DFS Data

DFS data is what you need to build your own DFS model. While daily fantasy being a ‘game of skill’ or not, the modern daily fantasy sports world really needs you to be armed with the DFS data. It’s available at many DFS websites, but when it comes to building your own database, having to do a ton of copy-paste to Excel is a huge hassle every day.

Looking for NBA DFS Data?
We compile DFS game logs which include player minutes, usage rates, fantasy points scoring, positions, salaries for DraftKings, FanDuel, and Yahoo.
Check out historical or in-season DFS datasets for the major sports leagues in the US.

Once you have the NBA DFS data, a significant amount of time plus some data analysis skills are required in an effort to have the following questions answered each day:

  • Do you have a good command of how your DFS site’s scoring is? DraftKings and FanDuel is explained.
  • How to determine value players in the main slate?
  • Are you tracking injury related last-minute opportunities?
  • How many DFS points does the opponent team allow? Measuring defense vs. position on the road & at home?

The key is combining knowledge of player performance, trends, correlations and models to gain an information edge over competitors when selecting your daily fantasy lineups and maximizing your earnings from the contests.
You can easily get a competitive edge with a dataset that includes logs of FTPS, usage rates, salaries and positions:

  • Look for trends in players who exceed or underperform their fantasy point projections based on usage rates and analyze why. Use this to find potential value plays.
  • Identify correlations between fantasy points and usage rates by position. This can help you estimate projected points based on typical usage for each position.
  • Calculate expected fantasy point per dollar ratios for players. Target players projected to return higher fantasy point totals relative to their salary.
  • Build models to project fantasy points based on factors like usage rate, minutes, opponent strength and other stats. Use your models to find players likely to outearn their salary.
  • Analyze usage rates and fantasy points when certain players are out. This can help you predict which teammates tend to benefit the most. Target those players.
  • Look for trends in optimal roster construction across positions and salary tiers. Use this to build balanced, high-upside rosters.
  • Study usage and point trends for back-to-back games, home vs. away, and rest days to factor in fatigue and location advantages.
  • Track your predictions and model performance over time. Continuously refine your methods to improve projection accuracy.
Visitors Interested in DFS Data Also Viewed
Box Score Data
Looking for a starting point to NBA analytics? Check out box score stats & Vegas odds & rest days for past seasons.
Play-by-Play Data
Learn more about having a powerful dataset of NBA play-by-play logs which include shot distance & x,y coordinates.
SportVu Data
Learn more about the player tracking technology that collects spatial data of the ball, players and referees.

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