| # UFC Fighters winrate - Exploratory Data Analysis (EDA) | |
| Author: stiven rodriguez | |
| Course: Introduction to Data Science | |
| Assignment: EDA & Dataset Analysis | |
| Dataset Source: Kaggle – UFC Fighters' Statistics Dataset | |
| Dataset Size: ~4,112 rows × 18 column | |
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| ## Project Goal | |
| The Project Goal was answer the question : What are the defining attributes—both physiological | |
| (physical) and strategic (fighting style)—that characterize the ultimate mixed martial arts fighter? | |
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| ## Dataset Overview | |
| The dataset contains information about UFC Fighters winrate with the following key columns: | |
| - **name** | |
| - **height_cm** | |
| - **weight_in_kg** | |
| - **stance** | |
| - **Age** | |
| - **reach_in_cm** - Arm span length (cm) | |
| - **stance** | |
| - **Strikes/Min** - significant_strikes_landed_per_minute | |
| - **Strike Accuracy** - significant_striking_accuracy | |
| - **Absorb/Min** - significant_strikes_absorbed_per_minute | |
| - **Strike Defense** - significant_strike_defence | |
| - **Takedowns/Fight** - average_takedowns_landed_per_15_minutes | |
| - **TD Defense** - takedown_defense | |
| - **Submissions/Fight** - average_submissions_attempted_per_15_minutes | |
| - **TD Accuracy** - takedown_accuracy | |
| - **total_fights** | |
| - **win_rate** | |
| The final dataset is the result of systematic cleaning, validation, and filtering procedures | |
| --- | |
| ## Data Cleaning Summary | |
| - Selected 13 relevant columns from the original 18 columns for analysis, and subsequently engineered 3 new features. | |
| - Handling Non-Informative & Duplicates | |
| - Missing Value Handling, Removing Fighters with 3 or More Missing Columns | |
| - Filtering 3 Fighters Due to Missing Height Data | |
| - Filtering Fighters with Missing Date of Birth | |
| - Filling Missing 'Reach' Values with the Mean | |
| - Filling Missing 'Stance' Values with 'Unknown | |
| - Column Renaming for Readability | |
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| ## Research Questions & Insights | |
| ### *Q1: "Does age influence the win rate (WINRATE) of UFC fighters, and what is the distribution of ages among the fighters in the dataset? | |
| **Visualization : barplot** | |
| #The graph does not include fighters over the age of 38, | |
|  | |
| *Click on the image above to view it in full size* | |
| **Insight:** | |
| - My conclusions are that age is highly significant for successfully entering the UFC (meaning fighters need many wins on the way, | |
| which often happens around age 30+). However, within the UFC, there is no correlation between a fighter's age and their win rate (WINRATE) | |
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| ### *Q2: Is there a correlation between a physical variable or fighting style with the Win rate? | |
| **Visualization: Correlation Heatmap of Variables** | |
|  | |
| *Click on the image above to view it in full size* | |
| **Insight:** | |
| - The analysis suggests that there is no strong predictive variable for success, given the consistently weak correlations found across all features. | |
| - Despite this, the maximum correlation observed was specifically associated with measures of a fighter's 'aggressive behavior during the bout.' | |
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| ### *Q3: Given that a fighter possesses all the 'aggressive' attributes with a positive correlation to victory, are these traits sufficient, | |
| on their own, to reliably predict or guarantee a high Win Rate? | |
| **Visualization: Correlation Heatmap of Variables** | |
|  | |
| *Click on the image above to view it in full size* | |
| **Insight:** | |
| - The conclusion is negative. While 'aggressive' attributes demonstrated the highest (positive) correlation with victory, these traits are not sufficient on their own | |
| - to reliably predict or guarantee a high Win Rate. This determination is based on the finding that the overall correlation remains extremely low, | |
| - even when considering the three most highly correlated variables—a fact visually evident in the data you referenced | |
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| ## Final Conclusions | |
| *Main Research Question:* What are the defining attributes—both physiological (physical) and strategic | |
| - (fighting style)—that characterize the ultimate mixed martial arts fighter? | |
| *Answer:* My analysis indicates that no single variable with a strong positive correlation can be identified or | |
| - relied upon to define the ultimate mixed martial arts fighter. Nevertheless, the data does suggest that a | |
| - fighter's aggressive behavior during a bout is a key strategic factor that statistically elevates the probability of achieving a victory | |
| ### Key Findings: | |
| - Physiological Attributes: Body measurements and physical characteristics (such as Height, Reach, or Leg length) show no significant correlation with a fighter's | |
| - probability of victory (WINRATE). This suggests that success in the UFC is not determined by inherent physical size. | |
| - Strategic Aggression: Successful Takedowns (control metrics) and Strikes Accuracy (offensive volume metrics) were found to be the variables with the highest positive | |
| - correlation with victory among all examined features. | |
| - Predictive Limitation: Despite being the top-correlated factors, the relationship between these aggressive metrics and the final outcome is still relatively weak. | |
| - Therefore, while Takedowns and Strike Accuracy are important strategic advantages, they are not sufficient on their own to reliably predict or guarantee a high Win Rate. | |
| ### Overall Insights: | |
| - There is no single parameter that can be truly relied upon to predict victory. | |
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| ## Video (https://www.veed.io/view/7decd8ee-9454-4b39-bbf0-48876d30fc42?source=editor&panel=share) | |