CFrench - MAT 243 Project One Summary Report

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Apr 3, 2024

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MAT 243 Project One Summary Report Christian French christian.french@snhu.edu Southern New Hampshire University

1. Introduction: Problem Statement The reason for this report is to analyze the historical data for basketball teams and find and analyze performance patterns, as well as distributions of key performances. This analyzation is set to aid the management in making improvements to their team. The two data sets I will be analyzing is the Nuggets from 2013 to 2015, and the Bulls from 1996 to 1998. Both of whom will have their own unique data charts, averages, and confidence intervals. I will be using data visualization and confidence intervals to calculate the average skill of both teams in their respective years. 2. Introduction: Your Team and the Assigned Team The team I was chose was the Nuggets, years 2013-2015, and the team assigned to me by management was the Bulls, years 1996-1998. Table 1. Information on the Teams Name of Team Assigned Years 1. Yours Team Nuggets 2013- 2015 2. Assigned Team Bulls 1996-1998 3. Data Visualization: Points Scored by Your Team Data visualization is used to study data distributions and trends by giving us a visual way to not only understand, but also see trends, outliers, and patterns within the data that is presented. Data visualization puts all of this into an image that is more easil digestible and understandable. When it comes to the visualization for points scored by my team, the Nuggets, from 2013 to 2015, I chose the bar graph over the scatter plot. The goal of this visualization is to accurately show the information presented on the graph. With the scatterplot, the points are lined in three spots, with two extra years added on to each side, making the dots clustered and hard to read. Between around 90-120, the points are clustered to the point where its hard to tell the exact number of points chosen. The coach wanted a distribution, and I believe the

bar graph shows that with a precision that is needed. The bar graph showcases the number of points received and how often they had them, giving him a better idea of how they perform as a whole over the course of three years instead of struggling to see an average or the exact numbers in the scatter plot. For instance, they scored 100 points most often, with scoring 100 points with a frequency of 35, and had an average of 110 and 115 points with a frequency of 30. This is able to show the visualization of their averages and how often they perform on a better level. 4. Data Visualization: Points Scored by the Assigned Team I chose the same graph for the Bulls for the same reason. Being able to see the same charts with comparative data is the best way to showcase the average of both teams and showcase how they compare. For instance, the Bulls scored 105 points with a frequency of 35, and when comparing the two, you can see their highest frequency has a difference. 5. Data Visualization: Comparing the Two Teams For the comparison of the two teams, I chose the box plot because I feel like it’s a better comparative than the histogram is. The histogram overlays the two and, due to the fact that on around one half of the graph the Nuggets are performing better and the Bulls perform better on the other half and it can cause confusion over who truly performs better. With the boxplot, there is a clear distinction between the two teams where you can see that the Nuggets performed better, even if it was by a small margin. With the boxplot, you can see the three outliers the Nuggets have, two being over 135 mark, and one under the 70 mark, while the Bulls have no outliers. On top of that, you can see the three point average between the variance and mean of their overall points. With the boxplot, you are able to more clearly see the difference while having all of the information about the two teams’ performances presented clearly.

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