Here is a sample of the game-by-game data libraries you will receive when you subscribe to a Vegas Plays package. Viewing this data in tandem with the Entropy predictions is an excellent way to augment your capping skills.

As a subscriber, you get access to a file for every upcoming game in the NFL, NBA, college football, and college basketball. Files are updated nightly.

This is some heavy-duty stuff. You won't find equivalent material anywhere.

Sample game data, upcoming Week 12 college football; click on a game to view:

Ball State at Northern Illinois
Western Michigan at Miami (Ohio)
Ohio at Bowling Green
North Carolina at Virginia Tech
Marshall at Memphis

Data legend:

• MoV = Margin of Victory (negative values mean the team was outscored on average).
• last_5_delta = how much better or worse the team has played in its last five games compared to their rating for the entire season. For football this is calculated for the last 3 games (so "last_3_delta" instead).
• The "O/U" stat lists how many times the team has gone over and under on totals for the current season.
• Schedule strengths and margins of victory are for the current season's schedule played to present. If the team has not yet played a home, road, or neutral game, then the respective values are from the previous season.
• A "vs" instead of an "at" denotes a game on a neutral field/court.
• The "similar games in recent history" section shows past games where the teams involved had power ratings similar to the teams in question.
• The average ATS (against the spread) differentials are relative to home teams and favorites. In the home/road results section, if the number is positive, then the home teams covered (on average), while if the differential is negative, then the road teams covered (on average). Likewise in the favorites/underdogs summaries - positive for favorites, negative for dogs. The differential is the amount by which the teams covered, or failed to cover, on average.

The time-weighted statistics put more weight on more recent results.

It's quite simple, but takes a few looks to become familiar. Check back in the future for new samples. I think you'll become addicted.

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