1. Introduction

Welcome to the documentation for the Football Player Ratings and Match Predictions Dataset, covering football matches across the globe. This dataset is sourced from the Fanzword app, where users rate player performances, predict match outcomes, and engage with fellow fans in real time. The data offers valuable insights into fan sentiment, player performance, and predictive analysis for football matches.

Whether you’re a football analyst, data scientist, or enthusiast, this dataset will enable you to:

2. Accessing the Data

The dataset is hosted on Snowflake, where users can directly access the data and integrate it with their own tools for further analysis. Here’s how to get started:

Step 1: Access the Snowflake dataset

Step 2: Query and Explore Data

Step 3: Integrating with BI Tools

3. Data Schema Overview

The dataset consists of multiple tables, each covering different aspects of football data. Below is an example of one of the key tables, which focuses on player ratings:

Table: RATINGS

Column Name Data Type Description
AVERAGE_RATING Number The average rating given to a player by fans.
DATE Date The date of the match or rating.
DAY Number Day of the month for the match.
LEAGUENAME Varchar The name of the football league.
MATCHNAME Varchar The specific match being rated.
MONTH Number Month of the year for the match.
PLAYERNAME Varchar The name of the player being rated.
SEASON Varchar The football season.
TEAMNAME Varchar The name of the team for which the player plays.
YEAR Number The year the match took place.

You can use the RATINGS table for tracking player performance trends and comparing fan sentiment across matches, leagues, and seasons.

For further exploration, the dataset includes additional tables with match statistics, predictions, and team ratings.

4. Use Cases

Here are some common use cases of the dataset:

  1. Player Performance Analysis
    Analyze trends in player ratings across multiple seasons and match types. For example, you can compare how a specific player has been rated in different leagues and against different teams.
  2. Fan Prediction Accuracy
    Explore how accurate fan predictions are when it comes to match outcomes, the first player to score, and the first team to score. You can track the correctness of predictions versus actual match results.
  3. Match Outcome Visualization
    Build dashboards to visualize match outcomes, player performances, and fan ratings over time. Below is a sample query to get started:

    sql
    SELECT MATCHNAME, AVERAGE_RATING, PLAYERNAME
    FROM FOOTBALL_DATA.RATINGS
    WHERE SEASON = '2024'
    AND LEAGUENAME = 'Premier League';
  4. Fan Sentiment and League Comparisons
    Analyze how fans across different regions rate their favorite players and teams.

5. Contact Information

For questions, data support, or access to our pre-built dashboards, please reach out to us at:

Email: contact@fanzword.com
Website: https://www.fanzword.com/contact-us/