NHL Datasets

Filter NHL datasets and in-season plans by class, category, or in-season plan. View samples to see the content and format of the dataset.

* You can sign up for a free account and get the 30% member discount on all of the NHL historical datasets.
* If you have purchased a “season pass” before, claim your 15% renewal discount on season pass plans for NHL in-season datasets.

DatasetSamplePriceAvailable Seasonshf:att:pa_statshf:att:pa_classhf:att:pa_datasethf:att:pa_in-season-plan
NHL 2023-2024 Regular Season Schedule

NHL Sample Schedule

NHL Historical DFS Data - Multiple Seasons

NHL DFS Sample

NHL Historical DFS Data - Single Season

NHL DFS Sample

NHL Historical Play-by-Play Data - Multiple Seasons

NHL Play-by-Play Sample

NHL Historical Play-by-Play Data - Single Season

NHL Play-by-Play Sample

NHL Historical Player Stats Multiple Seasons

NHL Player Sample

NHL Historical Player Stats Single Season

NHL Player Sample

NHL Historical Team Stats Multiple Seasons

NHL Team Sample

NHL Historical Team Stats Single Season

NHL Team Sample

NHL In-Season DFS Data One-Off Pass

NHL DFS Sample

NHL In-Season DFS Data Season Pass

NHL DFS Sample

NHL In-Season Play-by-Play Data - One-Off Pass

NHL Play-by-Play Sample

NHL In-Season Play-by-Play Data - Season Pass

NHL Play-by-Play Sample

NHL In-Season Player Data Season Pass

NHL Player Sample

NHL In-Season Player Stats One-Off Pass

NHL Player Sample

NHL In-Season Team Stats One-Off Pass

NHL Team Sample

NHL In-Season Team Stats Season Pass

NHL Team Sample


NHL Data in Excel Spreadsheets

How NHL Data Works & Who It Is For

There is a wealth of information about NHL hockey games and players, but not all of it is accurate or current. When conducting research or making informed decisions regarding NHL games, it is essential to utilize high-quality NHL data. Having access to reliable and accurate hockey datasets can significantly improve your individual decision-making. Once any of the BigDataBall’s NHL datasets has been into a data analysis tool such as Excel, R, or Google Sheets, you can conduct in-depth analysis and generate insightful visualizations. Statistical methods such as regression analysis and hypothesis testing can be useful when using data to derive conclusions and test hypotheses.

BigDataBall undertakes the job of organizing and compiling extensive NHL hockey data from multiple sources, including betting odds. NHL fans, bettors/fantasy players, data journalists, and academic researchers trust the scrupulously validated and presented Excel spreadsheets containing the collected data. With our NHL datasets, you have access to an abundance of explorable data points. The NHL datasets have been preprocessed and supplemented with external information, such as wagering odds and tournament details, enabling efficient analysis of NHL data.

What You Can Do With NHL Datasets

By analyzing data from previous seasons’ NHL games, it is possible to identify various factors that affect players’ performances, such as their strengths and limitations, head-to-head records, favorable weather conditions, and the type of ice surface on which they are playing. The hockey data provided by BigDataBall enables you to identify patterns and trends in player performances, as certain players may excel under particular conditions. Increase your likelihood of making accurate predictions by analyzing the following variables and patterns.

As you are your own sports data scientist, you need to investigate the factors and events that can impact the outcome of NHL hockey games. The availability of high-quality, enriched NHL datasets is a critical factor in your analysis. These files contain detailed information on many aspects that influence the game of hockey. Let’s look at some of these factors:

Player Analysis

Enriched datasets contain a plethora of player-level performance indicators, such as goals, assists, shots on goal, shooting percentage, goalie save percentage, and others. These statistics aid in evaluating individual player contributions and their impact on the game’s outcome.

Team & Odds Analysis

Our NHL datasets include information on even-strength, power play and shorthanded splits. This information can be used to evaluate a team’s effectiveness in capitalizing on or protecting against special teams scenarios.

Hockey Lineup Data

Understanding a team’s success requires tracking injuries and player lineup information included in play-by-play datasets, allowing researchers to assess how lineup changes effect a team’s performance.

Contextual NHL Data

Enriched datasets contain contextual data such as game location (home or away game), whether the game is being played on a back-to-back night, and rest days between games. Consideration of these elements helps you determine the impact of fatigue, home ice advantage, and other situational variables on game outcomes.

Historical NHL Datasets

We have historical datasets that capture previous team matchups provide useful insights into head-to-head performance, such as win-loss records, goal differentials, and key player matchups. This historical backdrop aids in determining how specific teams perform against one another and whether any patterns emerge.

Using Historical NHL Betting Odds Data Improves Decisions

The betting odds for each game in the dataset add a new level of analysis and can reveal how the betting market expects games to play out. NHL betting odds. Previous seasons can be useful in many ways:

Research Power Rankings

– Pre-game odds comparison: Historical odds data shows how teams were rated in terms of strength and likelihood of victory.
– Evaluating underdogs and favorites: Looking at the odds might help you predict surprises and upsets by showing when underdogs won and favorites lost.

Learn About NHL Betting

– Line movement analysis: Comparing the odds at the start and the odds at the end shows how market sentiment changed. Accidents, weather, and public opinion can impact odds significantly.
– Public vs. sharp money analysis: Watching odds alter can help you discern how the public and sharp money affect the market. A high odds change before a game may suggest that more knowledgeable or professional bettors have entered the market.

Value Bets

– Historical odds data can be used to assess the betting market’s predictions. By comparing odds to results, you may tell if the market undervalued or overvalued teams or players.
– Developing betting strategies: You can leverage market biases, discover profitable betting opportunities, and test different betting systems using historical odds data.

NHL Predictive Analytics

– Create a prediction model using odds: Predictive models can leverage prior chances. Market expectations may help models predict game outcomes.
modifying performance indicators: Taking into account how good the other team is, modifying player or team performance statistics based on odds can better reflect their true performance.

In conclusion; one can use BigDataBall’s NHL datasets to utilize advanced statistical models, and data visualization approaches to find hidden patterns, relationships, and trends. This allows for a more in-depth study of the various aspects and events that have an effect on NHL hockey game outcomes, ultimately improving decision-making for clubs, coaches, and analysts.