Choose Your Tools
Pick the tools that work best on your device. The skill matters more than the software. Chromebook, Windows, Mac and iPad users can all complete this activity successfully.
Use this to build your player data comparison table and performance metrics analysis in Steps 1 to 3.
Use this for your written scouting report and data ethics analysis.
Use these for player statistics, performance data and sports analytics research.
The key conceptual challenge in this series is helping learners understand that metrics like xG are probabilistic models, not measurements. xG does not measure how good a shot was; it measures the historical probability of scoring from that position and context based on thousands of previous similar shots. This distinction matters for evaluating what data can and cannot tell us. The FBref and WhoScored databases are genuinely free and extremely detailed, and learners can explore real player data for any current professional player.
Strong responses will distinguish between descriptive statistics (what happened) and predictive or evaluative metrics (what the data suggests about quality). Look for learners who understand that a player with high xG but low actual goals may be unlucky or may lack finishing quality, and that deciding which interpretation is correct requires watching the player, not just reading the numbers.
Support: Focus on xG and progressive passes only. Provide a pre-selected player profile from FBref with a simplified data reading guide. Extension: Research how Liverpool FC's data analytics department, founded partly by Ian Graham, contributed to their Premier League title wins and evaluate the specific decisions it influenced.
