Play by play data for division 1 college basketball for the 23-24 season. Also included is pregame spread/total lines for ~90% of the games, as well as conditional ESPN win probability

Overview

This dataset contains data about NCAA Basketball games, teams, and players. Game data covers play-by-play and box scores back to 2009, as well as final scores back to 1996. Additional data about wins and losses goes back to the 1894-5 season in some teams' cases.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME]. Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

Acknowledgements

Sportradar: Copyright Sportradar LLC. Access to data is intended solely for internal research and testing purposes, and is not to be used for any business or commercial purpose. Data are not to be exploited in any manner without express approval from Sportradar.

NCAA®: Copyright National Collegiate Athletic Association. Access to data is provided solely for internal research and testing purposes, and may not be used for any business or commercial purpose. Data are not to be exploited in any manner without express approval from the National Collegiate Athletic Association.

NCAA men's basketball is the college level of basketball played in the United States and is seen as the final step up before the NBA. In a survey conducted in March 2025, almost ** percent of respondents from the United States stated that NCAA men's college basketball was one of their top interests.

NCAA men's basketball is the college level of basketball played in the United States and is seen as the final step up before the NBA. In a survey conducted in March 2025 in the United States, ** percent of respondents aged 35 to 54 stated that NCAA men's college basketball was one of their top interests.

College Basketball Game Statistics (2014-2024)

Dataset Description

This dataset contains detailed game-by-game statistics for NCAA Division I men's basketball games from the 2013-14 season through the 2023-24 season (excluding 2019-20). The data was sourced from barttorvik.com, a respected resource for college basketball analytics.

Temporal Coverage

• Start: 2013-14 season
• End: 2023-24 season
• Note: 2019-20 season is excluded due to the COVID-19 pandemic's impact on that season

Column Descriptions

Game Information

• Date: Game date in YYYY-MM-DD format
• Type: Type of game
  • N: Non-conference regular season game
  • C: Conference regular season game
  • CT: Conference Tournament game
  • P: NIT Tournament game
  • T: NCAA Tournament game
• Team: Name of the team
• Conf.: Conference affiliation of the team
• Opp.: Opponent team name
• Venue: Game location (Home/Away/Neutral)

Game Outcomes

• Win: Binary indicator of game result (1 = Win, 0 = Loss)
• Points_For: Points scored by the team
• Points_Against: Points scored by the opponent
• Point_Differential: Point difference (Points_For minus Points_Against)

Team Performance Metrics

• Adj. O: Adjusted offensive efficiency
• Adj. D: Adjusted defensive efficiency
• T: Game tempo (possessions per 40 minutes)

Offensive Statistics (prefixed with O.)

• O.EFF: Offensive efficiency (points per 100 possessions)
• O.eFG%: Effective field goal percentage
• O.TO%: Turnover percentage
• O.Reb%: Offensive rebounding percentage
• O.FTR: Free throw rate

Defensive Statistics (prefixed with D.)

• D.EFF: Defensive efficiency (points allowed per 100 possessions)
• D.eFG%: Opponent's effective field goal percentage
• D.TO%: Forced turnover percentage
• D.Reb%: Defensive rebounding percentage
• D.FTR: Opponent's free throw rate

Performance Metrics

• G-Sc: Game Score (a measure of game performance)

Usage Notes

• All efficiency metrics are tempo-adjusted
• Conference affiliations reflect the team's conference for that specific season
• Neutral site games include both tournament and non-tournament games
• The dataset includes regular season and postseason games

NCAA men's basketball is the college level of basketball played in the United States and is seen as the final step up before the NBA. In a survey conducted in March 2025 in the United States, around ** percent of male respondents stated that NCAA men's college basketball was one of their top interests.

Basketball games and training sessions are characterized by quick actions and many scoring attempts, which pose biomechanical loads on the bodies of the players. Inertial Measurement Units (IMUs) capture these biomechanical loads as PlayerLoad and Inertial Movement Analysis (IMA) and teams collect those data to monitor adaptations to training schedules. However, the association of biomechanical loads with game performance is a relatively unexplored area. The aims of the current study were to determine the statistical relations between biomechanical loads in games and training with game performance. Biomechanical training and game load measures and player-level and team-level game stats from one college basketball team of two seasons were included in the dataset. The training loads were obtained on the days before gameday. A three-step analysis pipeline modeled: (i) relations between team-level game stats and the win/loss probabilities of the team, (ii) associations between the player-level training and game loads and their game stats, and (iii) associations between player-level training loads and game loads. The results showed that offensive and defensive game stats increased the odds of winning, but several stats were subject to positional and individual performance variability. Further analyses, therefore, included total points [PTS], two-point field goals, and defensive rebounds (DEF REB) that were less subject to those influences. Increases in game loads were significantly associated with game stats. In addition, training loads significantly affected the game loads in the following game. In particular, increased loads 2 days before the game resulted in increased expected game loads. Those findings suggested that biomechanical loads were good predictors for game performance. Specifically, the game loads were good predictors for game stats, and training loads 2 days before gameday were good predictors for the expected game load. The current analyses accounted for the variation in loads of players and stats that enabled modeling the expected game performance for each individual. Coaches, trainers, and sports scientists can use these findings to further optimize training plans and possibly make in-game decisions for individual player performance.

Duke NCAA Basketball Detection

## Overview

Duke NCAA Basketball Detection is a dataset for object detection tasks - it contains Zion Ball Player Ref Hoop annotations for 560 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Women's College Basketball Team Rosters

Women's College Basketball Team Rosters for the 2022-2023 Season

By Andy Kriebel [source]

About this dataset

The dataset consists of three files: wbb_years_cleaned.csv, wbb_rosters_2022_23.csv, and wbb_teams.csv.

The wbb_years_cleaned.csv file contains data on the count of women's college basketball teams and their rosters for each year. It focuses specifically on the 2022-2023 season and includes information such as the number of players in each team's roster and whether any players are on redshirt status.

The wbb_rosters_2022_23.csv file provides insights into the height distribution within women's college basketball teams during the 2022-2023 season. This file specifically examines how heights vary among players within a team.

Lastly, the wbb_teams.csv file offers detailed information about individual women's college basketball teams participating in the 2022-2023 season. It includes essential details like team names, Twitter handles, URLs to their profiles or websites, NCAA IDs (identification numbers), conference affiliations, and division affiliations.

This dataset is derived from sources curated by Derek Willis from Sports Data Analysis & Visualization class at Merrill College. The original visualization showcasing this dataset presents intriguing height distribution patterns across different women's college basketball teams during a specific period.

Overall, this dataset serves as a valuable resource for analyzing various aspects related to women's college basketball in terms of player demographics (such as height), positional roles played by individuals within a team hierarchy (primary position vs. secondary position), educational backgrounds (high schools attended and previous schools attended), hometowns, team affiliations at the conference and division level, and social media presence (Twitter handles)

How to use the dataset

Dataset Overview

The dataset consists of three CSV files: wbb_years_cleaned.csv, wbb_rosters_2022_23.csv, and wbb_teams.csv.

wbb_years_cleaned.csv

This file contains data on the count of women's college basketball teams and their rosters for each year, with a focus on the 2022-2023 season. It includes columns such as: - year: The year of the women's college basketball season. - count: The number of players in the roster for that particular year. - redshirt: Indicates whether a player is on redshirt status for that particular year.

wbb_rosters_2022_23.csv

This file contains data on the height distribution of women's college basketball teams in the 2022-2023 season. It includes columns such as: - team: The name of the women's college basketball team. - height_ft: The height of players in feet. - height_in: The height of players in inches. - total_inches: The total height of players in inches.

wbb_teams.csv

This file contains information about women’s college basketball teams in the 2022–2033 season, including team names, Twitter handles, URLs, NCAA IDs, conference/division affiliations, and more. It includes columns such as: - team_state: The state where the women’s college basketball team is located. - conference: The conference to which each team belongs.

Getting Started

• Download or import all three CSV files into your preferred data analysis or visualization tool, such as Python with pandas, R with dplyr, or Excel.

• Familiarize yourself with the available columns in each dataset. Refer to the provided Columns section for detailed information on each column's meaning and format.

• Determine your research questions or objectives based on the available data. Here are a few examples of what you can explore using this dataset:

  • Analyze the height distribution of women's college basketball teams in the 2022-2023 season.
  • Investigate how different conferences vary in team sizes and player redshirt status.
  • Examine the relationship between

Research Ideas

• Analyzing the distribution of player heights across women's college basketball teams can provide insights into the physical characteristics of players in different positions and divisions. This analysis can help identify trends and patterns in player recruitment and development.
• Comparing the height distribution of teams from different conferences or divisions can reveal any disparities or advantages in terms of team composition and playing style. This information can be useful for coaches, recruiters, and fans to understand how different factors influence team performance.
• By examining the secondary positions played by playe...

This dataset contains 1900 rows of college basketball players, including player metadata (age, height, weight, position, dominant hand), biometric indicators (heart rate, oxygen saturation, body temperature, reaction time, fatigue level), gameplay video-derived features (pose keypoints, player location, movement metrics), and performance metrics (shooting accuracy, dribbling efficiency, pass success, defensive response, rebounds, fouls).

NCAA men's basketball is the college level of basketball played in the United States and is seen as the final step up before the NBA. In a survey conducted in March 2023, around ** percent of Hispanic respondents stated that they were avid fans of NCAA men's college basketball in the United States.

During a 2024 survey, it was found that Caitlin Clark was the most widely known NCAA basketball player among adults in the United States, with ** percent of respondents representative of the general population saying they had heard of her. This figure was understandably higher among NCAA fans, with ** percent of them recognizing Clark, who broke the all-time scoring record in NCAA college basketball earlier that year.

Data taken from https://www.kaggle.com/datasets/andrewsundberg/college-basketball-dataset and updated with data from https://barttorvik.com/

TEAM: The Division I college basketball school

CONF: The Athletic Conference in which the school participates in (A10 = Atlantic 10, ACC = Atlantic Coast Conference, AE = America East, Amer = American, ASun = ASUN, B10 = Big Ten, B12 = Big 12, BE = Big East, BSky = Big Sky, BSth = Big South, BW = Big West, CAA = Colonial Athletic Association, CUSA = Conference USA, Horz = Horizon League, Ivy = Ivy League, MAAC = Metro Atlantic Athletic Conference, MAC = Mid-American Conference, MEAC = Mid-Eastern Athletic Conference, MVC = Missouri Valley Conference, MWC = Mountain West, NEC = Northeast Conference, OVC = Ohio Valley Conference, P12 = Pac-12, Pat = Patriot League, SB = Sun Belt, SC = Southern Conference, SEC = South Eastern Conference, Slnd = Southland Conference, Sum = Summit League, SWAC = Southwestern Athletic Conference, WAC = Western Athletic Conference, WCC = West Coast Conference)

G: Number of games played

W: Number of games won

ADJOE: Adjusted Offensive Efficiency (An estimate of the offensive efficiency (points scored per 100 possessions) a team would have against the average Division I defense)

ADJDE: Adjusted Defensive Efficiency (An estimate of the defensive efficiency (points allowed per 100 possessions) a team would have against the average Division I offense)

BARTHAG: Power Rating (Chance of beating an average Division I team)

EFG_O: Effective Field Goal Percentage Shot

EFG_D: Effective Field Goal Percentage Allowed

TOR: Turnover Percentage Allowed (Turnover Rate)

TORD: Turnover Percentage Committed (Steal Rate)

ORB: Offensive Rebound Rate

DRB: Offensive Rebound Rate Allowed

FTR : Free Throw Rate (How often the given team shoots Free Throws)

FTRD: Free Throw Rate Allowed

2P_O: Two-Point Shooting Percentage

2P_D: Two-Point Shooting Percentage Allowed

3P_O: Three-Point Shooting Percentage

3P_D: Three-Point Shooting Percentage Allowed

ADJ_T: Adjusted Tempo (An estimate of the tempo (possessions per 40 minutes) a team would have against the team that wants to play at an average Division I tempo)

WAB: Wins Above Bubble (The bubble refers to the cut off between making the NCAA March Madness Tournament and not making it)

POSTSEASON: Round where the given team was eliminated or where their season ended (R68 = First Four, R64 = Round of 64, R32 = Round of 32, S16 = Sweet Sixteen, E8 = Elite Eight, F4 = Final Four, 2ND = Runner-up, Champion = Winner of the NCAA March Madness Tournament for that given year)

SEED: Seed in the NCAA March Madness Tournament

YEAR: Season

As of March 2024, Pete Maravich held the record for the most points scored by a player in NCAA men's basketball history. Until that month, Maravich's record applied to both men's and women's college basketball, however, Caitlin Clark surpassed his points tally in a game for Iowa Hawkeyes against Ohio State.

While the players on the court might still be college students, the National Collegiate Athletic Association men's basketball top division still draws in big crowds. The North Carolina men's basketball team attracted the highest average attendance during the 2025 season. The team, traditionally known as the Tar Heels, had an average home audience of just over 20,521 in 2025. NCAA basketball attracts millions The total paid attendance at NCAA college basketball tournament games reached the highest figure to date in 2013, when nearly 800,000 spectators paid to watch the games. This figure has seen a slight decrease in recent years, with the 2025 count totaling around 708,000 spectators. The Division I basketball tournament, commonly referred to as March Madness, takes the format of a single-elimination tournament between 68 teams. The high-stakes nature of the tournament attracts television viewers reaching into the millions. Over 10 million viewers tuned in during the 2025 tournament, with 18.1 million fans glued to the gripping championship game in 2025 between the Houston Cougars and Florida Gators. March Madness mainstays The Kentucky Wildcats men's basketball team is one of the most successful Division I basketball sides in history, so it is no wonder that an average of around 20,000 fans flocked to their home games during the 2025 season. The Wildcats led the association in terms of March Madness appearances – the team had played in a total of 62 tournaments – and had the second most number of NCAA basketball titles as of 2025. The UCLA Bruins top this list, having taken home the title a record 11 times. The Los Angeles-based program achieved enormous success in the 1960s and 1970s, but their only title since that period came back in 1995. The Bruins reached the Final Four stage of the tournament for three years running between 2006 and 2008, as well as in 2021, but have been unable to recapture the success of times gone by.

Context

I was having an everyday conversation with two of my friends here about how much programming knowledge we need for our college classes. One of my friends is extremely knowledgeable on basketball statistics; he can recall seemingly randomly stats for almost any college player. When we learned about his process for writing articles and making conclusions based on data, we realized that using machine learning would expedite his process almost immediately. So the first step would be to compile all the data we need.

Content

Some of the statistics are obvious such as points, blocks, etc. However, some advanced statistics employ complicated equations, such as offensive rating and PORPAG. These statistics all need to be taken with a grain of salt, since some can be misleading. Specifically, plus/minus may seem to be an effective statistic for ranking how much of an impact players have on their team, but this can be heavily impacted by rotations.

For instance, on my favorite NBA team, the Golden State Warriors, plus/minus is almost irrelevant, since any player that is on the court with Stephen Curry almost always has a much better plus/minus than players who are forced to play without his presence on the court.

However, with the sheer bulk of stats present, I'm hoping there will be clear patterns that emerge with further digging into the data.

Acknowledgements

Avinash Chauhan and Logan Norman, who helped inspire this idea.

Kevin Bradshaw holds the single-game individual scoring record for a NCAA Division I college basketball game. Bradshaw scored a record 72 points in a 1991 Division I basketball game between Alliant International and Loyola Marymount.

See the latest NCAA D2 Men's College Basketball rankings for 2025-2026. Updated daily with power ratings, predictions, scores, team stats, conference strength, and more.

The Massey Ratings for NCAA basketball are a statistical system used to rank teams based on their performance. They incorporate game outcomes, margin of victory, strength of schedule, and other factors to produce a numerical rating for each team. The system uses mathematical models to assess team strength and predict future performance. It is one of many computer-based ranking methods and is often used for evaluating team quality beyond traditional win-loss records. The Massey Ratings also contribute to the NCAA’s broader ranking metrics and selection considerations for tournaments.

The First Four are the so-called "Play-In Games" of the NCAA's March Madness college basketball tournament. These games pit the ****-**** seeds in the tournament against the last four at-large seeds. During the 2021 tournament, the match up between UCLA and Michigan State was watched by **** million viewers on TBS, a record for the First Four stage of the tournament.