Data Predicting European Top 5 League Soccer Team’s Points

Posted on Jan 9, 2022

The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Data Science Introduction

In professional sports, we constantly hear the saying that “every game counts” and players are expected to give their all from the beginning to end of a season. Those familiar with professional sports in America know that teams will often tank a season to be compensated with priority draft picks for the next season based on previous data.

However, the top flight leagues in England, Spain, Germany, Italy, and France have a drastically different setups. Teams that finish in the top 3-4 places are rewarded with Champions League qualification for the following season, while those fighting for 4th-7th are competing for other competitions such as the Europa League and the Europa Conference League. On the other side of the league table, teams that finish in the bottom 2-3 places of their respective league face relegation.

Whether a club’s ambition is to challenge for Champions League qualification or avoid relegation, it is important to understand that failure to meet these objectives can destabilize the finances of the organization. For example, Barcelona is viewed as a behemoth of the sport across the globe. In the pursuit of staying competitive domestically and on the European stage, the club started spending above their means. Inflated transfer fees, massive contracts, and a downturn of results on the pitch were only magnified by the crippling effects of COVID. As a result, the club was reported to be $1.5 billion in debt by the summer of 2021.

For this reason, it is essential for a club to set their short/long term goals and structure a payroll that compliments their vision. As seasons are concluded by point totals which are highly influenced by goals, I evaluated 4 models which predict the amount of points a team will garner based on their total shots, shot % on target, and touches in the penalty area.

Data

To build this model, all data was sourced from Wyscout. The data is comprised of metrics from the top 5 European leagues (Premier League, Serie A, La Liga, Ligue 1, & Bundesliga) ranging from the 2015/16 season through the 2020/21 season. Data from Ligue 1 during the 2019/20 season was omitted since the season was halted without a resumption due to COVID.

Overall, the data frame is comprised of the following columns: Team, League, Year, Points, Total Shots, Shot % on Target, and Touches in the Penalty Area. Each row is a team’s data from a respective season. Before building the model, the data was randomly split into sub data set. The train data set was comprised of 75% of the data and was used to build the models, while the test data set was comprised of the remaining 25% and was tested against the model for accuracy.

Multiple Linear Regression - Training Dataset

For this project, 4 models were built to compare which can most accurately predict a club’s point total over the course of a season. The models are listed below:

  • mod_1 – Using Total Shots, Shot % on Target, and Touches in Penalty Area to predict Points.
  • mod_2 – Using Total Shots and Shot % on Target to predict Points.
  • bc_mod_1 – Box Cox with the same x variables as mod_1
  • bc_mod_2 – Box Cox with the same x variables as mod_2

While there were clear linear relationships between all three x variables and points, there was a slight skew when interpreting total shots and total touches in the penalty area. This is expected in soccer, because there are typically large gaps between top clubs and mid table clubs, as well as mid table clubs from relegation bound clubs. The Box Cox models were an attempt to normalize this.

Data Predicting European Top 5 League Soccer Team’s Points

Data Predicting European Top 5 League Soccer Team’s Points

Data Predicting European Top 5 League Soccer Team’s Points

Data Predicting European Top 5 League Soccer Team’s Points

Additional Data Exploration - Do All Leagues Share the Same Trend

In order to ensure all leagues share the same linear trend, ggplot scatter graphs were created with each league being separated via facet wrap. Images are pictured below:

Multiple Linear Regression – Test

Once all models were completed and confirmed significant by their p-value, we used the predict function in R to predict the point outcomes from our Test data set against each model. Upon completion, the predicted point totals were merged to 4 versions of the Test data set. Each new data set then had the predicted points generated from each model subtracted from the actual point metric, to generate a new column which displayed the point difference. Finally, we computed the mean point difference across the entire data set to find which was closest to the actual point metric, on average.

Below, we can see a snapshot of a summary table which shows the key takeaways from each model.

From this table, I conclude that mod_1 would be the strongest to utilize over the long term. At first glance, it seems as if the box cox models have stronger metrics. The RSE is drastically lower, both AIC/BIC are smaller than the non box cox models, and the R^2 adj is comparable to the non box cox models. However, when evaluating the average point differential, the standard models are far more accurate.

The average point difference, which is the most important factor when a club would be utilizing a model, is drastically more accurate on the non box cox models. For that reason, we will evaluate model 1 and 2. Model 1 has a lower RSE, higher R^2adj, and a smaller AIC/BIC. In addition to this, when we tested the model the average point difference was -0.2483004. This is closer than the average point difference of mod_2, which came out to -0.4957172.

Mod_1 Breakdown

Below, we can see some key takeaways listed from the summary of mod_1 and some plots:

  • All coefficients are significant.
  • The overall F-statistic is significant so the overall regression is significant.
  • The RSE is 9.894, which is an estimate of the average deviation of the observations around the regression line.
  • The R^2 adjusted is 0.6836, meaning 68.36% of the variation in points is accounted for by the variables in our model.
  • The VIF for all variables are below 5.0, so we don't have an issue with multi-collinearity.
  • Residuals vs Fitted Plot doesn't show a distinctive patter, indicating we don't have non-linear relationships.
  • Normal Q-Q Plot shows the residuals follow a straight line, with some slight deviation at the ends.
  • Scale-Location Plot shows that the residuals appear to be randomly spread, showing there will not be an issue with variance.
  • Residuals vs Leverage Plot shows that we do not have any outliers that are influential against the regression line.
Call:
lm(formula = Points ~ Total.Shots + Shot...on.Target + Total.Touches.in.Penalty.Area, 
    data = training_set)

Residuals:
    Min      1Q  Median      3Q     Max 
-30.910  -6.528  -0.367   6.348  34.309 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   -64.983879   5.562015 -11.684  < 2e-16 ***
Total.Shots                     0.091293   0.009888   9.233  < 2e-16 ***
Shot...on.Target                1.607951   0.149640  10.745  < 2e-16 ***
Total.Touches.in.Penalty.Area   0.030064   0.005400   5.567 4.59e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 9.894 on 427 degrees of freedom
Multiple R-squared:  0.6858,	Adjusted R-squared:  0.6836 
F-statistic: 310.7 on 3 and 427 DF,  p-value: < 2.2e-16

                  Total.Shots              Shot...on.Target Total.Touches.in.Penalty.Area 
                     2.895768                      1.219490                      3.245311 

 

Data Predicting European Top 5 League Soccer Team’s Points

Data Predicting European Top 5 League Soccer Team’s Points

Conclusion

In conclusion, mod_1 would be an effective model to predict points based on total shots, shot % on target, and total touches in the penalty area. A club can estimate these totals based on the data from the players they have on their roster, compute team totals over the course of a 34-38 match season (depending on the league), and plug them into our trained model to find out how many points they expect their team to generate.

Based on the predicted point metric, they can use historical league data to find out what position in the table they will find themself in. This will allow them to structure the player payroll accordingly, or act if any changes are needed.

Bonus - Shiny App

In addition to the model, a Shiny App was created from the complete data set. The app displays a histogram which shows the total shots taken by each team in an individual season. The user can alter the number of bins displayed on the histogram by adjusting the slider on the right. Below the histogram, we find a summary of the data. Lastly, we can see a histogram that displays points on the y axis and total shots on the x axis. The link to the Shiny App is below:

https://kdmarcopulos.shinyapps.io/Euro5_Shots_Points/

About Author

Kosta Marcopulos

My name is Kosta Marcopulos and I worked as an Assistant Buyer for Ross Stores after graduating with a degree in finance and economics from the University of Alabama. After working in the fashion industry for two years,...
View all posts by Kosta Marcopulos >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI