Influence of Age on the Manner of Playing Basketball - Case Study Example

s: Effect of age on basketball players’ performance Introduction For the past three years, I have been very interested in basketball. One of my favourite players is Reggie Miller who was playing for Indiana. The idea of my project is to compare top two 3-point shooters in the history of basketball. The two players that I will use in my analysis are Reggie Miller and Ray Allen. 1.1 Statement of task and methodology In my project I want to see how age affects the precision and way of playing as well as find out if age affected Reggie Miller and Ray Allen differently. To achieve this, correlations between age and my indicator variables will be computed. I will see if correlation positive or negative or there is no correlation at all, and from that I will conclude how does the age affect this type of shots. I will also support my results with scatter plots showing how these indicator variables change with age. 1.2 Data The data I used in my analysis is secondary data which was got from a web site. It had all the variables I needed about my players. I believe this data is reliable (Sports Reference LLC). Below is a list of variables considered in the analysis. Variable Type Age Continuous Field goals Continuous Field goal attempts Continuous 3 points Continuous 3 point attempts Continuous Steals Continuous Personal fouls Continuous 2 Results 2.1 Calculated correlation between age and field goals for Ray Allen Correlation between age and field goals obtained using Excel is -0.498. This correlation is negative and moderate. This means that as age increases, the numbers of field goals reduce. So there is a negative association. From the above scatter plot, we can see that the number of field goals reduce as age increases. This means that the older Ray Allen got the less field goals he scored. This result is supported by the calculated correlation between the two. 2.2 Calculated correlation between age and field goals for Reggie Miller Correlation between age and field goals obtained using excel is 0.068. This correlation is positive though low. This means that as age increases, the numbers of field goals increase. From the above scatter plot, it can be seen that the number of field goals scored improved slightly as the player got older. This means that the older Reggie Miller got, the more field goals he scored. This result is supported by the calculated correlation between the two. Comment From the results obtained for both players, I note that the number of field goals by Reggie Miller increased as he got older which was not the case for Ray Allen. This can also be seen in a plot below comparing field goals scored by each at a certain age. From the plot above, it’s clear that the number of field goals scored by each player at certain age is different. At ages 22, 29 and 30 Reggie Miller scored more goals than Ray Allen. 2.3 Calculated correlation between age and field goal attempts for Ray Allen Correlation between age and field goal attempts obtained using excel is -0.366. This correlation is negative and moderate. This means that as age increases, the numbers of field goal attempts reduce. From the above scatter plot, we can see that the number of field goal attempts reduce as age increases. This means that the older Ray Allen got the less field goals he attempted to score. Similar trend was observed with the field goals scored 2.4 Calculated correlation between age and field goal attempts for Reggie Miller Correlation between age and field goal attempts obtained using Excel is -0.012. This correlation is negative and low. This means that as age increases, the numbers of field goal attempts reduce slightly. The scatter plot above shows that as age increases, the number of 3 points decrease and the increase. Overall there is no much difference that can be seen from this plot. Comment For both players, the field goal attempts reduce slightly as they get older. This is shown by the weak negative correlations between age and field goal attempts. 2.5 Calculated correlation between age and 3 point for Ray Allen Correlation between age and field goal attempts obtained using Excel is 0.218. This correlation is positive and low. This means that as age increases, the number of 3 points increase though the correlation is weak. The scatter plot above shows that as age increases, the number of 3 points increase though the positive linear trend is not strong. 2.6 Calculated correlation between age and 3 point for Reggie Miller Correlation between age and field goal attempts obtained using Excel is 0.343. This correlation is positive and low. This means that as age increases, the number of 3 points increase though the correlation is weak. A weak positive linear relation can be seen from the scatter plot above. This implies that as age increases, the number of 3points goes up as well. Comment Both players show a slight improvement in the number of 3 points as they get older. This can be seen from the scatter plots as well as the positive correlations between age and 3 points. 2.7 Calculated correlation between age and 3 point attempts for Ray Allen Correlation between age and field goal attempts obtained using Excel is 0.308. This correlation is positive and low. This means that as age increases, the number of 3 points attempts increase though the correlation is weak. 2.8 Calculated correlation between age and 3 point attempts for Reggie Miller. Correlation between age and field goal attempts is 0.356. This correlation is positive and low. This means that as age increases, the number of 3 points increase though the correlation is weak. Comment From the two scatter plots and calculated correlations, it can be concluded that the field goals attempts increased as the players got older. This could be as a result of experience they gained over the years. 2.9 Steals and fouls for Ray Allen From the bar chart above, we can see that at every age, the number of steals is lower than the number of personal fouls. On average the number of steals and personal fouls reduce with age though the trend is not so obvious. It’s clear that the steals and personal fouls were highest between ages 23 and 29. 2.10 Steals and fouls for Reggie miller From the bar chart above, we can see that at every age, the number of personal fouls is higher than the number of steals. On average the number of steals and personal fouls reduce with age though the trend is not so obvious like in Ray’s case. It’s clear that the steals and personal fouls were highest between ages 21and 25. 3 Conclusion From the results obtained above, it’s clear that age has an effect on performance of a player. Some effects are positive while others negative. For most indicators, both players showed the same direction of effect apart from age versus field goals where it was noted that the number of field goals by Reggie Miller increased as he got older which was not the case for Ray Allen. Even if some comparisons between the players can be made, they might not be reliable since the two players played in different conditions and periods. A more valid comparison can be made where all factors that can affect performance are similar for the players. Reference Sports Reference LLC. Basketball-reference.com. 07 11 2011 г. web. 07 11 2011 г. Read More