college basketball betting database schema

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College basketball betting database schema 6 1 each way betting

College basketball betting database schema

Atlantic, Central, Southeast, etc. Los Angeles, Philadelphia, Boston, Chicago, etc. Name string 50 Yes No Yes The mascot of the team e. Lakers, 76ers, Celtics, Bulls, etc. Tier 1: Scores Game. Status string 20 Yes No Yes Indicates the game's status. Quarter string 10 Yes No Yes The current quarter in the game. This is updated once daily at AM ET. This is for display purposes and does not include corresponding data points. IsClosed boolean No No Yes Indicates whether the game is over and the final score has been verified and closed out.

Tier 2: Team Stats OpponentSeason. Tier 3: Player Stats BoxScore. InjuryNotes from string Yes Yes Yes Brief description of the player's injury and expected availability. OpponentRank from integer 32 Yes Yes Yes The ranking of the player's opponent with regards to fantasy points allowed.

OpponentPositionRank from integer 32 Yes Yes Yes The ranking of the player's opponent by position with regards to fantasy points allowed. Tier 4: Play-By-Play Game. NBA v3 Play-by-Play. Sequence from integer 32 No No Yes The order in which this play happened over the course of the game. PotentialPoints from integer 32 Yes Yes Yes The points that would have been potentially scored by the shot attempt if any.

Points from integer 32 Yes Yes Yes The points scores by the shot attempt if any. Category from string 20 Yes Yes Yes The category the play. Description from string Yes Yes Yes The description of the play. Created from datetime Yes No Yes The database generated timestamp of when this Play was first created. Fantasy DfsSlate. NBA v3 Projections. Operator string 20 Yes No Yes The name of the operator who is running contests for this slate.

Will often be null as most operators only have one game type. This points to data in the respective sports' player feeds. This points to data in the respective sports' projected player game stats feeds. OperatorPosition string 10 Yes No Yes The player's eligible positions for the contest assigned by the operator.

OperatorRosterSlots string[] Yes No Yes The player's eligible positions to be played in the contest assigned by the operator. This would include UTIL, etc plays for those that are eligible. Team string 12 Yes No Yes Name of the team the player belongs to. Indicates the player's status of being on an active roster.

Possible values include: Active, Inactive. The player's position category. Possible values: C, F, G. The player's primary position. The number of years experience the player has in the NBA. The player's cross reference PlayerID to the Rotoworld news feed. Indicates the player's injury status. Possible values include: Probable, Questionable, Doubtful, Out.

The body part that is injured Knee, Groin, Calf, Hamstring, etc. Brief description of the player's injury and expected availability. The player's full name in FanDuel's daily fantasy sports platform. The player's full name in DraftKings' daily fantasy sports platform.

The player's full name in Yahoo's daily fantasy sports platform. A globally unique ID for this team. The player's full name in FantasyDraft's daily fantasy sports platform. The last updated date of the player's headshot as provided by USA Today. The last updated date of the player's transparent background headshot as provided by USA Today.

The description of the season for display purposes possible values include: , , etc. The string to pass into subsequent API calls in the season parameter. Whether or not this stadium is the home venue for an active team. The division of the team e. The mascot of the team e. The league of the team possible values: Eastern, Western. The conference of the team possible values: Eastern, Western. The team's primary color. This is not licensed for public or commercial use. The team's secondary color.

The team's tertiary color. The team's quaternary color. The link to the team's logo hosted on Wikipedia. The link to the team's wordmark logo hosted on Wikipedia. Indicates the game's status. The timestamp of when the record was last updated US Eastern Time. The current quarter in the game. PointSpread from The oddsmaker Point Spread at game start from the perspective of the HomeTeam negative numbers indicate the HomeTeam is favored, positive numbers indicate the AwayTeam is favored.

OverUnder from AwayTeamMoneyLine from Money line from the perspective of the away team. HomeTeamMoneyLine from Money line from the perspective of the home team. A globally unique ID for this game. A globally unique ID for the away team. A globally unique ID for the home team. The money line payout odds when betting on the away team with the point spread. The money line payout odds when betting on the home team with the point spread.

Indicates whether the game is over and the final score has been verified and closed out. The details of the quarters including overtime periods for this game. NeutralVenue from OverPayout from UnderPayout from The Number Order of the Quarter in the scope of the Game. The total points scored by the away team in this Quarter. The total points scored by the home team in this Quarter. Indicates which position is included in opponent stats that are aggregated together. The aggregated season stats for this team's opponents.

Indicates whether the game is over and the stats for this player have been verified and closed out. Indicates whether player is starting, active, or inactive. A globally unique ID for this opponent. The details of the quarters associated with this box score.

Unique ID assigned to each player that stays with them throughout their career. The player's position associated with the given game or season. FanDuelSalary from The player's salary for FanDuel daily fantasy contests.

DraftKingsSalary from The player's salary for DraftKings daily fantasy contests. FantasyDataSalary from The player's salary as calculated by FantasyData. YahooSalary from The player's salary for Yahoo daily fantasy contests. InjuryStatus from Winning picks from SDQL masters. About this site Sports box scores provide numerical information about a sporting event: SportsDatabase.

A list of available parameters is give on each query page and you can see the temporal extent of each database using the SDQL: season. Once you have created an account you can enter and share other data fields such as city population and offensive coordinator. Members can also save and share trends and SDQL short cuts. Membership is free and you only need to enter a user name and password in the upper right to create an account.

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For example, say the New England Patriots have covered in nine of their past 10 games as touchdown favorites. Does New England going against the spread tell us anything about what will happen in the future when they are favored by seven or more points again? The size of the sample helps determine the level of confidence we have in our systems. The general rule of thumb is the more results larger sample a winning system has the more likely it is to succeed long term.

An appropriate sample size varies by sport. In football, with only 16 games played each season by a team, an acceptable sample size will be smaller than for a sport like baseball with its game schedule. A common mistake when building a betting system is unearthing a trend that has a high win rate and return on investment but failing to check for consistency.

The hallmark of a winning betting system is consistent winning results year-to-year. When building a betting system remember to start with a theory, have a large sample size and consistent year-over-year results. If a betting system meets this criteria we can have confidence it will continue to be profitable going forward. Sports Betting. Best Books. John Ewing. Download App. Top Offers. Bet Over 0 in Lakers-Nuggets. Follow Us On Social. Team Game Finder Search through box score and advanced game statistics spanning from to today that match your criteria.

Player Game Finder Search through box score and advanced game statistics spanning from to today that match your criteria. Question, Comment, Feedback, or Correction? Are you a Stathead, too? Subscribe to our Free Newsletter. This Month in Sports Reference Find out when we add a feature or make a change. Do you have a sports website? Or write about sports? We have tools and resources that can help you use sports data. Find out more.

We present them here for purely educational purposes. Our reasoning for presenting offensive logos. Logos were compiled by the amazing SportsLogos. All rights reserved. College Basketball Stats and History The complete source for current and historical college basketball players, schools, scores and leaders.

Non-Division I games are excluded from the ratings. Bonaventure St.

Winning picks from SDQL masters.

Betting generator 56
College basketball betting database schema TeamID from The PlayerID of the player who is involved in the play college basketball betting database schema the away team. Tier 4: Play-By-Play Game. Not only are the databases more thorough than ever before, but they are also more user-friendly and readily available to the general public. Description from string Yes Yes Yes The description of the play.
Ladbrokes betting slip expiry corporation If a team reaches the double bonus again, the double bonus attribute will reappear with a value of true. For neutral-site games, we set the neutral-site value college basketball betting database schema "true". Share and grow your knowledge of the SDQL at the discussion group. It impacts how much time the QB has to make plays and how much room the RBs have to find holes. Consider focusing on the first-half of games as this is when most teams are trying to stick to their original game plan whereas teams will change and make adjustments at halftime depending on whether they are winning or losing.

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The databases were established primarily for betting and contest purposes to give handicappers a huge archive that they could sort through. This allows everyone to search their own angles and find their own trends and not rely on websites that simply display matchup reports and trends without any context.

What line do we use in the database? It is intended as a fair, representative line of what bettors would have gotten before game time. It does not track opening lines or line moves , so if you bet the Patriots at -7 on a Tuesday and the line closed at As well, the database also allows you to search for results by teams before or after their bye week.

For baseball, the database contains extensive starting pitcher data, so you can find out how Clayton Kershaw has fared in his last 10 starts at home against the San Francisco Giants, or how David Price has performed on the road in his last 10 starts in the month of June. College sports bettors will find the College Football Database and College Basketball Database both invaluable tools, with the college hoops version adding the extra wrinkle of March Madness results so that you can find out how Duke has fared on the betting lines in its last 10 games in the third round of the NCAA Tournament.

Finally, the CFL Database and the WNBA Database give bettors the opportunity to dig up their own trends on a couple of less popular leagues — and find the bankroll-boosting angle. Need more winning picks? The handicapping, sports odds information contained on this website is for entertainment purposes only. Please confirm the wagering regulations in your jurisdiction as they vary from state to state, province to province and country to country. Using this information to contravene any law or statute is prohibited.

The site is not associated with nor is it endorsed by any professional or collegiate league, association or team. OddsShark does not target an audience under the age of Here are three steps to follow to create your winning systems:. All good betting systems begin with an idea or hypothesis.

After you know the question you can mine the data to find the answer. It is important to have an underlying theory to explain why a betting system is successful. There is an incredible amount of data that exists in sports. Data comes from individual player performance, coaching, in-game events, betting lines, officiating and weather. We can extract useful information from that data to make predictions about future outcomes.

However, one of the most common mistakes made by bettors is making definitive claims from small samples of data. For example, say the New England Patriots have covered in nine of their past 10 games as touchdown favorites. Does New England going against the spread tell us anything about what will happen in the future when they are favored by seven or more points again? The size of the sample helps determine the level of confidence we have in our systems.

The general rule of thumb is the more results larger sample a winning system has the more likely it is to succeed long term. An appropriate sample size varies by sport. In football, with only 16 games played each season by a team, an acceptable sample size will be smaller than for a sport like baseball with its game schedule.

A common mistake when building a betting system is unearthing a trend that has a high win rate and return on investment but failing to check for consistency. The hallmark of a winning betting system is consistent winning results year-to-year. When building a betting system remember to start with a theory, have a large sample size and consistent year-over-year results.