NFL Datasets

NFL Data In Excel Spreadsheets

How NFL Data Works & Who It Is For

BigDataBall gathers football stats, NFL odds, and fantasy data from a wide variety of sources and enriches it in an xlsx file format. Datasets make it possible to perform analytics on NFL data in an Excel spreadsheet at your own pace and skill set.
Bettors and fantasy football players, data journalists, and researchers all make use of our NFL data.

NFL datasets datasets include a wide range of NFL game-by-game team stats & betting odds, detailed information about NFL player game-logs, daily fantasy sports (DFS) salary & fantasy points data, and play-by-play (or drive-by-drive) data. Because of how accurate and reliable these datasets are, all use them all the time. Whether you’re a beginner just starting out with data-driven sports analysis or a seasoned pro, BigDataBall NFL databases in Excel will help you find trends, spot player performance trajectories, figure out how a team performs, and do a lot more. By looking at NFL schedule and box scores data, you can learn a lot about how an NFL team is doing, how each player is different positions doing, and how the game changes in different splits.

What You can do with NFL Datasets

Several analyses can be done on the given information to learn more about the players and games. Here are some ways to look briefly at it:

Analyzing NFL Games

Box scores are a summary of the game’s data, like the scores of each NFL team, the stats of each player, turnovers, penalties, and other important stats. all are in Excel so you can sort/filter or write formulas and do whatever can be done with Excel. Start by looking at team-level stats like total yards gained, passing yards, rushing yards, time of control, and third-down conversion rates. These measures give a general idea of how well a team is doing. Pay attention to how the teams are different to figure out what their strengths and flaws are.

Evaluating Football Players

Group data by positions: Quarterbacks, running backs, wide receivers, and defensive players, and other defensive key roles. Pass completions, rushing, receiving yards, tackles, sacks, interceptions, and fumbles. Compare how players did during the game and how they did on average for the season to find standout efforts or places to improve.

Finding Trends and Patterns

Look at how the game moves and how points are scored. Find out which teams did better in certain situations, such as in the red zone, on third down, or when they turned the ball over. Look for patterns in the way plays were called, strategies were used, or players were used that could have changed the result of the game.

Compare to Past Seasons

Dating back to 2016 season, BigDataBall’s historical NFL box score and odds data makes it possible to compare games played under similar conditions. Find any patterns, strengths, or flaws that teams or players tend to show over and over again. This past information can help you predict how teams will play against each other in the future or measure how far a team has come over time.

Add Advanced Data

Explore metrics in our NFL drive-by-drive data such as:

PLAY  TYPE (Run, Pass, Kickoff, Punt, Field Goal, Extra Point, Quarter End, Two Minute Warning, End of Half, End of Game, No Play, QB Kneel, Spike, Timeout)
YARDS GAINED, TOTAL FUMBLE RECOVERY YARDS, RETURN YARDS, PENALTY  YDS, KICKOFF, PUNT, EXTRA-POINT, or FIEL GOAL DISTANCE, PASS OUTCOME (Complete, Incomplete, Sack), PASS LENGTH, LOCATION, AIR YARDS, YARDS AFTER CATCH (YAC), RUN LOCATION, GAP, RECEPTION, INTERCEPTION, TACKLE, FUMBLE FUMBLE LOST, TOUCHDOWN (Pass/Offensive TD, Rush/Offensive TD, Return/Defensive TD

Make Better Decisions Using Historical NFL Betting Odds Data

The betting odds for each game in the dataset provide a new level to the analysis and can provide insight into how the betting market believes games will play out. NFL betting odds data from the past seasons can be valuable in a variety of ways, including:

Research Power Rankings

– Pre-game odds comparison: Looking at historical odds data allows you to determine how teams were perceived in the past in terms of their strength and likelihood of winning.
– Evaluating underdogs and favorites: By looking at the odds, you can observe when underdogs won and favorites lost, which can help you predict surprising results and potential upsets.

Discover How the NFL Betting Market Works

– Line movement analysis: By comparing the odds at the start and the odds at the finish, you can determine how the market’s sentiment evolved over time. Accidents, weather, and public opinion can all produce large enough changes in the odds to be recognized.
– Public vs. sharp money analysis: Observing how the odds change can help you distinguish between how the public and sharp money affect the market. A large change in the odds just before a game, for example, could indicate that more knowledgeable or professional bettors have entered the market.

Valuable Bet Opportunities

– Comparing odds and outcomes: You can use historical odds data to determine the accuracy of the betting market’s forecasts. You can determine whether the market underrated or overpriced certain teams or players by comparing the suggested probabilities from the odds to the actual results.
– Developing betting strategies: You can utilize historical odds data to develop and test various betting strategies, such as exploiting market biases, identifying successful betting chances, and determining how well different betting systems operate.

Predictive NFL Analytics

– Create your own prediction model by incorporating odds as features: Data about past chances can be used as characteristics in predictive models. Models may be able to generate more accurate forecasts if they consider how the market thinks a game will play out.
Changing performance indicators: By taking into account how good the other team is, changing player or team performance measures based on odds can provide a more realistic representation of their true performance.

These measures give a more detailed picture of how a player or team is doing, and they can help find hidden trends or insights. What’s more interesting is you can add information to BigDataBall datasets from other sources you like: Thanks to universal game/player/play IDs, you can improve your research by adding information from other sources, like injury reports, weather conditions, coaching styles, or player matchups. This all-around method helps to paint a fuller picture of the game and gives the box score statistics more meaning.