Leo Messi and Cristiano Ronaldo have been the two most consistent performers in world football for over a decade, and their evolution with age in terms of goals proves it.
Almost for the past 15 years, two monsters have stood out above the rest for their consistency, importance, statistics and records broken. Those are, of course, Lionel Messi and Cristiano Ronaldo. But not only have they been utterly dominant since they started to stand out when they were teenagers; they have almost got better with the passing years too. They are like fine wine: they get better with age. In Leo’s case, he has been expanding his set of skills and been maturing his overall game. From an electric and individualistic right winger, to a false 9 that found the net with unparalleled regularity, to an all-rounder that carries his team on his back through goals and also assists, creativity and dribbling. On the other hand, Ronaldo first was an explosive winger with unmatched physical gifts that terrified defences with his one-on-ones and capacity to create chances out of nowhere. As his physical conditions deteriorated, though, he knew how to reinvent himself by taking up a more central position, both in his final years at Real Madrid and during his time at Juventus.
Therefore, it is not surprising to see in the graph below how Cristiano’s goal rate increased significantly when he was 24 years old – that’s when he made the switch from the Premier League to Spain. At United he registered respectable figures at 118 goals in 292 games in all competitions, being the league’s top scorer in the 2007/08 campaign with 31 goals, but his numbers (0.404 goals per match) were still far from the ones he’d manage at the Santiago Bernabéu. With Madrid he scored 450 goals in 438 competitive appearances, with an outstanding ratio of 1.027 goals per game. At Juve, despite a slight and natural decrease in his ratio, he has found the net 53 times in 75 clashes – 0.707 goals per match.
Messi and Cristiano Ronaldo’s goal evolution by age | Figures for club and country, by @Maluem_
Meanwhile, Messi also experienced an impressive improvement in front of goal when he was young, but, by contrast, his goalscoring peak arrived when he was 24 or 25, scoring a historic 91 goals for Barcelona and Argentina in 2012. With that record he overcame Gerd Müller’s 85 strikes in 1972 for the most goals scored in a single calendar year. Leo’s goalscoring rate has continued to be unbelievably high, even if it was obviously impossible to match 2012’s astonishing numbers. In total, the little genius has 627 goals in 718 duels for Barça (0.873 goals per match), including his first appearances in the senior squad when he was a youngster.
Overall, while goals by no means are the only part of these two freaks’ game, it is clear how they have been maturing with age, both reaching a turning point when they were 24 years old. Messi’s ratio has always been higher than Ronaldo’s, but, all in all, we have to feel privileged to have been able to witness such consistent performers regarding football’s most prized asset: goals.
Using Machine Learning to predict Barcelona’s 2021/22 league season
Johan Cruyff famously said, “I’ve never seen a bag of money score a goal.” It is indisputable that money is not the only deciding factor behind a football club’s success. Time and time again, we have seen that football is chaotic and absurd. Anything can happen.
That being said, it is evident that statistically, the clubs with more money are usually the more successful ones. While one cannot predict the unpredictability that is engraved within football, an outcome that is statistically probable can be predicted. This article will explore the relationship between the points a club accumulates in a league season and the total value of the club’s squad. Then, Python will be used to build a linear regression model in order to predict the number of points Barcelona will obtain in the 21/22 season based on the club’s hypothetical squad.
Let us begin with visualizing the relationship between the total league points obtained by a club and the club’s total squad value. Since the amount of money spent on transfers has increased tremendously over the course of the last decade, in this article, only seasons 2014/15 – 2019/20 will be considered. Upon plotting the total points against the total squad value, it is evident that there is a positive correlation between the two. Granted, the correlation is not extremely strong, but it exists nonetheless.
A line of regression has been generated. The line’s gradient is approximately 0.05138986, and its y-intercept is approximately 44.5470726. There is only one dependant variable in question, club value/total value of squad, and so our line of regression can be modelled by the equation:
P = (0.05138986 )V + 44.5470726 where P is the points and V is the total squad value.
club_values = np.array(df['Values']).reshape(89,1) club_points = np.array(df['Point']).reshape(89,1) from sklearn.linear_model import LinearRegression linear_model = LinearRegression() linear_model.fit(club_values,club_points)
In order to predict the points accumulated based on Barcelona’s squad value next season, the squad value won’t directly be inputted into our equation above. The coefficient and the y-intercept have been rounded up after a certain number of decimal places, and so, to preserve the precision of the prediction, Python’s LinearRegression predict method will be utilized.
Now comes another challenging part of coming up with Barcelona’s hypothetical squad next season. Based on reports, it can be assumed that Samuel Umtiti, Miralem Pjanic, Martin Braithwaite and Phillipe Coutinho will be some of the notable players to leave. To compensate for the outgoing players, it can be assumed that Barcelona will sign Eric Garcia, David Alaba, Georginio Wijnaldum and Memphis Depay. Here is a table that contains all the players in Barcelona’s hypothetical 21/22 squad and their corresponding market values:
|PLAYER NAME||MARKET VALUE (MILLION POUNDS)|
|MARC ANDRE TER STEGEN||79.34|
|FRENKIE DE JONG||84.63|
|KONRAD DE LA FUENTE||2.64|
The club’s total value comes up to be £786.60 million. It is important to note that a player’s value is volatile. Transfermarkt updates player values every few months, and so it is extremely probable that this squad’s market value will be drastically different next season.
It is also important to understand that Transfermarkt’s market values are not official and so they are not always an accurate representation of a player’s true market value. Moreover, a player’s value is not always an accurate representation of their quality. Age is a key factor in determining market value, and so while a young player might have a high market value indicating that they are a bright prospect for the future, they might not necessarily be an excellent player at the time when the value is assigned.
Now that all of this has been established, the only thing left is making the prediction.
The machine learning model predicts that Barcelona will obtain 85 points in the 21/22 La Liga season. In the 19/20 season, Real Madrid won the league with 87 points, and in the 18/19 season, Barcelona won the league with 87 points as well. The difference between the points accumulated by the last two winning La Liga squads and the predicted points that Barcelona’s 21/22 squad will obtain is a mere 2 points. This shows that it is possible for Barcelona to win the league next season.
Since the correlation between the two variables, as observed from the data from the last 6 seasons, is not even close to being extremely strong, Barcelona’s 21/22 league points tally may be drastically different from what was predicted by the machine learning model. However, at the end of the day, the model can only work with the data it is exposed to.
Erling Haaland can change the course of Barcelona’s season. It would take forever to make predictions based on Barcelona’s hypothetical squads that include all of the club’s potential transfer targets, however, due to Haaland’s influence, an exception can be made for him. Laporta’s interest in signing Haaland has been widely reported.
However, he will cost a fortune this summer, and with Barcelona’s debt situation, it is unlikely that the Catalan club will pursue him in the upcoming transfer window. That being said like mentioned previously in the article, football is chaotic, and anything can happen.
Assuming that Barcelona does end up signing Haaland in the summer, Antoine Griezmann will most definitely have to leave (due to wages and the money that can be generated from his sale). Haaland’s current value is £99 million, and so Barcelona’s total squad value for next season, after being corrected for Griezmann’s exit and Haaland’s arrival is £831.60 million.
Haaland’s addition can put 21/22 Barcelona on level with the previous two Spanish champions in terms of total points won. The difference in points between a squad containing Haaland and one not containing him is only two. However, an argument about whether signing Haaland is worth it, is not in the scope of this article.
“Transition seasons do not exist at Barcelona.”Joan Laporta | Presidential campaign
A club of Barcelona’s stature will always compete for titles. The machine learning model shows that Barcelona is in contention for the 2021/22 La Liga title. Ronald Koeman has done a splendid job at the Blaugrana club this season, and there is no logical reason for him to not do an even better job next season after getting fresh players in the summer.
While football at its core is unpredictable, statistically, it would not hurt to trust Barcelona to win the 2021/22 La Liga title.