Nba play by play data scraping11/8/2023 The list of betting outcomes from each sportsbook in this market Shows if any bets are currently available for betting in this market The sportsbooks that have had odds in this market The name of the player tied to this market (if applicable) The player id of the player tied to this market (if applicable) The team key of the team tied to this market (if applicable) The team id of the team tied to this market (if applicable) The unique identifier of the event this betting market exists for The unique identifier of this betting market The date and time the connected game starts, where applicable The list of betting markets for this event The home team rotation number of the game tied to this event The away team rotation number of the game tied to this event The combined scores of the home and away team of the game tied to this event (post-game) The home team score of the game tied to this event (post-game) The away team score of the game tied to this event (post-game) The global home team id of the game tied to this event The global away team id of the game tied to this event The Home team ID of the game tied to this event The Away team ID of the game tied to this event The Home team key of the game tied to this event ![]() The Away Team Key of the game tied to this event The status of the game tied to this event The GlobalGameId of the game tied to this event The GameId of the game tied to this event The last updated date of this betting event The furthest forward time any book has a market set to close for this betting event The name of the bet type of this betting event The identifier of the bet type of this betting event The unique identifier for this betting event Win, Loss, Push)Ī list of the combinations of BettingMarketTypeID, BettingBetTypeID, and BettingPeriodTypeID that will be resulted Home, Over)Ī list of the possible BettingResultTypes (e.g. Game, Future)Ī list of the possible BettingOutcomeTypes (e.g. Full Game, Regulation Time, 1st Half)Ī list of the possible BettingEventTypes (e.g. Game Line, Team Prop)Ī list of the possible BettingPeriodTypes (e.g. Moneyline, Spread)Ī list of the possible BettingMarketTypes (e.g. doi: 10.2466/pms.106.1.A list of the possible BettingBetTypes (e.g. Differences in game-related statistics of basketball performance by game location for men’s winning and losing teams. Performacne difference between winning and losing basketball teams during close, balanced and unbalanced quarters. Investigating the game-related statistics and tactical profile in NCAA division I men’s basketball games. ![]() 2009 8: 458–462.Ĭonte D, Tessitore A, Gjullin A, Mackinnon D, Lupo C, Favero T. Effects of consecutive basketball games on the game-related statistics that discriminate winner and losing teams. Ibanez SJ, Garcia J, Feu S, Lorenzo A, Sampaio J. Game related statistics which discriminate between winning and losing under-16 male basketball games. Lorenzo A, Gomez MA, Ortega E, Ibanez SJ, Sampaio J. ![]() Also, overall shooting efficiency (i.e., free-throw, 2-point, and 3-point combined) accounted for 23-26% of the total percentage of explained variance. Two key game-related statistics capable of discriminating between winning and losing game outcomes were field goal percentage and defensive rebounding, accounting for 13.6% and 14.2% of the total percentage of explained variance during the regular season and 11.5% and 14.7% during post-season competitive periods. Discriminant function analysis correctly classified winning and losing game outcomes during the regular and post-season competitive periods in 82.8% and 87.2% of cases, respectively. It becomes more conservative (i.e., fewer field goal attempts, assists, steals, turnovers, and points scored), most likely due to greater defensive pressure. Despite small to moderate effect sizes, the findings suggest that NBA teams' style of play (i.e., tactical strategies) changes when transitioning from the regular to post-season competitive period. The total number of games examined in the present investigation was 3933 (3690 regular season and 243 post-season games). ![]() The data scraping technique was used to obtain publicly available NBA game-related statistics over a three-year span (2016-2019). The purpose of the present study was to examine differences in game-related statistical parameters between National Basketball Association (NBA) regular and post-season competitive periods and to determine which variables have the greatest contribution in discriminating between winning and losing game outcomes.
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