WWE SQL Database

WWE Events & Match Ratings Database (2015–2025)

A structured SQL database built from real WWE event and match rating data sourced from Cagematch.net and the Wrestling Observer Newsletter (WON).

This project demonstrates end-to-end data engineering and analytics skills from schema design and data ingestion to SQL querying and Python visualization.

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Table of Contents

• Project Overview
• Dataset
• Schema Design
• Data Cleaning
• Analysis Questions
• Visualizations
• Tech Stack
• How to Run
• Limitations & Future Work

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Project Overview

This project was built over 7 weeks as part of a database design course. The goal was to:

1. Choose a real-world dataset
2. Design a relational schema
3. Clean and ingest data into a SQL database
4. Write analytical SQL queries
5. Visualize results in Python
6. Document and present findings

The dataset covers 10 years of WWE programming (2015–2025), including televised shows, non-televised live events, and premium live events (PLEs/PPVs).

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Dataset

Source: Kaggle — WWE Events & Match Ratings (2015–2025)

| File | Rows | Description |
|------|------|-------------|
| `wwe_events.csv` | 5,970 | Event-level data (show name, type, location) |
| `wwe_match_rating.csv` | 8,101 | Match-level data with fan and critic ratings |

Rating Sources: - CageMatch Rating — crowdsourced fan ratings from cagematch.net (6,235 matches rated) - WON Star Rating — critic ratings from Dave Meltzer’s Wrestling Observer Newsletter (2,171 matches rated)

Event Types:

| Type | Count |
|------|-------|
| Televised | 3,261 |
| Non-Televised | 2,421 |
| PLE/PPV | 288 |

Table Definitions

wwe_events — Event-level fact table
| Column | Type | Key |
|--------|------|-----|
| Date | DATE | PK |
| Promotion | TEXT | PK |
| Event | TEXT | |
| EventName | TEXT | |
| EventType | TEXT | |
| CityTown | TEXT | |
| StateProvince | TEXT | |
| Country | TEXT | |

wwe_match_rating — Match-level fact table
| Column | Type | Key |
|--------|------|-----|
| Index | INTEGER | PK |
| Date | DATE | FK → wwe_events |
| Promotion | TEXT | FK → wwe_events |
| Match | TEXT | |
| CageMatchRating | DECIMAL | |
| CageMatchRatingVotes | INTEGER | |
| WONStarRating | DECIMAL | |
| Opponent1 | TEXT | |
| Opponent2 | TEXT | |

tag_teams — Unpivoted tag team opponents
| Column | Type | Key |
|--------|------|-----|
| OpponentID | INTEGER | PK |
| MatchIndex | INTEGER | FK → wwe_match_rating |
| Team | TEXT | |

superstars — Unique superstar lookup table
| Column | Type | Key |
|--------|------|-----|
| SuperstarID | INTEGER | PK |
| SuperstarName | TEXT | |

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Data Cleaning

Cleaning was performed in R using the tidyverse package.

Steps taken:

• Dates were already in YYYY-MM-DD format and loaded as proper date types, no reformatting needed
• Numeric columns (CageMatchRating, WONStarRating, CageMatchRatingVotes) contained no text or mixed values
• Missing ratings represented as NA (expected: not every match is rated by both sources)
• Trailing commas removed from opponent strings (e.g. "Raquel Rodriguez, Stephanie Vaquer,")
• Tag teams extracted by detecting & in opponent columns and pivoting to long format
• 515 duplicates looking rows in wwe_events are legitimate. WWE runs multiple house shows in different cities under the same event name on the same date

Known limitation: 81 match dates in wwe_match_rating have no corresponding row in wwe_events. These matches are excluded from any query that joins both tables.

Tag Teams Table (R Code)

library(dplyr)
library(tidyr)
library(stringr)

tag_teams <- match_rating %>%
  select(Index, Opponent.1, Opponent.2) %>%
  pivot_longer(
    cols = c(Opponent.1, Opponent.2),
    names_to = "Side",
    values_to = "Team"
  ) %>%
  mutate(Team = str_trim(str_replace_all(Team, ",\\s*$", ""))) %>%
  filter(str_detect(Team, "&")) %>%
  select(MatchIndex = Index, Team) %>%
  mutate(OpponentID = row_number()) %>%
  select(OpponentID, MatchIndex, Team)

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Analysis Questions

Q1 — How has fan engagement varied across WWE event types from 2015 to 2025?

SELECT 
    YEAR(m.Date) AS Year,
    e.EventType,
    SUM(m.CageMatchRatingVotes) AS TotalVotes
FROM match_rating m
JOIN events e ON e.Date = m.Date AND e.Promotion = m.Promotion
GROUP BY Year, e.EventType
ORDER BY Year ASC, TotalVotes DESC;

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Q2 — Who are the top 20 highest rated WrestleMania superstars (2015–2025)?

SELECT s.SuperstarName,
       ROUND(AVG(m.CageMatchRating), 2) AS AvgRating,
       COUNT(*) AS MatchCount
FROM superstars s
JOIN wwe_match_rating m ON s.MatchIndex = m.Index
JOIN wwe_events e ON e.Date = m.Date AND e.Promotion = m.Promotion
WHERE e.Event = 'WWE WrestleMania'
GROUP BY s.SuperstarName
ORDER BY AvgRating DESC
LIMIT 20;

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Q3 — How did John Cena’s performance vary across career eras?

📓 johncena_timeline.ipynb

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Tech Stack

Tool	Purpose
R / tidyverse	Data cleaning and ingestion
SQLite	Relational database
Python / pandas	Data manipulation
Python / matplotlib	Visualization
Kaggle	Data source