NBA Game Elo And Carmelo Ratings by Kaggle | Sports

About this Dataset

NBA Game Elo And Carmelo Ratings

NBA Game Elo and Carmelo Ratings

Examining Historical Matchups and Forecasts

By FiveThirtyEight [source]

About this dataset

This dataset contains game-by-game Elo ratings and forecasts, as well as Carmelo ratings and forecasts for NBA games. It is a comprehensive insight into the league's competitive spirit, team dynamics, and individual brilliance. Use this data to make insightful predictions about upcoming NBA match-ups! The columns include date of game, season played in, whether the game was neutral or not, playoff status of the matchup, name of each team involved in the matchup (team1/team2), Elo rating prior to game for each team (elo1_pre/elo2_pre), probability of each team winning based on their mutual Elo ratings (elo_prob1/elo_prob2), Elo rating after game for each team( elo1_post/ elo2_post ), Carmelo rating prior to game for each team (carmelo1_pre / carmelo 2 _ pre ), Carmello rating after game for each teams(carmello 1 _ post / carmel o 2 _ post ), probability of winning based on mutated Caramelosi ratings (carmeloprob 1 / carmelloprob 2 ) , score obtained by teams(score 1 / score 2 ). Get ready to take your understanding and prediction capabilities farther with this comprehensive dataset!

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How to use the dataset

This dataset provides an in-depth look at the historical Elo and Carmelo ratings of NBA teams dating back to 1946. Using this dataset, you can analyze the performance of individual teams over time, compare their performances against each other, and evaluate their progress and progress relative to other teams. With its comprehensive set of data points, this dataset is a great resource for those interested in basketball analytics or the history of the NBA.

To use this dataset efficiently:

Familiarize yourself with most of the columns (described above) - some contain Elo & Carmelo rating data while others hold game-specific information such as playoff status and team names.

Understand how Elo & Carmelo ratings are determined by reading up on the corresponding FiveThirtyEight pages (see Sources).

Clean and explore your data using a spreadsheet program like Excel or LibreOffice Calc - focus on plotting visual representations such as line graphs for easier comprehension.

Use statistical software like R or Python help conduct deeper analyses such as calculating correlation coefficients between Elo & Carmelo ratings to help understand overall trends in past gameplay.

With successful exploration and understanding of this datasets contents combined with sufficient programming knowledge you will be able to answer pertinent questions about historical NBA events!

Research Ideas

Create a visualisation to explore the differences between teams from different eras. This could be done by plotting the Elo and Carmelo ratings of teams over time relative to each other, or through another type of analysis that allows a comparison between eras.

Construct accessible models for predicting the outcome of regular season and playoff games using either Elo or Carmelo ratings as predictors. These models could then be used on future games by adjusting based on current team standings and outside factors such as trades and injuries to ensure accurate predictions.

Analyse relationships between team performances during regular season games versus playoff games, such as changes in Elo or Carmelo ratings following specific types of wins in either tournament format . This may help reveal strategies used by certain teams that are more successful when playing in high-pressure situations during playoffs compared to regular-season matches

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: nba_elo.csv

Column name	Description
date	Date of the game. (Date)
season	Season in which the game was played. (Integer)
neutral	Whether the game was played at a neutral site. (Boolean)
playoff	Whether the game was a playoff game. (Boolean)
team1	Name of the first team. (String)
team2	Name of the second team. (String)
elo1_pre	Elo rating of the first team before the game. (Float)
elo2_pre	Elo rating of the second team before the game. (Float)
elo_prob1	Probability of the first team winning based on Elo ratings. (Float)
elo_prob2	Probability of the second team winning based on Elo ratings. (Float)
elo1_post	Elo rating of the first team after the game. (Float)
elo2_post	Elo rating of the second team after the game. (Float)
carmelo1_pre	Carmelo rating of the first team before the game. (Float)
carmelo2_pre	Carmelo rating of the second team before the game. (Float)
carmelo1_post	Carmelo rating of the first team after the game. (Float)
carmelo2_post	Carmelo rating of the second team after the game. (Float)
carmelo_prob1	Probability of the first team winning based on Carmelo ratings. (Float)
carmelo_prob2	Probability of the second team winning based on Carmelo ratings. (Float)
score1	Score of the first team. (Integer)
score2	Score of the second team. (Integer)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit FiveThirtyEight.

Tables

Nba Elo

@kaggle.thedevastator_nba_game_elo_and_carmelo_ratings_1946_2020.nba_elo

4.58 MB
67068 rows
21 columns


CREATE TABLE nba_elo (
  "index" BIGINT,
  "date" TIMESTAMP,
  "season" BIGINT,
  "neutral" BIGINT,
  "playoff" VARCHAR,
  "team1" VARCHAR,
  "team2" VARCHAR,
  "elo1_pre" DOUBLE,
  "elo2_pre" DOUBLE,
  "elo_prob1" DOUBLE,
  "elo_prob2" DOUBLE,
  "elo1_post" DOUBLE,
  "elo2_post" DOUBLE,
  "carmelo1_pre" DOUBLE,
  "carmelo2_pre" DOUBLE,
  "carmelo1_post" DOUBLE,
  "carmelo2_post" DOUBLE,
  "carmelo_prob1" DOUBLE,
  "carmelo_prob2" DOUBLE,
  "score1" DOUBLE,
  "score2" DOUBLE
);