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https://github.com/anshpg/exploring-ipl-rivalries-an-analysis-of-matches-from-2008-to-2022
In this project, I delved into an extensive analysis of IPL matches spanning from 2008 to 2022, utilizing a dataset sourced from Kaggle.com. My aim for this IPL season was to explore something novel, steering away from conventional analyses.
https://github.com/anshpg/exploring-ipl-rivalries-an-analysis-of-matches-from-2008-to-2022
csv-files ipl kaggle-dataset matplotlib numpy pandas
Last synced: about 1 month ago
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In this project, I delved into an extensive analysis of IPL matches spanning from 2008 to 2022, utilizing a dataset sourced from Kaggle.com. My aim for this IPL season was to explore something novel, steering away from conventional analyses.
- Host: GitHub
- URL: https://github.com/anshpg/exploring-ipl-rivalries-an-analysis-of-matches-from-2008-to-2022
- Owner: ANSHPG
- License: mit
- Created: 2024-03-28T20:19:49.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-03-28T20:26:17.000Z (9 months ago)
- Last Synced: 2024-03-28T21:34:35.160Z (9 months ago)
- Topics: csv-files, ipl, kaggle-dataset, matplotlib, numpy, pandas
- Language: Jupyter Notebook
- Homepage:
- Size: 1.91 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Exploring-IPL-Rivalries-An-Analysis-of-Matches-from-2008-to-2022
In this project, I delved into an extensive analysis of IPL matches spanning from 2008 to 2022, utilizing a dataset sourced from Kaggle.com. My aim for this IPL season was to explore something novel, steering away from conventional analyses. Initially, I contemplated various avenues within the dataset, including performance metrics and historical data. Eventually, I decided to focus my analysis on the intriguing rivalry between the Rajasthan Royals (RR) and the Delhi Capitals (DC).
It's crucial to emphasize that this analysis was not aimed at prediction but rather focused on deriving insights from the dataset. The dataset primarily comprised match data, lacking individual player statistics. Therefore, my analysis primarily revolved around factors such as toss outcomes, past rivalry history, venue dynamics, and the stage of the league.
Upon conducting the analysis, several key findings emerged:
1. Across all matches, winning the toss and choosing to bat yielded a 32.9% chance of winning, while choosing to field provided a 67.1% chance.
2. In matches between RR and DC, winning the toss and opting to bat resulted in a 41.7% chance of winning, whereas choosing to field provided a 58.3% chance.
3. The rivalry between RR and DC resulted in a balanced outcome, with both teams winning 50% of the matches.
4. In matches held in Jaipur, RR boasted a dominant record, winning 66.7% of the matches against DC.
5. In Jaipur, winning the toss and choosing to bat led to a 24% chance of winning, while opting to field increased the chance to 76%.
6. The chance of winning a match in Jaipur after winning the toss stood at 53.3%.
7. Overall, winning the toss correlated with a 51.5% chance of winning across all matches.
8. In the first 10 matches of the tournament, RR exhibited superiority over DC, winning 71.4% of the encounters.
9. Notably, in the initial 10 matches held in Jaipur, RR maintained a flawless record, winning 100% of the matches against DC.
Combining these insights, a cumulative analysis revealed that Rajasthan achieved a victory percentage of 53.6%, while DC secured a 46.4% victory rate. Furthermore, the project culminated with the anticipation of a match scheduled for March 28, 2023, the results of which would serve as a significant benchmark for the rivalry analysis.
This repository is managed by Anshuman Pattnaik, and permissions are granted for personal or research purposes.
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