NBA Player Betting Trends Docs

Intro

The Player Betting Trends endpoint provides detailed insights into individual player performance in various betting scenarios. Player betting trends are available for various sport-specific individual performance metrics, such as hits, points scored, rushing yards, and other relevant statistics. By analyzing historical data and context-dependent trends, users can understand how players perform under different conditions.

An example of a player betting trend can be seen below:

Betting Markets Offered

• Total steals (including overtime)
• Total blocks (including overtime)
• Total points (including overtime)
• Total assists (including overtime)
• Total rebounds (including overtime)
• Total 3-point field goals (including overtime)
• Total points plus assists plus rebounds (including overtime)
• Total points plus rebounds (including overtime)
• Total points plus assists (including overtime)
• Total rebounds plus assists (including overtime)

One-Parameter Scenarios

* Only available after first games of season (i.e. does not go back to previous season)

One-Parameter Scenario Examples

• Luka Doncic has failed to exceed 32.5 points in 4 straight playoff games (29.5 points/game average)
• Luka Doncic has exceeded 1.5 steals in 4 of his last 5 games against top 10 scoring defenses (2.4 steals/game average)
• Luka Doncic has failed to exceed 42.5 points + rebounds in 11 of his last 12 games after a win (36.8 points + rebounds/game average)

Two-Parameter Scenarios

* Only available after first game of season (i.e. does not go back to previous season)

Two-Parameter Scenario Examples

• Luka Doncic has exceeded 3.5 three pointers made in 6 of his last 7 games on the road as an underdog (4.6 three pointers made/game average)
• Luka Doncic has exceeded 9.5 assists in 8 of his last 9 games at home against opponents with a losing recor (11.1 assists/game average)
• Luka Doncic has exceeded 8.5 rebounds in 13 of his last 15 games on the road after a win (10.2 rebounds/game average)

Data Points

• day: The date of the event in YYYYMMDD format.
• book: The name of the sportsbook providing the odds.
• line: The line associated with the given market (if applicable).
• odds: The betting odds for the market.
• rank: The rank of the trend in the latest set of trends produced
• type: A dictionary containing details about the type of bet:
• flavor: Additional flavor text or subcategory for the bet.
• market: The market type for the bet (e.g., moneyline).
• sport: The sport the data pertains to (e.g., MLB).
• total: The consensus total line for the game
• ngames: The number of games considered in the trend.
• opp_id: The sportradar US-id (UUID) for the opposing team.
• param1: The first parameter defining the scenario/filter for the trend (e.g. “opp”)
• value1: The value of the first parameter (e.g. “NYJ”)
• param2: The first parameter defining the scenario/filter for the trend (if applicable)
• value2: The value of the second parameter.
• spread: The point spread for the game.
• book_id: The unique sportradar identifier for the sportsbook.
• game_id: The unique sportradar US-id (UUID) for the game.
• matchup: The matchup identifier for the event in format:** {AwayTeam}at{HomeTeam}.
• team_id: The unique sportradar US-id (UUID) for the team.
• weekday: The day of the week the game is played.
• prop_id: The unique Fansure assigned identifier for the trend. Same as tracking_id
• cover_pct: The percentage of time the line is exceeded in the games in the trend
• data_type: team_betting_trends
• game_date: The date and time of the game in ISO 8601 format.
• odds_type: The type of odds (e.g., moneyline).
• opp_sr_id: The unique sportradar US-id (UUID) for the opposing team
• statistic: The statistic being referred to by the trend (e.g. for moneyline, statistic = wins)
• team_name: The full name of the team.
• text_long: A detailed description of the trend.
• date_lastn: The dates of the n games which define the trend in ascending order.
• game_sr_id: The unique sportradar id (srid) for the game
• push_lastn: The number of push outcomes in the n games in the trend window.
• team_sr_id: The unique sportradar id (srid) for the team
• text_short: A short description of the trend.
• trackingId: The unique Fansure assigned tracking ID for the trend. Same as prop_id
• trend_type: The type of trend (hot or cold).
• under_odds: The betting odds for the under (if applicable).
• cover_lastn: The number of times the line was exceeded in the n games in the trend
• stats_lastn: List of statistical values associated with each of the n games in the trend
• abbreviation: The abbreviation of the team name.
• content_long: A detailed description of the trend including the odds.
• gameId_lastn: List of the unique sportradar US-id (UUID) for each game in the trend
• losses_lastn: The number of times the line was not exceeded in the n games in the trend
• odds_type_id: The unique sportradar identifier for the odds type.
• matchup_lastn: List of the matchup strings for each game in the trend.
• opp_team_name: The full name of the opposing team.
• interest_score: The interest score for the trend.
• seasontype_lastn: List of the season type (e.g., REG, PST) for the n games in the trend
• best: Indicates whether this is the best available odds or line for the event (true / false).
• position: The position of the player in the context of the data (e.g., P for pitcher).
• market_id: The unique identifier for the betting market.
• over_odds: The betting odds for the over.
• exceed_lastn: The number of times the line was exceeded in the n games in the trend
• participant_id: The unique sportradar US-id (UUID) for the participant (player).
• mean_performance: The player’s average performance for the statistic of interest in the games covered by the trend
• player_relevance: The relevance score of the player associated with the trend
• participant_sr_id: The unique sportradar id (srid) for the participant (player)
• participant_full_name: The full name of the participant (player).