationships among input and output variables. However, it requires an extensive dataset for good performance [12].

Finally, we evaluated our results with MAE, MSE and RMSE. These were selected for this purpose because the nature of our experiment and the specific dataset allow the use of them. Additionally, we investigated the more crucial factors that determine our forecasting by analyzing the weights assigned to each feature in the LR model and using feature importance scores from RF and MLP.

# 4 Findings

In this section, the algorithms' results, the use case of two football players, and the more important predictors for this experiment are presented. Generally, all algorithms performed well as their metric values are low, but LR performed better than the other two algorithms followed by MLP which in turn was closely followed by RF. The results are shown in Table 1.

LR resulted in the lowest MAE, MSE and RMSE. Then MLP follows, while RF's performance is the worst. MAE for all algorithms is between 1.60 and 1.69. Correspondingly, MSE ranges from 7.06 to 7.75, while RMSE ranges from 2.66 to 2.78. Generally, it is noticeable that all three models demonstrated good results with slight differences in their performances, with MAE from 1.60 to 1.69, when the Standard Deviation (SD) of Season's 2021–22 true Goals was four. This addresses our first Research Question.
<table><caption>**Table 1.** Results.</caption><thead><tr><th>Metrics</th><th>LR</th><th>MLP</th><th>RF</th></tr></thead><tbody><tr><td>MAE</td><td>1.60</td><td>1.63</td><td>1.69</td></tr><tr><td>MSE</td><td>7.06</td><td>7.68</td><td>7.75</td></tr><tr><td>RMSE</td><td>2.66</td><td>2.77</td><td>2.78</td></tr></tbody></table>

The predictions for two football players are presented next, in order to better understand the differences among models. Figure 1 illustrates the predictions for two football players, David Silva and Danny Welbeck. During season 2021–22, they scored 2 and 6 goals, respectively. Their selection is not random, as they are both midfielders and strikers. The results confirm that LR performs better, as it predicts that David Silva and Danny Welbeck would score 3 and 6 goals respectively. Then, MLP’s predictions follow predicting 3 and 7 goals respectively and finally the RF’s predictions for the two players are 4 and 7 goals, respectively.

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Performance Prediction Example.</figcaption></figure>

However, many players’ goals predictions were inaccurate. For instance, Lionel Messi used to score 30 goals in any average season, but in the season 2021–22 that we made the predictions for, he scored just six goals. However, the algorithms identified and followed the earlier trend and predicted that he would score at least 20 goals.

We then produced coefficients for the LR model, features importance for RF, and weighted coefficients for MLP to determine the relative importance of each feature and the most important factors in predicting the performance of football players who belong to the selected pool [18, 29]. Table 2 shows the results of these identifications for each model.

Based on these results, the top three features for LR, that have the highest positive impact on predicting future goals for our pool of players are the previous season’s non-penalty expected goals per 90 min (npxG per 90), the previous season’s expected goals per 90 min (xG per 90), and previous season’s goals plus assists per 90 min (G + A per 90).
Additionally, the RF model indicates that the previous season's expected goals (xG) is the most important predictor, followed by previous season's non-penalty expected goals (npxG) and previous season's expected goals per 90 min (xG per 90).

Finally, the MLP model shows that the top four features that contribute significantly to our predictions are previous season's goals per 90 min (Gls per 90), previous season's expected goals plus expected assisted goals per 90 min (xG + xAG per 90), previous season's goals plus Assists per 90 min (G + A per 90), and previous season's non-penalty expected goals plus expected assisted goals per 90 min (npxG + xAG per 90). This addresses our first Research Question.

<table><caption>**Table 2.** Feature importance and coefficients (Top 3 Highlighted per Model).</caption><thead><tr><th>Features</th><th>LR (Coefficient)</th><th>RF</th><th>MLP (Weights Coefficient)</th></tr></thead><tbody><tr><td>npxG per 90:</td><td>33.4578</td><td>0.0148</td><td>11.3758</td></tr><tr><td>xG per 90:</td><td>28.1599</td><td>0.0571</td><td>11.0137</td></tr><tr><td>G+A per 90:</td><td>23.7282</td><td>0.0160</td><td>11.3785</td></tr><tr><td>G-PK per 90:</td><td>19.7128</td><td>0.0187</td><td>10.9199</td></tr><tr><td>Ast per 90:</td><td>12.4782</td><td>0.0197</td><td>10.4302</td></tr><tr><td>xG+xAG per 90:</td><td>11.3893</td><td>0.0414</td><td>11.4351</td></tr><tr><td>G+A-PK per 90:</td><td>11.0409</td><td>0.0167</td><td>11.1907</td></tr><tr><td>Gls per 90:</td><td>7.5871</td><td>0.0186</td><td>11.7314</td></tr><tr><td>xAG per 90:</td><td>4.9581</td><td>0.0185</td><td>10.1835</td></tr><tr><td>npxG+xAG per 90:</td><td>4.3857</td><td>0.0166</td><td>11.8397</td></tr><tr><td>PKatt:</td><td>2.8251</td><td>0.0049</td><td>10.0188</td></tr><tr><td>PK:</td><td>2.3797</td><td>0.0060</td><td>9.4945</td></tr><tr><td>npxG:</td><td>1.9374</td><td>0.1302</td><td>9.6061</td></tr><tr><td>xG:</td><td>1.5504</td><td>0.4370</td><td>10.3756</td></tr><tr><td>npxG+xAG:</td><td>1.1518</td><td>0.0504</td><td>11.2250</td></tr><tr><td>xAG:</td><td>1.0387</td><td>0.0225</td><td>10.2168</td></tr><tr><td>CrdR:</td><td>0.2143</td><td>0.0044</td><td>9.8497</td></tr><tr><td>90s:</td><td>0.1642</td><td>0.0239</td><td>10.2893</td></tr><tr><td>Starts:</td><td>0.1618</td><td>0.0144</td><td>9.7125</td></tr><tr><td>G-PK:</td><td>0.0872</td><td>0.0182</td><td>9.4400</td></tr><tr><td>Ast:</td><td>0.0521</td><td>0.0093</td><td>9.3500</td></tr><tr><td>MP:</td><td>0.0255</td><td>0.0215</td><td>10.6533</td></tr><tr><td>CrdY:</td><td>0.0185</td><td>0.0194</td><td>11.1278</td></tr></tbody></table>

# 5 Discussion

In this section, we discuss and evaluate our results in the light of the Research Questions stated in Sect. 3. Generally, the results were good for all 3 algorithms particularly given that we used only open data for our experiments. Comparing algorithmic performance, we observe that LR is the best, with MAE 1.60, MSE 7.06 and RMSE 2.66. LR can accurately predict players' scoring capability with a slight deviation of almost two goals, while the SD for Goals during the 2021–22 season was four.
The other two algorithms, MLP and RF, are also reliable for prediction, given their corresponding very low MAE, MSE and RMSE. Moreover, their results are very similar. For instance, their MAE is 1.63 and 1.69, respectively. However, we observe from the results that MLP performs better than RF, because its metrics are lower than those of RF. Their MSE is 7.68 and 7.75, respectively, while their RMSE is 2.77 and 2.78 respectively. This addresses our first Research Question.

Furthermore, with regards to our first Research Question, we investigated the predictors that are most important for enhancing model predictive performance. Each feature related to the previous season's expected goals (xG) is a consistently important feature in forecasting player performance across all three models. Additionally, statistics related to the previous season's goals are also crucial for prediction-making. These imply that a player's ability to create high-quality goal-scoring opportunities and opportunities for teammates is critical to their overall performance. In contrast, some features, such as the previous season's Minutes Played (MP), Yellow Cards (CrdY), and Red Cards (CrdR), appear to have relatively little impact on predicting future goals based on these models.

Regardless of the positive results obtained, it is important to consider threats to the validity of research findings. Players' performance deviation across their carriers is affected by multiple factors. Factors contributing to players' performance are injuries sustained during a specific period, the "chemistry" among team players and squad, transfers, and conditions in different countries that are transferred; each psychological state could be a daunting experience for a player [5].

Every profound change in a player's state of mind, physical health, and teamwork with coaches and other players could affect the next season's performance and produce anomalies in his enduring performance momentum. Moreover, Messi's use case for season 2021-22, which we presented, provides valuable insights based on the earlier assumptions; Messi in Barcelona from seasons 2017-18 to 2020-21 scored 30 to 35 goals having a specific scoring momentum with slight deviations while transferring in the summer of 2021 to Paris Saint-Germain, resulting in his declining scoring performance with 6 season's 2021-22 goals. This also partly addresses our second Research Question.

# 6 Conclusions

## 6.1 Conclusion

In this paper, we aimed to predict the performance of football players who are currently active in the best European leagues. We predicted how many goals a player would achieve in season 2021-22 based on his previous years' performance, from season 2017-18 up to 2020-21. To achieve this, we implemented three ML algorithms: LR, RF and MLP. The models were evaluated with the use of MAE, MSE and RMSE. We found that LR performs better than the other two algorithms. The results are good for all models, as the predictions were very close to the goals actually scored by the players. LR's performance was MAE 1.60, MSE 7.06 and RMSE 2.66. MLP's performance was MAE 1.63, MSE 7.68, and RMSE 2.77. Lastly, RF's performance was MAE 1.69, MSE 7.75 and RMSE 2.78.
Additionally, based on feature importance analysis of these models, we concluded that specific advanced performance statistics from the previous season are strongly associated with predicting the future goals of our selected football players' pool. The most important features identified across all models include the previous season's xG and Gls. These findings provide valuable insights for teams and coaches, as they can use this information to identify and recruit players with high goal-scoring potential and develop existing players' skills in key areas to improve their performance.

In order to excel in forecasting goal scoring performance, the changes in a player's state of mind, physical health, and teamwork with coaches and other players should also be considered, and their effect evaluated. Considering only open advanced football statistics provide us with a good overview of each player's upcoming performance, however, the performance momentum, if any of the above changes are applied, should be detected with external transfer data, data associated with performance based on synergies and different tactics for each player's or next season's team.

Furthermore, because of this research findings, while xG is the most critical statistic for predicting upcoming scoring performance, data from wearable devices and cameras for identifying physical players' health movements on the field are considered essential, capturing each player's performance momentum and ability to create and finish scoring chances.

## 6.2 Future Work

The results from our research indicate that we can predict a player's performance for the next season with sufficient accuracy, based on historical open data. However, further work in this field could yield further improvements. Obviously, results may improve with the addition of more features. Players' physical characteristics like height and weight, number of successful passes and minutes with ball possession during a game, are some examples of such features. Moreover, using different ML techniques could improve performance.

Furthermore, it would be very helpful to take advantage of statistics from wearable devices, magnetic jackets, and cameras. Also, future research can focus on strikers. As mentioned earlier, it is more challenging for the algorithms to predict the goals of a center forward whose scoring capability presents fluctuations. Lastly, future work with different techniques can be done to expand this research. For instance, association rules and sentiment analysis can be utilized. Association rules' implementation will help to find patterns and correlations in the database that are probably undiscovered, while applying sentiment analysis can be very helpful to consider for instance the players' feelings, information that will be useful to our predictions [9, 15, 16].

**Acknowledgments.** This research is co-financed by Greece and the European Union (European Social Fund-SF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning 2014–2020» in the context of the project “Support for International Actions of the International Hellenic University”, (MIS 5154651).
# Appendix

We include here both basic and advanced features for forecasting experiments.

## Basic and Advanced Features Dataset Glossary

<table><thead><tr><td>Term</td><td>Meaning</td></tr></thead><tbody><tr><td>npxG</td><td>Non-penalty expected goals</td></tr><tr><td>npxG per 90</td><td>Non-penalty expected goals per 90 min</td></tr><tr><td>xG</td><td>Expected goals</td></tr><tr><td>xG per 90</td><td>Expected goals per 90 min</td></tr><tr><td>G-PK</td><td>Non-penalty goals</td></tr><tr><td>G-PK per 90</td><td>Goals minus penalty kicks per 90 min</td></tr><tr><td>Ast</td><td>Assists</td></tr><tr><td>Ast per 90</td><td>Assists per 90 min</td></tr><tr><td>npxG + xAG</td><td>Non-penalty expected goals plus expected assisted goals</td></tr><tr><td>npxG + xAG per 90</td><td>Non-penalty expected goals plus expected assisted goals per 90 min</td></tr><tr><td>xAG</td><td>Expected assisted goals</td></tr><tr><td>xAG per 90</td><td>Expected assisted goals per 90 min</td></tr><tr><td>xG + xAG per 90</td><td>Expected goals plus expected assisted goals per 90 min</td></tr><tr><td>G + A-PK per 90</td><td>Goals plus assists minus penalty kicks per 90 min</td></tr><tr><td>G + A per 90</td><td>Goals plus assists per 90 min</td></tr><tr><td>Gls per 90</td><td>Goals per 90 min</td></tr><tr><td>PKatt</td><td>Penalty kicks attempted</td></tr><tr><td>PK</td><td>Penalty kicks made</td></tr><tr><td>CrdR</td><td>Red cards</td></tr><tr><td>90s</td><td>Minutes played divided by 90</td></tr><tr><td>Starts</td><td>Games started</td></tr><tr><td>MP</td><td>Games played</td></tr><tr><td>CrdY</td><td>Yellow cards</td></tr></tbody></table>

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