Reversed ageism in soccer penalties

Reversed ageism in soccer penalties

Sacha Pel 10821821

University of Amsterdam FEB

Economics and Business 26th _{of June, 2018}

Statement of Originality

This document is written by Student Sacha Pel who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

Index

2.4 Choking under pressure ...9

2.5 Performance differences in Age categories ...9

2.6 Experience ... 10

3. Methodology ... 12

Hypothesis 1. ... 12

Hypothesis 2 ... 13

Experience (Control variable) ... 13

Combining ‘Performance’ and ‘Chosen’ ... 14

Last Penalty ... 14

Data ... 14

4. Results ... 15

Hypothesis 1: ... 15

Hypothesis 2: ... 16

Experience (Control variable) ... 20

The last penalty (Control variable) ... 21

5. Discussion ... 22

Bibliography ... 24

1. Introduction

In the currently ongoing World Cup 2018 in Russia, the most penalties since 1966 were taken per game. So far, nine penalties have been given in the opening rounds of the tournament and the average number of penalties per game is 0,56. This comes down to approximately one penalty every other game, while in the world cups of 2006 and 2010 the average number of penalties was 0,06 per game (Rumsby, 2008). This increase in the number of penalties per game makes penalties a bigger influencer of the outcome of a game and therefore the more interesting to research what factors influence the outcome of a penalty.

Professional soccer players all have exceptional soccer skills, among which the placement of the ball. This leads to the assumption that, generally, every professional soccer player would be able to score a penalty when lucky enough that the goal keeper jumps in the wrong corner. “It’s a battle of the mind. When I missed in one game, it was my responsibility that everyone went home miserable that evening. It took a while to get over that. Penalty shots don’t get any easier just because you have taken lots.” – Alan shearer (John DeBenedictis, 2013). So, when a player misses, it is very likely that the reason behind this isn’t that the player is less qualified, but that the stress has gotten the overhand and that the player choked under the pressure.

Jordet, Hartman, Visscher & Lemmink (2007) conducted a research on the effects of stress, skill and fatigue on penalty kick outcomes with age as a control variable. In their outcomes they found that younger players get to take a lot less penalties (14,9%) then medium or older players (58,9% and 25,7%) while their scoring percentages are higher. This is a contradicting finding, which could indicate that there could be a selection bias based on age within the soccer world.

There has been multiple research on older people being treated differently in a negative way because of their age outside the soccer context (Raymer, Reed, Spiegel & Purvanova, 2017, and Kite, Stockdale, Whitley & Johnson, 2005). This type of discrimination goes by the definition of ‘Ageism’. Do young people get less opportunities because of their age and the prejudices that come with it? A common topic is the difficulty that young individuals have with finding jobs in the current, though job market (Conway, 2016). This research discusses the question whether reversed ageism also plays a role within the soccer world. I will investigate the

relationship between age and the choosing of a particular age group. And to check if this preference is justifiable I will look at the correlation between age and performance.

Despite the interesting outcomes of Jordet et al (2007) mentioned above, this is never further analyzed. This research goes more in depth into whether younger players indeed get less opportunities than older players and I will analyze the relationship between age and performance to consider if the possible difference in selection could be explained by performance. A one sample T-test and logistic regression analyses are conducted in order to answer these questions. Data from 2008-2018 are analyzed of all penalties taken in the national competitions of The Netherlands, Germany, Spain, Italy and England.

Answering this question can be very helpful for coaches is particular, because they might not be aware of the difference between age groups in dealing with stressful situations like penalty kicks or they might not be aware of their selection bias against younger players. Furthermore, it contributes to the growing literature on reversed ageism which can eventually take concrete forms, like already has happened for ageism against older individuals.

The second section consists of previous conducted research and theories that form the basis on which this research builds. The third section is the methodology, which entails details about the data and variables used and which analyzes are used. In the fourth section the results of the previously explained analyzes can be found. The fifth and last section contains a discussion where the overall conclusion is presented, possible explanations for this conclusion are given and the limitations of the research are mentioned.

2. Theoretical Framework

Since this research discusses the events of penalty kicks, its definition is an important starting point. There are two types of kicks which are taken in different situations; penalties and shootouts. FIFA defines a penalty kick as follows: “A penalty kick is awarded against a team that commits one of the ten offences for which a direct free kick is awarded, inside its own penalty area and while the ball is in play. A goal may be scored directly from a penalty kick.”. The player taking the penalty doesn’t have to be the one against whom the offence was made (Law 14 - The penalty kick, 2011). The other type of kick is conducted when a game ends and it’s still a tie after extra time. In this case, shootouts will be given (Jordet, Elferink-Gemser, Lemmink, & Visscher, 2006).

When taking a penalty, the keeper has to decide which way to jump, or if he’ll stay in the middle, before the penalty taker has kicked the ball. If the keeper waits until the penalty taker has kicked, he will be too late to catch the ball even if he jumps to the right corner, due to the high speed of the ball. Therefore, the keeper will always have to guess.

During the penalty, the ball has to lay completely still on the penalty mark. When the other players have taken their position, at least 9.15 meters away from the penalty mark, outside the penalty area but within the playing field, the referee blows the whistle. After this the kicker, who must be clearly identifiable, is allowed to take the kick. The goalkeeper is not allowed to move outside the goalposts before the ball is kicked. The penalty is taken when the ball is hit and it clearly moves. The penalty is over when the ball is no longer moving, is outside the playing field or when the referee stops the game due to an offence. A game must be extended when a penalty is taken before the end of a half game or at the end of the complete game (KNVB, 2017).

Penalties are seen as a relatively easy way to score a goal. This creates the expectation of that the penalty will probably lead to a goal, which increases the pressure since failing would lead to extra disappointment of both the supporters and the team (Bakker, Oudejans, Binsch, & Van der Kamp, 2006).

Jordet et al (2007) show that players who take penalty kicks in major international tournaments are more likely to choke under pressure than players in soccer matches that are played within smaller, less significant, tournaments. This provides evidence for the theory about the higher the pressure on the players, the harder it becomes to perform well and the more important it is to have players who can perform well under pressure. Another important factor that possibly influences the pressure is the importance of the kick. When the shot immediately determines a loss, and therefore is of higher importance than when it doesn’t, the players perform significantly worse than when the shot is neutral or immediately determines the win (Jordet & Hartman, 2008). Another, perhaps surprising finding, is that of Wallace, Baumeister and Vohs (2005). Their research showed that playing at the home turf increases the likeliness of choking under pressure.

The coach selects the kicker based on who he thinks has the best chance to score the goal. It is very likely to be a biased decision since this decision is based on an opinion. To find out whether or not there is a bias in the decisions coaches make, it is necessary to see if the specific player chosen to take the penalty is indeed the best choice, meaning he is most likely to handle the pressure best.

2.2 Ageism

The traditional view on ageism is that older adults experience negative attitudes and are discriminated (Raymer et al, 2017). Older people are viewed in a negative way of being old fashioned, inactive and forgetful. This can lead to negative behavior on the work floor, called ageism (Kite et al, 2005).

The discrimination of older individuals on the work floor is a much disgusted topic. Serious measures are taken to protect older individuals from this ageism. A clear example of this is the formation of ‘The Age Discrimination in Employment Act’, signed by President Johnson on 5 December, 1967. This is an American Employment act that protects applicants and employees of 40 years or older from discrimination on basis of their age in the following activities: Hiring, promoting, discharging, compensation, terms, conditions and privileges of employment (Landy, 2005)

2.3 Reversed Ageism

Even though the importance of discrimination of older generations is considered of large importance, younger generations are more often sharing their experiences with age discrimination against younger individuals. Findings about younger people being unemployed more often and older coworkers confirming that they take younger people less seriously, back this up (Kessler, Mickelson, & Williams, 1999). Nevertheless, no measures have yet been taken to protect younger people from reversed ageism.

Reversed ageism is often due to the traditional beliefs that older people are wiser and more experienced (Ayalon & Toch, 2013), better leaders (Collins, Hair, & Rocco, 2009) and more reliable (Holt, Marques, & Way, 2012). Research shows that younger individuals experience age discrimination just as much, if not more, than older individuals (Kessler et al, 1999). In soccer the age distribution is more densed than in other industries, soccer players generally retire on the average age of 35 (Bailey,

2014). Therefore, the age categories within soccer are different then in most industries,

but the principle of different age groups within a group of colleagues is the same. In soccer it is likely that ‘middle aged’ and older players, who are considered to have more experience, are chosen to take the penalty kick. Jordet et al (2007) found that most players taking a kick were between 23 and 28, the younger players (18-22) were chosen least, leaving the oldest players (29-35) in the middle. The youngest generation however, scored relatively more than both the middle and the older generation. This finding was not significant with a ‘N’ of 61 young players, 241 middle aged players and 105 older players.

Even though this finding is not significant, the trend is against the expectations of older people performing better (Ayalon & Toch, 2013). Considering this, it isn’t logical that the youngest players get chosing less than the older players, which gives reason to believe that there might be a (reversed) bias in the selection process of the penalty kicks. Therefore the following hypothesis is tested in order to get clearity on whether or not younger player indeed get chosen less often than older players to take the penalty kicks.

H1: Younger players get chosen less often than (middle and) older players to take penalties

2.4 Choking under pressure

Choking under pressure is an expression used when the performance decreases while the athlete does the best he can. ‘Choking’ refers to the performance decrements when pressure is high. ‘Pressure’ is defined as one or multiple factors that cause an increase in the importance of performing well in a certain situation. The athlete is motivated to perform well, has the skill to perform well and he values the situation as highly important but still the performance isn’t optimal (Beilock & Gray, 2007).

Clark, Tofler and Lardon (2005) describe choking as the situation where the athlete is able to think straight, unlike when panicking, but that he cannot execute the performance correct physically because of involuntary muscle contractions and movements. This can be due to the consciously monitoring and controlling the movements that normally aren’t monitored and controlled and this disrupts the automatic movement (Baumeister, 1984). Furthermore, Wegner and Guiliano (1980) found evidence that arousal increases the focus of attention on oneself. This self-awareness can interfere with the automatic movement, causing a decrease in performance due to overly controlling and monitoring the movements of well-learned skills. In penalty kicks this can lead to a serious decrease in performance, since taking a penalty is a well-learned skill and the focus of the kicker shouldn’t be on the specific movements the body makes when kicking the ball but more on general, larger thoughts like on which side of the goals to aim.

2.5 Performance differences in Age categories

Aldwin, Sutton, Chiara & Spiro (1996) performed a study on age differences in stress, coping and appraisal. They used a coping checklist and they did semi structural interviews on three different age groups of men; young, middle-aged and old. They didn’t find any difference in how people of different ages perceive the stressfulness of a situation, how the importance of the harm or loss is perceived, how many emotions were reported and lastly their coping efficiency. This could indicate that there are no differences in the choking under pressure of soccer players when taking a penalty kick

and this would mean that the overall performance of different age categories should be equal.

Baumann, Friehe & Wedow (2011) discuss in their article that specialization of the kicker in one side of the goal proves to be more succesfull. While it is expected that specialization, and therefore predictability, is bad in this situation, it is possible that the increased performance of the kicker when specializing in one side will compensate for the negative effects of predictability. This increase in performance is possibly due to the possibility for the kicker to have a standard routine before the kick and doing everything automatic instead of having to think about which corner to aim at. It is possible that older players feel more pressure to switch it up instead of being ‘too predictable’, while younger players like to play it save and focus on one side of the goal that feels right to them. This would lead to a higher performance in the younger age categories.

Furthermore, Molander & Backman (1996) did a study on the performance difference between young and old adults in competitions versus under training conditions. They found that younger players perform equally well or better under competitive conditions than under training conditions, while older players perform worse in competition than under training circumstances. This could indicate that younger players are less prone to stress and/or that they perform better in complex motor tasks under high pressure conditions. And therefore, it is unlikely that there will be a difference in performance between younger and older players.

H2: Younger players perform better than older player in penalties

2.6 Experience

Experience is the most important control variable when looking at age difference. In the case of penalty kicks, there is a lot of discussion on whether or not the taking of a penalty kick is trainable. Navarro, Miyamotoa, van der Kamp, Morya, Savelsbergh and Ranvaud (2013) did a study where participants simulated penalty kicks. Before and after task-specific practice, participants were tested on the same task both under low- and high-pressure conditions. They found that task-specific practice does

increased resilience to high-pressure, but that the effect was a function of how the participants responded initially to high-pressure.

On the contrary, Dawson (2012) performed a quantitative research in which they found evidence that social pressure does influence the referee behavior and that a similar effect appears on both experience and inexperienced referees. Therefore, this article creates doubt whether or not experience actually leads to better performance.

This leads to the conclusion that it is possible that experience plays a role in the taking of a penalty kick and that this influences the relationship between age and performance.

3. Methodology

This is a quantitative research with data collected from national competition in male soccer games between 2008 and 2018. The data is divided into two datasets. Dataset 2 consists of 4784 penalties in five national competitions in Europe; The Netherlands, England, Italy, Spain and Germany taken by 1390 players in total. The dataset was collected between 2017 and 2018. Dataset 1 consists of the first penalty of every season from 08/09 to 13/14 for all five competitions mentioned before with the age of the kicker. Furthermore, Dataset 1 contains all the ages of the players that were in the team at the moment of the penalties.

Important to mention is that when a difference is found in performance, I assume that this difference comes from how well players deal with pressure, because I work under the assumption that in a calm situation, every professional soccer player can score a penalty.

By employing quantitative modes of enquiry, I attempt to shed light on the question whether there is reversed ageism in the selection of soccer penalty shooters. Three main variables are taken into account; Age, Experience (the number of taken penalties) and lastly whether the penalty was scored or not. Experience is the main control variable, but besides that the following variables are taken into account: Minute, Audience size, Home/away game and the Score difference.

Hypothesis 1.

(Younger players get chosen less often than older players to take penalties)

First, I looked at the relationship between ‘Age’ and ‘Taken Penalties’. The amount of taken penalties per age group was analyzed to see if a certain group takes more penalties than others. This will show if coaches are biased with regard to age when choosing the penalty takers.

Next, Dataset 1 was used to perform a one sample t-test. This test analyzes whether or not there is a significant difference between the average age of the team and the average age of the kicker to see if kickers were generally older than the average age of the team. In an effort to objectify the age categories of the kickers I used the age categories that Jordet et al (2007) used in their research on variables that play a role in

Age Cat 1 (Young): Younger than 23 years Age Cat 2 (Medium): Between 23 and 28 years Age Cat 3 (Old): Older than 28 years

Hypothesis 2

(Younger players perform better than older players in penalties)

First, the correlations between all the variables, including the control variables, were analyzed to get a first impression of the relationship between the variables. The same age categories as mentioned above, that of Jordet et al (2007), were used. This analysis used the independent variable Age and the bivariate, dependent variable Score with as outcome ‘goal/ missed’. Next, the logistic regression with a total of three different models was conducted. The first model considered Age as the only variable in the regression to analyze its relationship with Score. In the second model, Experience was included as well and in the third model all control variables were taken into account. Experience (Control variable)

It is very likely that experience plays a controlling role in the relationship between Age and Performance. This is because professional soccer players all start playing soccer at a young age and they go through approximately the same path when it comes to experiences. A 19-year-old professional soccer player can (almost) never have more experience when it comes to soccer than a 29-year-old professional soccer player. Therefore, it is very likely that age and experience are closely intertwined. Experience was measured by counting the amount of penalties the kicker took in Dataset 2, before the current penalty. Experience is divided in three categories in order to get a clear distinction between players who are not experienced, fairly experienced and very experienced. The three categories are divided as follows:

Category 1 (Not experienced): Less than 3 kicks

Category 2 (Fairly experienced): Between 3 and 15 kicks Category 3 (very experienced): More than 15 penalties

Experience was used as a control variable in case the coaches based their decisions on this variable instead of purely the age, which would indicate that reversed ageism has less effect on the decision of the coach than I thought in first instance.

Combining ‘Performance’ and ‘Chosen’

When comparing the results of ‘Performance’ and ‘Chosen’ between the different groups, it will become clear whether the choice of the coaches correlates with the performance of the players. If this is the case, then there is no reason to assume that there might be reversed ageism. But if younger players perform just as well/better than the older players (H1), but they still are chosen less, then that gives reason to assume that reversed ageism could play a role in this relationship.

Last Penalty

The last penalty is another important control variable that has taken a lot of consideration. The Last Penalty does influence the outcome of Experience slightly because of the increased change of a players not being allowed to take another penalty after missing one. Which would cause a lower scoring percentage when experience goes up, because the last penalty is very often a missed one. A possible solution for this is to remove the Last Penalty of every kickers to remove this issue. But when the Last Penalty is taken out, this would mean that a lot of penalties that were missed will be taken out of the dataset, which can create a mis sketch of the data. Therefore, I made the decision to leave the Last penalty in the dataset to keep the amount of scored and missed penalties real.

Data

The age of the kickers was determined by looking up the players in www.transfermarket.nl. The team members and their ages were found on this site as well. Experience is measured by the amount of penalties taken by the kicker at the moment of the penalty in Dataset 2. Performance is determined by whether or not the kick was scored or not.

4. Results

In this section all the results of the analyses will be discussed. The two hypotheses were tested by two different statistical tests; one sample t-test and one logistic regression. The results from these analyses form the basis of the conclusion of whether the hypothesizes hold.

Hypothesis 1:

(Younger players get chosen less often than (middle and) older players to take penalties)

The first hypothesis tries to find whether younger players get chosen less often than (medium and) older players to take the penalty kicks. First, to get a general overview, table 1 gives the percentages of the amount of taken penalties per age category. It shows that younger players take a lot less penalties than other age categories and that the middle group takes the most.

Table 1. Taken penalties per age category

Age Cat Amount of Penalties taken % of penalties taken

Young 905 19%

Medium 2375 49,7%

Old 1494 31,3%

Next, a one sample t-test was used to analyze if the average age of the team and the average age of the kicker differ significantly. The average ages were both determined by using Dataset 1. As mentioned above, this data was collected from the teams of the first kicks of the season for the years 08/09 to 13/14 for all five competitions. This led to thirty teams (therefore thirty kickers) and 942 players team members in total.

In order to perform this test, the independent variable Age was divided in categories. As mentioned in the methodology section, the distinction between young, medium and old players was based on the age categories of Jordet et al (2007).

The one sample t-test tested for a significant difference between the average ages of the whole team versus the average age of the kickers. The test results showed that the average age of the whole team was 24,26 (N=942, Std. Dev.=4,496) while the

average age of the kickers was 26,20 (N=30, Std. Dev.= 3,671)1_{. This is a significant}

difference of 1,940 (p=0,007). Hypothesis 2:

(Younger people perform better than older players in penalty kicks)

Since the start of the soccer season 2008/2009, 4778 penalties were taken by 1390 players in the five largest national competitions in Europe. In total 1113 kicks were missed (23,29%) and 3665 kicks were scored (76,71%). The average age of the kickers who’ve missed their penalty is 26,30 years old and the average age of the kickers that did score is 26,34 years old, which is a nihil difference.

Hypothesis 2 analyzed if younger players perform better, meaning that they score more often, than older players. This was tested by a logistic regression with two main variables. The independent variable ‘Age’, which is divided in the same categories as in the first hypothesis, and the dependent variable ‘Performance’, which is a bivariate variable with the outcome ‘scored’ or ‘missed’. Multiple control variables where used to check for other possible influential factors with as most important control variable ‘Experience’. The other used control variables were: Experience, Home (yes/no), Minute, Audience size, Score difference.

Table 2 shows that the scoring percentages of the different age categories are fairly small. The youngest players score least (74,9%) and the medium players most (77,0%), with a difference of 2,1%. The oldest players score 76,8% of the penalties, leaving them in the middle regarding performance.

Table 2. Descriptive table on percentage scored per Age category

Age Category Total

Young Medium Old

Observed 525 2537 1715 4777

Scored 74,9% 77,0%% 76,8% 76,7%

Total 100.0% 100.0% 100.0% 100.0%

Table 3 displays an overview of the Means (M), Standard Deviations (SD), the correlations and their significance of all the variables, including the control variables. This gives a good overview on the relationships between the different variables and to which degree their movements are associated. It provides an initial insight in the hypotheses.

Table 3. Descriptive statistics (Part 1 out of 2)

M SD 1 2 3 4 5 1. Score 0,77 0,423 2. Age; Low 0,1100 0,31291 -0,015 3. Age; Medium 0,5806 0,49352 0,007 -0,091** 4. Age; Old 0,3593 0,47985 0,002 -0,263** -0,881** 5. Experience; Low 0,3826 0,48607 0,024 0,121** 0,076** -0,137** 6. Experience; Middle 0,5303 0,49913 -0,033* -0,0733** -0,024 0,065** -0,836** 7. Experience; High 0,0872 0,28209 0,016 -0,080** -0,088* 0,120** -0,243** 8. Score Difference -0,03 1,285 0,024 0,034* 0,086** -0,070** -0,011 9. Home 2 _{0,61 0,486 0,018 0,023 0,012 0,187** 0,025} 10. Minute 53,15 26,608 -0,014 0,005 -0,013 0,024 -0,014 11. Audience 30129,1 9 18485,247 0,012 -0,022 0,260** 0,107** 0,085**

** Correlation is significant at the 0,01 level (2-tailed) * Correlation is significant at the 0,05 level (2-tailed)

Table 3. Descriptive statistics (Part 2 out of 2) M SD 6 7 8 9 10 11 1. Score 0,77 0,423 2. Age; Young 0,1100 0,31291 3. Age; Medium 0,5806 0,49352 4. Age; Old 0,3593 0,47985 5. Experience; Low 0,3826 0,48607 6. Experience; Middle 0,5303 0,49913 7. Experience; High 0,0872 0,28209 -0,328** 8. Score Difference -0,03 1,285 -0,019 0,054** 9. Home 0,61 0,486 -0,016 -0,014 0,188** 10. Minute 53,15 26,608 -0,010 -0,018 0,007 -0,018 11. Audience 30129,1 9 18485,247 0,026 0,155** 0,099** 0,074** 0,018

** Correlation is significant at the 0,01 level (2-tailed) * Correlation is significant at the 0,05 level (2-tailed)

The relation between Score and AgeYoung is slightly negative. The relationship between Score and AgeMiddle and between Score and AgeOld is slightly positive. This means that younger players perform worse than older and medium aged players. Since the correlation between Score and AgeMedium players is highest, this indicates that AgeMedium players score relatively most. However, this correlation value is very close to zero and not significant, since the p-value is <0,05. The relationship between Experience and Score is positively correlated which indicates that more experience increases the scoring percentage. However, these results aren’t significant either. Overall, there were no significant results found between Score and any other variable. It does show a positive, significant result between age and experience (P<0,01). This

was expected since age and experience are generally closely related. There were significant results found between some of the control variables.

Table 4. Regression results for all models

Model 1 Model 2 Model 3

DV Age Age + Experience All variables

Sig. Exp. (B) Sig. Exp. (B) Sig. Exp. (B)

Age Medium 0,286 1,126 0,437 1,091 ,411 1,096 Age Old 0,361 1,111 0,742 1,040 ,658 1,054 Experience Medium 0,000 1,302 ,000 1,294 Experience High 0,052 1,295 ,083 1,266 Score Difference ,164 1,038 Home ,391 1,063 Minute ,374 ,999 Audience ,821 1,000 Constant 0,000 2,977 0,000 2,648 ,000 2,674

To come to a more concrete conclusion, I’ve continued the analysis in the following logistic regression. Table 4 shows a positive relation between Age and Score with Exp. (B) which are higher than 1. Experience Medium has a significantly positive relation with Experience Low (P=0,000) and Experience High also has a positive relation with Experience Low, but this is not significant (P=0,052). This indicates that players with

medium and high experience are more likely to score than players with lower experience. That Medium Experience seems to perform better in comparison to Low Experience, then High Experience compared to Low Experience, this could indicate that the relation between Score and Experience has a reversed U-shape, meaning that the scoring percentage increases with medium experiences players and slightly drops again when players become very experienced.

Furthermore, Score Difference and Home have a slightly positive relationship with Score. Minute has a negative relationship with Score while Audience Size has a neutral relationship with Score. None of these relationships are significant and thus it cannot be said that Performance is influenced by any of the independent variables. Experience (Control variable)

The most important control variable is ‘Experience’. As shown in table 5, Experience category 1 scored least with a scoring percentage of 77,7%. Experience category 2 had the highest scoring percentage, which was 85,4%. And lastly, Experience category 3 scored 81,1% of the penalties. This is no significant effect (P= 0,162). These results match the previous finding where younger players (who generally have less experience) score less than the older (more experienced) generations and the medium aged players and the medium experienced players are both most efficient when taking penalties (N=4777).

Table 5. Goal percentages per Experience category

Experience Category Total

Unexperienced Fairly experienced Very experienced Observed 1830 2531 416 4777 Scored 77,7% 85,4% 81.1% 81,7% Total 100.0% 100.0% 100.0% 100.0%

The last penalty (Control variable)

As mentioned in the Theoretical Framework, the last penalty is an important control variable that has taken a lot of consideration. Because the decision was made to keep all the Last Penalties, the results of the Correlations and the Logistic Regression without all the Last Penalties aren’t displayed in the Results. To assure the completeness of this research, they are taken into account in the Attachment.

5. Discussion

Do the younger generations on the work floor get less opportunity then the older generations? Ageism, whether against older or younger individuals, is a well-spoken topic and this research tried to find an answer on the question whether coaches make decisions based on age. This decision can be caused by the believe that experience will be beneficial for the performance, while the performance isn’t necessarily dependent on age or it could even have a negative correlation. This research looked at the specific situation of whether soccer coaches are more inclined to choose players who are more experienced (older) to take a penalty kick and whether or not they indeed perform better in the taking of penalty kicks. Penalty kicks are a good example of high pressure situations where the outcome is/feels of great importance. The high sample size increases the reliability of the dataset.

This research did indeed find a significant difference in the amount of taken penalties by the youngest age category of the team and the other age categories. It was found that the youngest players only got to take 19% of the penalties, leaving the other 81% of the penalties for the medium and older aged players. This corresponds to the research of Jordet et al (2007), where results showed that the youngest age group was chosen least as well. Furthermore, the average age of the team is 1,94 years higher than the average age of the kickers, which is a significant difference (p=0,007).

A possible explanation for this difference in amount of taken kicks per age category is that coaches are more inclined to choose older players because of the expected higher performance due to the amount of experience older players (are expected to) have. This points towards reversed ageism. Another important part of this research was to analyze if younger players are more likely to score when taking penalty kicks than older players. There are no significant findings on this topic since the differences in scoring percentages between the age categories were rather small.

However, it was found that the main control variable, Experience, has a significant relation with Score. Medium Experienced players score significantly more than Low Experienced players. And Highly Experienced players score more than Low Experienced players as well, but the latter result is not significant.

Experienced players perform better than the Low Experienced players. This does require more research since the scoring percentages per age category don’t show a large difference, but the scoring percentages of the experience categories do show some deviation. Follow-up research could concern more cases on the deviance of the team members ages, since in this research the N was limited to 30. Furthermore, to go more in depth on the topics discussed in this research, qualitative research on how coaches make their decisions or on how soccer players experience the decision-making process could be a very interesting complement to this field of research.

The research has multiple limitations. The first one is that the Experience Categories might not be ideal. It is possible for example that a player already took many penalties before 08/09 but this is not registered in this dataset. Therefore, a better measurement for Experience could lead to interesting results. Furthermore, the age range (18-37) is relatively limited and females are left out of this sample completely. Therefore, it is doubtable whether this sample is representative for the complete working population.

A variable that isn’t considered in the research but might cause a significant bias is the existence of self-selection when players have ‘High Mental Toughness’ that Thelwell and others (2005) researched. When self-selection plays a considered role within the selection of penalty kicks, the basis of this research, which is a possible selection bias from without the coaches, falls away.

This research can be used in practice by soccer coaches when making a decision on which players to choose for penalty kicks or other relatable situations like basketball coaches and free throws. But it mostly functions as addition to the growing literature on Reversed Ageism. This research found no evidence for discrimination in the taking of penalties in professional soccer. But it is important that research on Reversed Ageism continues, so when results of actual Reversed Ageism are found, action can be taken that changes this discrimination of younger individuals.

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Attachment

The Correlation and Regression results when the Last Penalties are removed from the data.

Table 3. Descriptive statistics (Part 1 out of 2)

M SD 1 2 3 4 5 1. Score 0,81 ,394 2. Age; Low ,11 ,311 -0,016 3. Age; Medium ,53 ,499 0,009 -0,371** 4. Age; Old ,36 ,480 0,001 -0,263** -0,798** 5. Experience; Low ,33 ,471 -0,008 0,108** 0,077** -0,049** 6. Experience; Middle ,57 ,495 0,007 -0,049** -0,004 0,036* -0,808** 7. Experience; High ,10 ,300 0,001 -0,087 -0,114** 0,175** -0,235** 8. Score Difference -0,02 1,285 -0,02 0,035* 0,060** -0,085** -0,040 9. Home 3 _{,62 ,486 0,017 0,022 -0,004 -0,011 -0,014} 10. Minute 52,70 26,608 -0,020 0,016 -0,011 0,001 0,022 11. Audience 30964,0 8 18485,247 0,002 -0,028 0,051** -0,035* -0,127

Table 3. Descriptive statistics (Part 2 out of 2) M SD 6 7 8 9 10 11 1. Score 0,81 ,394 2. Age; Young ,11 ,311 3. Age; Medium ,53 ,499 4. Age; Old ,36 ,480 5. Experience; Low ,33 ,471 6. Experience; Middle ,57 ,495 7. Experience; High ,10 ,300 -0,383** 8. Score Difference -0,02 1,285 -0,004 0,070** 9. Home ,62 ,486 0,008 0,008 0,187** 10. Minute 52,70 26,608 -0,018 -0,006 -0,002 -0,021 11. Audience 30964,0 8 18485,247 -,003 0,204** 0,107 0,085** 0,023

Table 4. Logistic Regression

Model 1 Model 2 Model 3

Included variables

Age Age + Experience All variables

Sig. OR Sig. OR Sig. OR

Age Medium ,321 1,144 ,336 1,140 ,321 1,145 Age Old ,406 1,124 ,447 1,116 ,384 1,134 Experience Medium ,673 1,040 ,734 1,032 Experience High ,849 1,030 ,952 1,010 Score Difference ,170 1,047 Home ,465 1,066 Minute ,000 3,750 ,000 3,676 ,263 ,998 Audience ,899 1,000 Constant 0,000 4,825 0,000 4,839 ,000 3,914