Data-Driven Dominance: Enhance Your Sports Team’s Strategy
Discover How Rolling Insights Transforms Raw Data into Winning Strategies
Introduction
Have you ever wondered if you could use data analytics in sports? Imagine being able to use data to set an accurate, updated strategy for your fantasy sports team. By analyzing historical team performance, you can make informed decisions and get the best value for your efforts. Rolling Insights is where fantasy sports data meets insights, offering tools to make this process easy and efficient.
Enter Sports Wise by Rolling Insights
Using data analytics in sports is easier than you might think. With Rolling Insights, you can access season stats, team info, league matchups, historical data, depth charts, player injuries, player info, and player stats — all without updating spreadsheets manually. This allows you to spend more time enjoying the game and less time managing data.
For example, Rolling Insights, a team based in Alberta, Canada, specializes in sports analytics. They use advanced techniques to analyze sports data, providing valuable insights that can help improve team performance.
- Choose Your Data: Connect your Yahoo! Fantasy League or import your roster for instant insights. Get daily injury updates, schedules, and depth charts.
- Create DataSpaces: Easily create data relationships without needing complex formulas or advanced database knowledge. Dive deep into your data with endlessly customizable DataSpaces.
- Get Automatic Updates: Receive daily insights on player statistics, injuries, and matchups. Build calculations to score your players and create customized weighting models.
- Easy Import and Export: Save time by exporting your DataSpace to Excel or any other application, making data management seamless.
Four Use Cases
Case 1 | NBA Player Performance per season 2018 to ‘23
In the NBA, you can measure important metrics such as Effective Field Goal Percentage (EFG) and True Shooting Percentage (TS%) and other custom metrics you want to measure. With historical data readily available, you can easily compare your favorite teams and players. By filtering data by season and team, you can find the best matchups and performance combinations.
Sample sports analytics questions
- How have the True Shooting Percentage (TS%) and Effective Field Goal Percentage (EFG%) of top NBA players changed from 2018 to 2023?
- Which player had the highest TS% in the 2020 season, and how does it compare to their performance in other seasons?
- How does the EFG% of a specific team correlate with their win-loss record over the seasons from 2018 to 2023?
- What are the trends in league-wide TS% and EFG% from 2018 to 2023?
This dataspace analyzes the performance from previous years (2018 to 2023). We created a custom calculated field called ‘(2023) Overall Perc’ to measure their performance in a particular year. There are about 20+ calculated fields. We also added two important metrics: True Shooting Percentage and Effective Field Goal Percentage.
Case 2 | NBA and NHL Team Performance Analysis for 2022
For team performance, you can analyze both offensive and defensive metrics. Using machine learning, you can create clusters and models to visualize your data. Tools like ring charts and radar charts can help you perform detailed analyses, comparing each season’s performance against similar teams within the same conference.
Sample sports analytics questions
- What are the common characteristics of the five identified team clusters in terms of offensive and defensive metrics for the NBA 2022 season?
- Which teams balance both strong offensive and defensive metrics effectively, and how does this balance impact their win-loss record?
- How do offensive metrics (e.g., FT%, 2PTS%, 3PTS%) correlate with defensive stats (e.g., steals, rebounds) in determining overall team performance for NBA teams in 2022?
For this dataset we added FT%, 2PTS% and 3PTS% for each team.
Using Google Colab, we used Plotly to visualize metrics such as shooting performance, defensive stats, steals, and rebounds, and we were able to identify at least five team clusters.
Applying a similar approach to NHL Teams…
This can be useful in analyzing common patterns in team performance in terms of each key metric. The tools and visualizations could also be applied to other sports and teams, including an analysis of NFL team clusters based on performance.
Case 3 | NHL Player Metrics for 2023
In the NHL, you can compare player metrics within the same team or against similar players from other teams. Predicting future performance becomes easier by measuring player points relative to overall team points. You can also compare team performances, such as the Flames versus the Oilers, to gain insights for the upcoming season.
Sample sports analytics questions
- How does each player’s performance in terms of assists, blocks, saves, and faceoff wins contribute to the overall team performance in the 2023 NHL season?
- How do the top players from the Calgary Flames compare to those from the Edmonton Oilers in terms of blocks, assists, saves, and faceoff wins for the 2023 season?
- How does the distribution of individual player contributions (blocks, saves, assists, faceoff wins) differ among the top-performing teams in the 2023 NHL season?
This data space contains NHL 2023 individual player performance statistics, including assists, wins, and losses. We have calculated each individual’s performance over the team total performance ratio for analysis in terms of blocks (BKS), saves, assists (ASSISTs), and faceoff wins (FOW).
We have calculated each individual’s performance over the team total performance ratio for analysis in terms of blocks (BKS), saves, assists (ASSISTs), and faceoff wins (FOW).
Case 4: NFL Player Injuries and Performance
This dataspace provides valuable insights into player information and their injuries within the NFL. It includes essential player attributes such as weight, height, as well as detailed injury data including the location of injury and the expected return date.
As of the current dataset from 2023, we have a comprehensive set of columns detailing the dates and durations of injuries. By analyzing this rich dataset, we can gain a better understanding of injury trends and potentially predict the likelihood of injuries in the upcoming season.
The dataset contains player names, positions, physical attributes, and specific injury details. By joining information based on player names, we can extract valuable insights into player health and performance.
Sample sports analytics questions
- What are the top 10 most common injuries among NFL players in the 2023 season, and how do they vary by player position?
- Is there a correlation between player attributes (height, weight, age) and the likelihood of specific injuries?
- Can we predict the likelihood of future injuries based on historical injury data and current player attributes?
Data space containing NFL 2023 data about players, their teams, height, weight, DOB, and injuries. We also include player performance metrics such as Rush — ATT, Pass — TDs, and INT.
This dataspace can help in the analysis of finding patterns between injuries and height, weight, age, and player metrics.
Using NFL Injuries and Player performance information we found out the top 10 most common injurries, we also identified some clusters based on player position category, their attributes and injury occurrence
Summary
Data analytics in sports offers a wealth of opportunities to improve team performance and make informed decisions. Rolling Insights provides a no-code, customizable platform that makes this process easy. Whether it’s the NBA, NHL, or any other sport, you can use historical data and advanced analytical techniques to uncover valuable insights. Stop updating spreadsheets manually and start optimizing your league for free today with Rolling Insights.
References
Rolling Insights. https://rolling-insights.com/
Effective Field Goal. NBA Advanced Stats: eFG%
True Shooting. Fantasy basketball: What True Shooting Percentage reveals
