The best research tool is one you build yourself. A custom dashboard lets you track exactly the metrics you care about, analyse matches your way, and build historical records for pattern identification.
This guide walks you through building a football stats dashboard using spreadsheets and free data sources.
Data Sources
FBref (Sports Reference): Free access to xG, xGA, possession, shooting stats, defensive metrics. Download data as CSV.
WhoScored: Free browsing of Opta data. Screenshots or manual entry for specific metrics.
Understat: Detailed shot data with some free access.
League official websites: Team stats, fixtures, results.
Basic Dashboard Structure
Section 1: Team Summary Stats
Create a table with these columns for each team:
- Team name
- Matches played
- Goals scored
- Goals conceded
- xG (average per match)
- xGA (average per match)
- Possession (average)
- Shots per match
- PPDA (lower = more aggressive pressing)
- Form (W-D-L last 5 matches)
- League position
- xPts (expected points based on metrics)
This table becomes your quick reference. Update weekly and sort by different columns to spot patterns.
Section 2: Match Analysis
For upcoming matches you're considering betting on:
| Team A | Team B | xG Diff | Expected | Bookmaker | Edge | Decision |
|---|---|---|---|---|---|---|
| Liverpool | Everton | +0.6 xG | 58% | 1.75 | +6% | Value |
Calculate expected win probability using Poisson or your own model. Compare to bookmaker odds. Identify edge (if any).
Section 3: Historical Record
Track your betting decisions and outcomes:
| Date | Match | Bet | Odds | Result | ROI |
|---|
This becomes invaluable for identifying which strategies work and where your analysis is strong or weak.
Section 4: Trend Analysis
Charts showing:
- Each team's xG trend across season
- xGA trend
- Points vs xPts divergence
- Home vs away performance
- Form trends
These visualizations help spot patterns that raw numbers hide.
Building in Google Sheets
Google Sheets is free and works well for collaborative dashboards:
- Create a new spreadsheet
- Set up sheets (tabs) for each component
- Use IMPORTHTML or QUERY functions to pull data from public sources
- Create pivot tables for summary analysis
- Build charts for visualisation
Example formula to import FBref data:
=IMPORTHTML("https://fbref.com/en/comps/9/2024-2025/2024-2025-Premier-League-Stats", "table", 1)
This pulls the Premier League table directly. Adjust URL for other leagues.
Building in Excel
Excel offers more power but less ease of online collaboration:
- Create workbooks for each league
- Manually enter or import data
- Use formulas to calculate derived metrics
- Build pivot tables for analysis
- Create charts and dashboards
Excel's formula power allows complex calculations. Vlookup can match teams across sheets. Index/Match allows flexible data lookup.
Metrics to Track
Essential
- xG and xGA (most important metrics)
- Actual goals for and against
- xPts vs actual points
- Form (W-D-L last 5 and last 10 matches)
- Home and away records separately
Important
- PPDA (pressing intensity)
- Shots per match
- Pass completion %
- Possession %
- Head-to-head record against opponent
Advanced (if you have data)
- Expected Threat (xT)
- Expected Assists (xA)
- Pressure success rate
- Dribble success %
- Key passes per match
Updating Your Dashboard
Weekly: Update after matchweek finishes
- New results
- Recalculate rolling averages (last 5 and 10 matches)
- Update xPts with new matches
- Note injuries and tactical changes
Monthly: Review trends
- Identify teams outperforming/underperforming xPts
- Spot form reversals
- Note managerial changes
- Adjust upcoming match predictions
Seasonally: Rebuild
- Archive previous data
- Begin fresh season tracking
- Update league-specific benchmarks
Automating Updates
Several tools reduce manual work:
Zapier: Automatically posts FBref updates to your spreadsheet.
Python scripts: For tech-savvy users, scrape FBref or other sites and populate spreadsheets automatically.
IFTTT (If This Then That): Trigger actions based on football data changes.
Most bettors manually update weekly. It's only 15-30 minutes and forces you to review data properly.
Spotting Patterns
Once your dashboard has 10-20 weeks of data, patterns emerge:
- Teams consistently outperforming xPts
- Defensive improvements or declines
- Form reversals before they fully manifest
- Home advantage variation by team
- Fixture difficulty impacts
These patterns become your edge.
Common Dashboard Mistakes
Tracking too many metrics: Focus on 5-10 core ones. Too much data paralyses decision-making.
Not updating consistently: A stale dashboard is useless. Weekly updates are essential.
Trusting single data points: One match isn't a trend. Use rolling averages (5 and 10 matches).
Forgetting context: A team's xG of 0.8 means something different against Manchester City than against Luton Town.
Ignoring external factors: Injuries, suspensions, and tactical changes don't show in stats. Add notes.
Using Your Dashboard
Before betting:
- Check both teams' recent xG trends
- Compare expected win probability to bookmaker odds
- Review head-to-head data and form
- Note any external factors (injuries, suspensions, weather)
- Only bet where clear edge appears
In Summary
- A personal football statistics dashboard takes 2-3 hours to build initially, then 15-30 minutes weekly to maintain.
- It provides enormous value by forcing disciplined analysis and creating historical record of your decision-making.
- Start simple with basic xG, possession, and form data.
- Expand as you grow more sophisticated.
- Build in whichever tool suits you (Google Sheets or Excel).
- Update consistently.
- Your edge comes from data-driven decision-making, and the dashboard enables this process.
FAQs
How much coding knowledge do I need to build a dashboard? None. Google Sheets or Excel with basic formulas (SUM, AVERAGE, IF) are sufficient. Advanced automation requires coding but is optional.
Which league should I start with? Begin with one league (Premier League is easiest due to free data availability). Once proficient, add others. Specialisation beats breadth.
How often should I update my dashboard? Weekly after matchweek finishes is ideal. Monthly at minimum. More frequent updates are useful but not necessary.
What's the most important metric to track? xG and xGA. Everything else is secondary. If you track only those, you still have strong foundation.
Should I track individual player stats in my dashboard? Optional. Team metrics matter more for match predictions. Add player stats if you're betting player props.
Can I publish my dashboard online? If using Google Sheets, yes. Just ensure you're comfortable sharing it publicly. Excel dashboards are harder to share but possible through cloud storage.