However, Roberto went on to join Palmeiras and did not retire until Since retiring from professional soccer, Roberto serves as a technical advisor to Palmeiras.
Marco Ballotta is the second oldest Italian player ever whose career spanned over 25 years. Ballotta appeared in Serie A games and mostly played for the football club, Lazio. In , Ballotta, at age 43, became the oldest player to play in a Champions League match, beating the old record held by Alessandro Costacurta who was Ballotta retired from his professional career in after his contract with Lazio expired, despite expressing an interest in continuing to play.
Although Marco Ballotta had officially retired from professional soccer, in late , he joined Prima Categoria, an Italian amateur league, as a forward despite playing as a goalkeeper for his entire professional career. He is also one of the oldest soccer players ever as he retired at age He played for different teams in the S.
Over his nearly thirty year career, Burridge played in league games in the English and Scottish leagues. From — , Burridge continued to play as a goalkeeper for any team that requested his services, despite being in his 40s. During this time, he usually only played one or two games with these football clubs as an emergency goalkeeper. When Burridge played for Manchester City in , he became the oldest player to appear in the Premier League at age Of course a month-long tournament is different from a regular league season that typically spans 10 months and, likely, so are the determinants of performance.
Attackers tend to peak earlier than defenders but, interestingly, he also argues peak performance can last a while, until about age 31 Kuper, Caley analyzed minutes played in the Premier League as a proxy for performance and concluded that peak age likely occurs between the ages of 24 and 28, with attacking players likely to peak at Using a similar approach, Gleave labels players into three age categories: Talents younger than 24 , peak age 24—29 and veterans older than Chris Anderson and David Sally, writing on their blog, Soccer by the Numbers, analyzed transfer market value of all players in the Premier League in October using regression analysis.
Their results showed that an inverted-U curve characterizes the relationship between market valuation and age, with peak value occurring at age 26 Anderson and Sally, There are several reasons why, for analysts, the age-performance profile of players may be difficult to map, possibly explaining the dearth of systematic evidence on the issue.
As noted by many observers, soccer is the quintessential team game that lacks clear and quantifiable dimensions of individual performance, which are typically present in other sports such as baseball, tennis or even basketball Anderson and Sally, ; Simmons, Second, whereas clubs track data on some measures of physical performance e. In recent years match-day data on these and other variables are becoming increasingly available for analysts and the public, but the extent to which they can be aggregated and utilized to measure longer-term performance by players is yet to be seen.
Knowledge of optimal age in soccer is intrinsically valuable from the perspective of performance analysis. However, it also has significant value for the business of soccer. Player contract is especially important in soccer because, unlike in other sports e. This paper aims to shed light on the optimal or peak age in professional soccer.
For the analysis, it uses performance ratings of players who played in the top four European leagues during the last five seasons, to The ratings come from WhoScored. The estimation exercise adopts a panel fixed effects model, which utilizes the longitudinal variation in age and performance to determine optimal age.
Separate models are estimated for each outfield position defence, midfield and forward. Additional robustness exercises estimate individualized age-performance curves and test for potential variation in peak age by ability. The rest of the paper is organized as follows. The next section describes the data. Section 3 presents preliminary estimates of peak age using distributional and bivariate methods.
Section 4 discusses the main fixed effects model and results. Further results are presented in Section 5. Section 6 contextualizes the results in the broader literature on age and performance in sports and the final section concludes. The data for the study come from the increasingly influential soccer statistics website WhoScored. For the top leagues around the world, the site obtains its raw statistics from Opta and presents them in an accessible, publicly available platform.
Here is how WhoScored. Every event of importance is taken into account, with a positive or negative effect on ratings weighted in relation to its area on the pitch and its outcome. According to the ratings scale, a rating of 6.
Ratings of 5. While ratings are updated live during a match, the final rating is determined after full time, taking into account match outcomes and any necessary adjustments and corrections in statistics.
The use of an algorithmic rating such as WhoScored. First, as noted the rating gives a composite measure of performance, taking into account all contributions, positive and negative, of a player. It can be argued that no other single statistic commonly reported in the sport sufficiently captures overall performance, even for a homogenous group of players.
Lack of an adequate proxy for performance is even more of an issue for defenders and, particularly, midfielders whose roles are intrinsically multi-faceted. Second, although the weighting of outcomes underlying an algorithm inevitably involves some value judgment, the overall rating itself is objective in the sense that it is applied consistently across players and over time. We prefer average rating because it likely smoothes out fluctuations in performance that occur during the course of a season — due to individual and team form, injury, luck, and various other factors — and thereby gives a truer measure of sustained performance.
It is thus measured on a continuous scale. For instance, a player born on October 26, is In instances where age will be treated as a discrete or categorical variable, the computed age is rounded off to the nearest whole number.
The initial sample consisted of all outfield players in the four leagues who registered positive minutes in any of the five seasons under consideration.
The data are stacked in panel form each player across years. Players are then classified into three groups based on their field position: Forwards, Midfielders and Defenders.
We exclude observations player-year with less than minutes of total playing time during a season roughly the equivalent of 3 full games. This is done to minimize potential bias arising from including fringe players who nonetheless receive a rating. For example, a player that is just breaking through sometimes shines — and receives a high rating — with minimal minutes, but it is difficult to assume he would maintain that level of performance over extended playing time over a season.
The threshold of minutes provided an acceptable balance between the competing goals of removing outlier observations and minimizing the loss of unduly large number of observations. We also dropped entries when a player is younger than 18 after rounding off or older than It is reasonable to assume that those who manage to play sustained minutes in a competitive European league at these ages are outliers.
After these constructions, the final sample consisted of observations on players. The breakdown comprised of observations in the sample for forwards, for midfielders and for defenders. The panel data for each group was unbalanced because not all players had entries for each of the five seasons.
Table 1 presents summary statistics on WS rating and age for each group. Before we present and estimate a formal statistical model of the age-performance relationship, we begin with two relatively simple methods of identifying peak age: Age distribution and bivariate analyses.
The premise is simple: If most players, including those with marginal ability, play professionally when they are at their highest performance level, then the modal age — the age at which most players participate — also happens to be the peak age. Figures 1a to 1c present the histogram age distribution for forwards, midfielders and defenders. The modal age for forwards happens at But near peak frequencies — contributions of 8 percent or above — are observed 2 years either side of the peak.
There is a substantial drop-off at 29, albeit followed by a slight recovery at Midfielders realize a very similar pattern as forwards — a peak at 26 and near peak frequencies between 24 and The percentage contribution to the age distribution in these years ranges from 8.
But the decline in participation after the drop-off at 29 is more uniform for midfielders. Defenders, meanwhile, have their modal age a year later, at 27, and near peak frequencies for a longer period 24—29 years. The decline later on is also visibly more gradual.
Bivariate approaches to estimating peak age simply plot a measure of performance against age. Two types of bivariate analysis are employed here. In the first playing time is used to proxy performance. The second uses WS rating. The use of playing time as a proxy for performance follows a similar logic. If, at any given time, coaches or managers pick players purely on the basis of performance, then the players who are given the most minutes are the best performers.
The age at which the most minutes are registered is thus the peak age. Figures 2a to 2c plot the ratio of total minutes by age for forwards, midfielders and defenders, respectively. Age is again rounded off to the nearest integer and the ratio for a given age is calculated by dividing the total minutes of players of that age by the total minutes of all players. For example, for 23 year-old forwards, the ratio represents the minutes played by all 23 year-old forwards to total minutes played by all forwards.
In addition to the simple plot, the best cubic fit line is also shown. As can be seen from Fig. However, near-peak ratios, again conveniently defined as contributions of 8 percent or more, are observed between the ages of 24 and In Fig. Interestingly, the plot for midfielders has a sharper peak, signifying that during the peak years, midfielders are given a relatively higher proportion of playing time than forwards.
Another routinely used method defines peak age as the one when average performance, computed over the available sample, is highest. Accordingly, we calculate mean WS rating by age, for ages 18 to Figures 3a to 3c plot average performance by age for forwards, midfielders and defenders, respectively. When possible, the best quadratic or cubic fit line is also shown. According to this method, forwards technically peak at age 27 — that is when computed average performance is highest — but they maintain near peak performance roughly from 21 to A cubic function fits the average rating — age relationship best with a highly significant cubic term and R 2 of 0.
The predicted peak age from the cubic fit is 25 years. Midfielders meanwhile hit the literal peak at 29 years, although near peak performance, even when conservatively defined as average WS rating of 6.
For Defenders, however, the bivariate approach fails to show any discernible pattern of improvement and decline in performance in the normal career years Fig. A regression of average performance on age yields statistically insignificant estimates whether in a cubic or quadratic polynomial.
This result, as well as a careful look at the implied average performance of forwards and midfielders during the early and late career years in Figs. It suffers from the problem of selection bias. Entry and exit from professional sports is non-random. Very good players start earlier and quit later than average or weak players. This means average computed performances during the early and later career years are biased upward due to the prevalence of players of very high ability in the sample.
In turn, this dulls the age-performance curvature — that is, it masks the improvement and decline in performance in the pre- and post-peak years, respectively. In summary, the age distribution and bivariate approaches, while simple to implement and informative, can in fact lead to biased estimates of the age-performance curve and peak age.
The aspirants who wish to know more about the sport of football can refer to the below-mentioned books and study material-. Is being a soccer player a good career? Ans : Yes, it is a well-paid sport. The aspirants with an interest in the sport can pursue football as a career. How do you pursue a football career? Ans : To make a career in football, the aspirants must acquire a degree in sports and go for professional training in the sport. What age is too late to be a footballer?
Ans : Age is not the limit. However, you must be a minimum of 16 years old to sign a professional contract. Can football be a career? Ans : Yes, with the increasing scope of football in India, one can pursue it as a career.
Can I start playing football at 15? Ans : Yes, one can start playing football at the age of Aspirants can also join football academies for professional training. How many hours a day do footballers train? Ans : On average, a soccer teams practices for 4 — 6 hours a day for 5 days a week.
Ans : Soccer players normally retire at the age of What factors determine the salary of a football player?
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Stay tuned to embibe. Support: support embibe. General: info embibe. Skip to primary navigation Skip to main content Skip to primary sidebar Skip to footer Career Career. Career in Football in India — Check Courses, Scope Career in Football, also known as soccer, is now a multi-billion-pound global industry, which means there are more football-related jobs than ever before. Career in Football: Educational Requirements To become a professional player, one only requires stamina and ambition.
Experience and training in the sport will increase the chances of selection. The player must complete four years of college and graduate with a degree of their choice to enter the NFL draft.
Instead, it takes a lot of dedication and patience to be at the peak. Of course, you can play soccer for fun, but if you want to be a professional, you have to go through the developmental stages to reach the top.
In short, for you to be a professional soccer player, you have to go through the developmental stages. Does supervised coaching and training have a place in being a professional soccer player? Yes, it does. This brings us back to the point on the development of young players up to their professional level. Coaches understand that this sport requires consistent hours of training and dedication for one to become a pro.
Supervised coaching comes in handy in developing a player through the ranks. If you do your research on the best players in the world at the moment, one thing that they share in common is that they started their professional development at a young age.
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