DATA4100
Data Visualisation Software
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Part A:
Business Problem:
The business problem is to review players and team statistics to advise on contracts, sponsorships, and advertising on behalf of the leagues and the respective teams to ensure appropriate strategic business decisions that would see the leagues and teams improve not just on the field but even in business earnings.
Justification
The business problem is justified as data analytics is vital in the modern sporting industry due to its contribution to contract negotiations, sponsorship, and advertisement. This is the case because performance analysis ensures that bright rising stars and potential high performers are achieved, which creates better and more profitable business management returns. In addition, since the system uses data in negotiation and marketing aspects, the consultancy can present information that influences revenue generation.
Part B:
1. TEAM AND PLAYER PERFORMANCE BY SPORT(BASKETBALL)
The chart above represents the total points likely scored by various teams. TUL has the most points, 21.47%, and then PHO with 20.97%. The rest of the points are shared quite equitably over the rest of the teams.
In this pie chart, all the turnovers committed by the various teams have been presented. The lowest rates for turnovers are ranked with WAS (22.08%), and SEA (20.74%). The turnover rates of the other teams are more or less similar to each other.
The bar chart indicates the wins achieved by each team. Currently, the winningest team is PHO followed by SAN and WAS team. TUL and SEA have fewer wins.
The chart provided below depicts the average efficiency and average points of the given players. Tina Charles has the highest efficiency per patron average, while Tiffany Hayes follows. However, Tiffany Hayes gets an average of the highest points and this is followed by Tina Charles. The rest of the players have lower averages of both efficiency and points.
The chart also provides the average goals and the defensive performance of various players. Tina Charles takes the highest average Goal while Tiffany Hayes comes second with the second highest average Goal. But Tina Charles also makes a high average of defensive output and the second highest is Tiffany Hayes. Other players try to have a lower average for goal and defense rather than competitiveness and efficiency (Nabil, et al., 2023, p.12).
It’s worth noticing that on the chart presented below goal/minutes Total for every match is given. Here the y-axis displays the aggregate of goals and minutes the x-axis, matches the date. The blue bars correspond to the total number of goals resulted per match and the orange bar corresponds to the total number of minutes per match (Gonçalves, Gonçalves, & Campante, 2023, p.4).
From the chart, we realize that the most goals were scored on 29-07-2022, 22-06-2022, 10-08-2022, 30-05-2022, 13-06-2022, 16-05-2022, 05-08-2022, 22-07-2022, 29-06-2022, 17-07- These were also the dates when the highest minutes played were recorded.
The mentioned chart represents the count of dates and the total of goals for groups of defensive blocks. On the x-axis, we have applied the defensive block to plot the data set whilst on the y-axis we are interested in the count of dates and the sum of goals. The blue bars on the figure indicate the number of dates associated with each defensive block, and the orange bars indicate the total goals for each block (Palma-Ruiz, et al., 2022, p.4).
The total points are indicated as well as the total players’ ID numbers of each team are shown on the chart. On the x-axis, the teams are marked While on the y-axis we have the total points and the total player identification numbers. The bars in blue show the total points of the teams while the bars in orange show the total player IDs of the teams (Mei, et al., 2020, p.15).
The pie chart given below represents the total of several factors about a certain team. The biggest contribution is the “Sum of player_id” which accounts for 52.53% while the “Sum of team_pts’ accounts for 44.13%. The other percentages are quite minor, with “Sum of Goal” taking 2.02 % and “Sum of fouls” taking 1.32%.
The chart shown is a treemap and represents the sum of fouls, goals, points, and steals of the teams. Each rectangle is a team; and if you add these metrics together, the size of the resulting rectangle will be proportional to the sum. The color of each rectangle also represents the main KPI of the corresponding team.
Dashboards:
The dashboard is quite informative being aimed at the analysis of basketball teams and individual performers. It involves several diagrams, which present information concerning some aspects of the game, namely the points scored, the turnovers, the wins, the efficiency of players, and the defensive performance.
Here's a breakdown of the key features and insights from the dashboard:
Filters:
Enables one to subset the data using the team, player, or any other parameters.
This helps you in case you are interested in particular subjects and would like to receive more relevant results.
Points by Team:
A pie chart of points scored by various teams.
On this type of chart, it is easy to distinguish which teams achieved the highest and the lowest scores.
Turnover by Team:
A second pie chart of the teams showing the number of turnovers committed.
This makes us identify teams that badly play the ball control.
Win by Team:
A bar chart that will represent the total wins of various teams.
The results work well when used to compare the success of one team to another.
In conclusion, it can be said that the dashboard is useful for basketball analysts as well as coaches to monitor the performance, as well as to look for any factors that may have led to the result of a game. It offers a graphical display of major performance measures for comparison purposes, to help users in understanding them. Thus, using the filters and navigating through the different charts will help to draw valuable conclusions about team and player performance and make the right decisions.
The dashboard involves a complex of several views, each of which contains information on some aspect of the basketball team and players. It includes several charts and visualizations that allow you to get an overview of scores, turnovers, wins, players’ efficiency and defense performance, and many other features.
Sum of Goal and Sum of Minutes by Match_date:
This chart is on total goals and time for each match and was useful in extracting the raw data used to complete the chart.
You can search for matches with high scoring or many playing time.
Count of Date and Sum of Goal by Block and Defensive:
This chart compares the blocking percentage against the goal-scoring ratio during games.
It facilitates knowing how defense mechanisms affect scoring.
The sum of points and Sum of Player_id by Team:
This chart plots the points and players’ identification number of each team added together.
It helps them to understand possible issues in having a certain type of team and its work characteristics.
The sum of team_pts, Sum of player_id, Sum of Goals, Sum of fouls, and First opponent:
This is a pie chart for indicating various metrics for a team.
It is particularly useful in giving a broad matrix of the performance of a team with different parameters.
All in all, the presented dashboard can be seen as beneficial for basketball analysts and coaches. As it presents key performance indicators, it is easy to analyze and differentiate by providing a visual insight. With the help of the filters and shifting from one chart to another, we get the deepest realization of the team and player statistics and can both, make good choices.
Part C:
Justification of Visualizations
The visualizations in the dashboard effectively convey the data and insights through:
Cognitive Load:
Simplicity: The charts are basic and conventional kinds of charts; bar charts, pie charts, and line charts which makes it easier to comprehend and analyze.
Clarity: These elements include proper names and terms, figures and table captions, and titles to enable simple usability.
Pre-attentive Attributes:
Color: Values in one category or variable are of different colors so that one can differentiate between the results quickly.
Size: The order of elements such as bars, pie slices, etc., depends on their values making it easy for the viewer to identify with the most significant displayed aspect.
Tufte's Principles:
Data-Ink Ratio: The charts are free from other than the data, this means that any item included in the chart is to illustrate data simply.
Chartjunk: It also minimizes the noise and distractions thus enabling the reader to grasp the message the displayed data holds.
Data-Ink Maximization: The charts combine the use of picture form to display data and the details are easily understood due to clarity.
References:
Gonçalves, C.T., Gonçalves, M.J.A. & Campante, M.I., 2023. Developing Integrated Performance Dashboard Visualisations Using Power BI as a Platform. Information, vol.14, no.11, p.1-16, viewed 7 October 2024, <https://www.mdpi.com/2078-2489/14/11/614/pdf>
Mei, H., Guan, H., Xin, C., Wen, X. & Chen, W., 2020. Data: Data visualization on large high-resolution displays. Visual Informatics, vol.4, no.03, pp.12-23, viewed 7 October 2024, <https://www.sciencedirect.com/science/article/pii/S2468502X20300280>
Nabil, D.H., Rahman, M.H., Chowdhury, A.H. & Menezes, B.C., 2023. Managing supply chain performance using a real-time Microsoft Power BI dashboard by action design research (ADR) method. Cogent Engineering, vol.10, no.2, p.1-19, viewed 7 October 2024, <https://www.tandfonline.com/doi/pdf/10.1080/23311916.2023.2257924>
Palma-Ruiz, J.M., Torres-Toukoumidis, A., González-Moreno, S.E. & Valles-Baca, H.G., 2022. An overview of the gaming industry across nations: using analytics with power BI to forecast and identify key influencers. Heliyon, vol.8, no.2, p. 1-10, viewed 7 October 2024, <https://www.cell.com/heliyon/pdf/S2405-8440(22)00247-X.pdf>
