There is already some data and statistics available in European basketball. To extend the level of insights a box-score offers by a large margin, we provide you with different analytical tools. These can be understood as a second pair of glasses to consult in addition to your basketball knowledge and considerable experience.
For more information either scroll through the different aspects listed below or click on a specific one you´re interested in.
Player Evaluation, Comparison & Development Monitoring || Lineup & Player Synergies || Live In-Game Evaluation || Spatial Shooting Analysis || Scouting Data Management || Roster Fit
Player-Evaluation, Comparison & Development Monitoring
- Evaluate, compare and analyze players in their personal development using metrics to cover different aspects of their game
- Statistics as a second opinion for roster planning, scouting, in-season player evaluation or opposing teams
- Example: Our Player Similarity Model applies artificial intelligence to enhance scouting large player databases and find the player you need. View a demo here…
Lineup & Player Synergies
- Analyzing lineup and player combinations fosters the assessment of collective level performances
- Hidden patterns, positive or negative lineups combinations can be identified
- Example: Which players to best combine in the front court, who is beneficiary to a certain lineup, who is not? How does your team perform if #2 and #4 are on court and #1 and #3 benched?
Spatial Shooting Analysis
- Available data, metrics and visualizations support more detailed information than commonly used approaches (e.g. FG%)
- Offers important insight to shooting behavior, spatial patterns and efficiency
- Example: Shot charts assist to analyze both volume and efficiency of a player from any given spot on the court
Live In-game Evaluation
- Why not step analytics up the court, live, during competition?
- Potential to deliver intuitive data and visualizations to the bench via a personalized app on tablet or during halftime
Scouting Data Management
- Structured data storage are vital for a productive and efficient scouting process
- Databases and scouting systems can be developed to fit individual scouting habits; to allow you adding scouting notes from your smartphone while watching games
Roster Fit
- Tries to use methods from machine learning to predict a fit of players without these players ever having together on the court before
- Using such a method could potentially foster roster scouting and designing rosters or player additions during the season
You can also check out, download or print all these information with our PDF. Click HERE!
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