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Dynamic Cricket Insights: Python-Powered Player Analytic using Python

  • Unique Paper ID: 164366
  • Volume: 10
  • Issue: 12
  • PageNo: 502-505
  • Abstract:
  • In This project we are Identifying the player overall performances based on three formats we are going to find out by analyzing and identifying the last eight years historical data we can analyze the boundaries of 4s and 6s, strike rate, in which position does he comes out, how many balls he faced, and how he dismissals. The key components of the project include data collection, processing, analysis, and visualization. Player performance data is collected from various sources such as historical databases, and statistical archives. Moreover, by analysis of cricket data, harnessing the prowess of libraries such as NumPy, Pandas, and Plotly. After loading the dataset from the file "cricket_analysis.csv" into a Pandas Data Frame, the code embarks on a journey of exploration and visualization foundation for subsequent analysis. Statistical insights emerge as the code meticulously computes the total runs scored and the mean runs per match, illuminating the performance landscape. Visual storytelling takes center stage as Plotly Express choreographs a symphony of line plots, pie charts, and bar graphs, capturing the essence of the cricket player's journey. Batting positions metamorphose into descriptive labels, adorning a pie chart that unveils the distribution of matches graced at each position. while another pie chart paints a vivid portrait of runs plundered from different vantage points on the crease. Bar plots narrate tales of centuries scored, dismissals suffered, and runs amassed against various adversaries, shedding light on the player's resilience and prowess in the face of competition. It covers various aspects of cricket data analysis, including player performance, match details, and opposition analysis.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 12
  • PageNo: 502-505

Dynamic Cricket Insights: Python-Powered Player Analytic using Python

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8.01 (Year 2024)
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Journal no 47859

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