How to Read Football Statistics - Complete Guide | OwnOdds

How to Read Football Statistics: A Complete, Practical Guide

Understanding football statistics is one of the most powerful ways to improve your betting decisions. Stats don’t guarantee profit, but they help you move from “gut feeling” to informed, probability-based judgment.

This guide will walk you step-by-step from basic stats to advanced metrics, show you how to interpret them, and explain how to apply them directly to betting markets.

---

1. Why Football Statistics Matter for Betting

Most bettors:

• Look at league tables, recent scores, and maybe goal totals.
• Rely heavily on narratives: “This team always scores,” “They’re good at home,” etc.

Serious bettors:

• Use stats to estimate the true strength of teams.
• Translate those estimates into probabilities for match outcomes.
• Identify value by comparing their probabilities with bookmaker odds.

Key idea: Stats are tools, not predictions

Statistics:

• Don’t tell you what will happen in a single match.
• Help you estimate how likely different outcomes are over the long run.

Your goal: Use stats to answer questions like:

• “Is Team A really that strong, or just running hot?”
• “Are this team’s high scores sustainable, or just variance?”
• “Is the market overrating or underrating this team?”

---

2. Start with the Basics: Core Team Statistics

Before diving into advanced analytics, master the fundamentals.

2.1 The League Table – Interpreting It Properly

Typical league table columns:

• P – Played
• W / D / L – Wins, Draws, Losses
• GF / GA – Goals For, Goals Against
• GD – Goal Difference (GF – GA)
• Pts – Points

What most people do: Look at points and say “top teams are good, bottom teams are bad.”

What you should do instead:

• Look beyond points.
• Use goal difference and performance vs. expected metrics (later) to check whether points reflect true strength.

Example:

Team A after 10 games:

• Points: 22 (7W, 1D, 2L)
• Goals: 20 scored, 8 conceded (GD +12)

Team B after 10 games:

• Points: 20 (6W, 2D, 2L)
• Goals: 24 scored, 16 conceded (GD +8)

The table says Team A is better (more points). But:

• Team A: solid defense (0.8 conceded per game)
• Team B: more open, higher-scoring matches (1.6 conceded per game)

If you’re evaluating an Over/Under 2.5 goals bet:

• Team B may be better for overs than Team A, even if they have fewer points.

---

2.2 Basic Attack and Defense Stats

Key simple indicators:

• Goals Scored per Game (GF/g)

= Total goals scored / Matches played

• Goals Conceded per Game (GA/g)

= Total goals conceded / Matches played

• Average Goals per Match Involving the Team

= (GF + GA) / Matches played

Example:

After 10 games:

• Team C: GF 10, GA 20 → GF/g = 1.0, GA/g = 2.0, Avg 3.0 goals per match
• Team D: GF 14, GA 6 → GF/g = 1.4, GA/g = 0.6, Avg 2.0 goals per match

Betting applications:

• Team C: Strong candidate for Over 2.5 or BTTS (Both Teams to Score).
• Team D: More suitable for Under 2.5 or “Team D clean sheet” type markets.

2.3 Home vs Away Splits

Teams often perform very differently at home vs away:

• Some rely on home crowd and familiarity.
• Others use counter-attacking styles that work better away.

Always separate:

• Home stats for home matches.
• Away stats for away matches.

Simple method:

For each team, calculate:

• Home GF/g & GA/g
• Away GF/g & GA/g

Then for a given matchup:

• Use home stats of the home team.
• Use away stats of the away team.

Example:

After 8 home games:

• Home Team: 16 GF, 4 GA → 2.0 scored, 0.5 conceded per home game

After 8 away games:

• Away Team: 8 GF, 12 GA → 1.0 scored, 1.5 conceded per away game

This suggests:

• Home Team is strong defensively at home.
• Away Team scores fewer goals away.

For Over/Under 2.5, blindly using total season averages might be misleading. Home/away splits give sharper insight.

---

3. Moving Beyond Goals: Performance Stats

Raw goals are noisy (influenced by luck: missed sitters, goalkeeper heroics, etc.). To better understand team quality, use performance metrics.

3.1 Shots and Shots on Target

Key metrics:

• Shots per Game (SPG)
• Shots on Target per Game (SoT/G)
• Shots Conceded per Game
• SoT Conceded per Game

These tell you:

• How often a team gets into shooting positions.
• How often they allow opponents to shoot.

How to interpret:

• High SPG + low GF → finishing issue or unlucky.
• Low SPG + high GF → clinical finishing or unsustainable hot run.

Example:

Team E (10 matches):

• 19 shots/game, 7 on target, 1.4 goals scored on average.

Team F (10 matches):

• 8 shots/game, 3 on target, 1.5 goals scored on average.

Surface-level (goals): they score roughly the same. Under the hood:

• Team E creates many more chances. Over time, that’s more sustainable.
• Team F may be overperforming (scoring high % of fewer chances), potentially unsustainable.

For outright bets (league winners, top 4), Team E might be more reliable long term despite similar goal numbers.

---

3.2 Possession and Pass Accuracy

Statistics:

• Possession %
• Pass accuracy %
• Passes per game

These indicate style more than quality:

• High possession → more ball control, but not necessarily more goals.
• Low possession → can still be very dangerous on counters.

Betting implications:

• High-possession teams often:
• Face deep defensive blocks.
• Have many shots but not always high-quality chances.
• Counter-attacking teams:
• Lower possession.
• Fewer shots but more space when they attack.

Don’t overrate possession. Combine it with shots, shots on target, and (ideally) xG.

---

4. Advanced Metrics: Expected Goals (xG) and Beyond

To truly read football stats like a pro, you need to understand expected metrics.

4.1 What is Expected Goals (xG)?

Expected Goals (xG) estimates the probability a shot becomes a goal based on:

• Shot location (distance, angle).
• Body part used (head, foot).
• Shot type (open play, penalty, free kick).
• Defensive pressure, etc. (depending on the model).

An xG value:

1. 0.1 xG → 10% chance of scoring.
2. 0.5 xG → 50% chance of scoring.

Team xG for a match:

• Sum of xG for all shots.

Example:

Team G vs Team H:

• Team G: 1 goal from 0.9 xG.
• Team H: 0 goals from 2.3 xG.

Scoreline: 1–0 Performance: Team H actually created better scoring chances.

Key idea: Over a large sample of matches, goals tend to converge toward xG more than they do toward shots or pure narratives.

---

4.2 xG For and xG Against

For each team, track:

• xG For (xGF) – quality of chances created.
• xG Against (xGA) – quality of chances conceded.
• xG Difference (xGD) = xGF – xGA.

Interpretation:

• Positive xGD → likely strong team.
• Negative xGD → likely weak team, even if league position is “ok.”

Example (10 games):

| Team | Goals Scored | Goals Conceded | xGF | xGA | xGD | |------|--------------|----------------|-----|-----|-----| | I | 18 | 8 | 13 | 10 | +3 | | J | 12 | 10 | 16 | 9 | +7 |

From goals alone, Team I looks stronger (GD +10 vs +2). From xG:

• Team I: Running hot (scoring more and conceding less than expected).
• Team J: Underperforming in conversion; underlying stats stronger.

In betting:

• Team I might be overrated by the market.
• Team J might be underrated, potentially offering value.

---

4.3 Using xG in Practice (Step-by-Step)

Step 1 – Gather xG data Use reputable sites (e.g., FBref, Understat, Infogol, etc., depending on league).

Step 2 – Calculate averages (preferably last 8–12 matches):

• Team xGF per game.
• Team xGA per game.
• Separate home/away if possible.

Step 3 – Compare to actual goals

• If goals > xGF consistently → finishing hot or lucky.
• If goals < xGF consistently → underperforming; possible regression.

Step 4 – Translate to betting angle

• Overperforming team vs underperforming team:
• Market often prices heavily based on recent results.
• You can look for value opposing unsustainable runs.

Scenario:

Team K:

• Last 5 matches: 5 wins.
• Goals: 12 scored, 2 conceded.
• xGF: 6.8, xGA: 6.1.

Team L:

• Last 5 matches: 1 win, 2 draws, 2 losses.
• Goals: 4 scored, 7 conceded.
• xGF: 7.1, xGA: 5.8.

Betting public will love Team K. But:

• Team K: xGD over 5 games = +0.7 (good, but not dominant).
• Team L: xGD = +1.3 (actually stronger on underlying performance).

If odds are:

• Team K: 1.80 (implied prob ~55.6%)
• Draw: 3.60
• Team L: 4.50 (implied prob ~22.2%)

The market may be overrating Team K’s recent hot finishing. This is the type of spot where value on Team L (e.g., +0.5 or +1 Asian Handicap) might exist.

---

4.4 Expected Goals on Target (xGOT) and Non-Shot xG

Some advanced models also track:

• xGOT (Expected Goals on Target)

Measures shot placement and goalkeeper reaction. Helps distinguish between:

• Weak shots at the keeper (low xGOT).
• Great finishing to the corners (high xGOT).

• Non-shot xG (NSxG)

Estimates goal probability of possessions even if they don’t lead to shots.

For most bettors:

• xG is enough.
• xGOT and NSxG can refine judgments but are more niche.

---

5. Reading Player Statistics

Team stats tell you system-level performance. Player stats help you:

• Evaluate absences (injuries, suspensions).
• Gauge the impact of key players.
• Spot mispriced markets (goal scorers, assists, cards).

5.1 Goals, Assists, and Underlying Numbers

Don’t just look at:

• Goals
• Assists

Also consider:

• xG per 90 minutes
• xA (expected assists) per 90
• Shots per 90
• Key passes per 90

Example:

Striker A:

• 10 goals in 10 games.
• xG: 6.5.
• Shots: 2.0 / 90.

Striker B:

• 5 goals in 10 games.
• xG: 7.2.
• Shots: 3.5 / 90.

Striker A is overperforming (finishing very well or lucky). Striker B is underperforming (or facing good keepers/bad luck).

For anytime scorer bets:

• If odds are similar, Striker B may be better value because his underlying chance volume is higher.

---

5.2 Defensive and Midfield Contributions

For defenders and midfielders, look at:

• Tackles per 90
• Interceptions per 90
• Clearances per 90
• Aerial duels won
• Progressive passes, passes into final third (for creative roles)

Betting applications:

• Cards markets (Yellow/Red cards):
• Players with many tackles and fouls, especially in high-intensity games, are more likely to be booked.
• Team performance when a key DM (defensive midfielder) is absent:
• Higher xGA and shots conceded → consider opposing team or overs.

---

6. Context: Form, Sample Size, and Schedule

Statistics are powerful only when interpreted correctly.

6.1 Sample Size – How Much Data is Enough?

• Early in the season (3–5 games):
• Stats are highly volatile.
• Use previous season data plus transfers and manager changes.
• Mid-season (10+ games):
• Stats become more reliable.
• Always ask: “Is this trend real, or just noise?”

Practical rule:

• Avoid overreacting to last 2–3 games alone.
• Use rolling windows (e.g., last 8–12 matches) to smooth variance.

---

6.2 Strength of Opposition

A team might look strong statistically because:

• They have played many weak sides.
• They have had many home games.

Check:

• Who they played (top-half vs bottom-half).
• Home vs away distribution.

Example:

Team M:

• Played 10 games: 7 at home, mostly vs lower-table teams.
• High goals scored, strong xG stats.

Team N:

• Played 10 games: 7 away vs strong teams.
• Modest stats, but context is tougher.

Blindly comparing their numbers is misleading. Always adjust for fixture difficulty.

---

6.3 Injuries, Suspensions, and Rotations

Stats assume relatively stable line-ups. When key players are out:

• Offensive xG may fall without the main striker or creator.
• Defensive solidity might drop without the DM or key CB.

Action steps:

• Check team news (injuries/suspensions).
• Identify players crucial to:
• Chance creation (xA, key passes).
• Transition/ball winning (tackles, interceptions).
• Compare team performance with and without those players (if enough data).

---

7. Applying Stats to Specific Betting Markets

Now, let’s connect the statistics to actual bets.

7.1 Match Result (1X2) and Asian Handicap

Goal: Estimate the probabilities of home win, draw, away win.

Basic stat-based approach (simplified):

Step 1 – Estimate expected goals for each side

• Use a blend of:
• Home team’s home xGF and xGA.
• Away team’s away xGF and xGA.

Example (last 10 home/away matches):

• Home Team: xGF home = 1.8, xGA home = 0.9
• Away Team: xGF away = 1.2, xGA away = 1.6

Roughly:

• Expect Home Team to score maybe ~1.7–1.9
• Expect Away Team to score maybe ~1.0–1.3

Step 2 – From expected goals to probabilities (simplified) You can:

• Use Poisson models (advanced, requires some math).
• Or, more simply, say:
• Higher expected goals and stronger xGD → higher probability of winning.

Step 3 – Compare with odds If bookmaker odds (decimal) are:

• Home: 1.85 (implied ~54.1%)
• Draw: 3.60 (~27.8%)
• Away: 4.20 (~23.8%)

You ask:

• Do my stats suggest the home side actually wins more like 60%+?

If yes, the home odds might represent value.

• Or does xGD difference and schedule difficulty say the away team is undervalued? Then maybe take Away +0.5 or +1.0 on Asian Handicap.

---

7.2 Over/Under Goals

You can use:

• Goals per game.
• xG per game.
• Shots and SoT per game.

Step-by-step:

1. Calculate average xG per game for each side (home and away). Example:

• Home Team: home xG = 1.7, xGA = 1.2.
• Away Team: away xG = 1.3, xGA = 1.6.

Rough total expected xG: (1.7 + 1.3 + 1.2 + 1.6) / 2 ≈ 2.9 (We’re averaging both sides’ attack & defense contributions; this is approximate.)

2. Use this to guide Over/Under 2.5 angle.

• Total xG close to 3.0 → lean Over 2.5.
• Total xG near 2.0 → lean Under 2.5.

3. Adjust for context:

• Injuries to key attackers/defenders.
• Style match-up (two high-pressing teams often = more chaos).
• Weather (extreme rain/wind can reduce goals).

---

7.3 Both Teams to Score (BTTS)

Key indicators:

• xGF and xGA for each side.
• Frequency of BTTS in recent matches.
• Whether they are offensively minded but defensively weak.

Example:

• Home Team:
• BTTS in 8 of last 10.
• xGF = 1.6, xGA = 1.4 per game.

• Away Team:
• BTTS in 7 of last 10.
• xGF = 1.4, xGA = 1.5 per game.

Stats say both create and concede consistently. Unless odds are very short, BTTS: Yes is attractive. If bookmaker is pricing BTTS: Yes at 1.80 when your estimation from stats suggests ~60%+ likelihood, you might have value.

---

7.4 Player Props (Anytime Scorer, Shots, Cards)

Anytime Scorer:

• Look at:
• xG per 90.
• Shots per 90.
• Penalty duties.
• Higher xG/90 and penalties = better candidate.

Shots/Shots on Target lines:

• Player’s average shots / SoT per 90.
• Role in the team (focal point or secondary?).
• Opponent’s defensive style (blocks vs allowing long shots).

Cards:

• Player’s fouls per 90 and cards per 90.
• Referee’s average cards per game.
• Match intensity (derbies, relegation battles).

Example: Defensive mid averages:

1. 2.8 tackles, 1.5 fouls, 0.3 yellow cards per 90.

In a derby where both teams press and ref averages 5+ cards, over 0.5 cards at high odds could be reasonable.

---

8. Common Mistakes When Reading Football Stats

Avoid these traps:

• Overreacting to tiny samples
• One or two matches mean almost nothing statistically.

• Ignoring context (opponent quality, home/away, absences)
• Stats are not created equal; who you play matters.

• Using only one metric
• Don’t just use goals or xG alone. Combine:
• Goals + xG + shots + home/away context.

• Ignoring style match-ups
• A possession-heavy team vs a deep block is different from two pressing, open teams.

• Forcing certainty from probabilistic data
• Stats indicate likelihood, not guarantees. Even a 70% favorite loses 3 in 10 times.

---

9. Building a Simple, Actionable Workflow

Here’s a practical system you can follow for each match you consider betting on:

Step 1: Pre-filter Matches

• Skip leagues where you lack data or knowledge.
• Focus on:
• Major leagues with reliable stats.
• Teams you follow regularly.

Step 2: Collect Core Stats

For each match:

• League table position, recent form (last 5–8 matches).
• Goals for/against (overall and home/away).
• xG for/against (overall and home/away).
• Shots & shots on target per game (for and against).

Step 3: Context Check

• Injuries/suspensions (especially key players).
• Schedule congestion (European matches, travel).
• Motivation (title race, relegation battle, dead rubber).
• Weather and pitch conditions if relevant.

Step 4: Form a View on Team Strengths

Answer:

• Who is stronger, and by how much?
• Is the market overrating recent results that differ from xG trends?
• Is there an underlying overperformance or underperformance?

Step 5: Choose Suitable Markets

Match your edge to the market:

• Expect a tight, low-event game? → Unders or + handicap on underdog.
• Expect chances both ways? → BTTS, Overs.
• See finishing regression coming? → Look for value opposing overperforming favorites.

Step 6: Compare to Odds

• Convert odds to implied probabilities:
• Implied probability = 1 / decimal odds.
• Ask:
• “Are my stats-based probabilities higher or lower than the market’s?”
• Only bet where you believe you have value, not just “I think they’ll win.”

Step 7: Record and Review

Keep a simple log:

• Bet type, odds, rationale, key stats.
• Result.
• Over time, analyze:
• Are you overrating certain stats?
• Are certain leagues/markets more profitable for you?

---

10. Practical Example: End-to-End Match Analysis

Let’s walk through a simplified, realistic scenario.

Match: City United vs River Town

Bookmaker odds:

• City United: 1.90
• Draw: 3.60
• River Town: 4.20
• Over 2.5: 1.85
• Under 2.5: 1.95

Step 1: Basic Stats (Last 10 league games)

City United (home):

• Table: 4th.
• GF: 18 (1.8/g), GA: 9 (0.9/g).
• Home GF: 11 in 5 (2.2/g), GA: 4 (0.8/g).

River Town (away):

• Table: 12th.
• GF: 12 (1.2/g), GA: 16 (1.6/g).
• Away GF: 6 in 5 (1.2/g), GA: 9 (1.8/g).

Step 2: Advanced Stats (approximate)

City United:

• xGF: 16.0 (1.6/g)
• xGA: 11.5 (1.15/g)
• xGD: +4.5

River Town:

• xGF: 13.5 (1.35/g)
• xGA: 17.0 (1.7/g)
• xGD: –3.5

Observation:

• City United have slightly overperformed in goals vs xG (18 vs 16).
• River Town roughly in line or slightly underperformed.

Step 3: Context

• City United:
• Played midweek in Europe (travel, some fatigue).
• Main striker fit, creative midfielder slightly doubtful.
• River Town:
• Full week of rest.
• Key defensive midfielder suspended (big for defense).

Step 4: Interpret

• City United:
• Strong at home, slightly overperforming but still good.
• Potential minor fatigue, but deep squad.
• River Town:
• Concede many chances (xGA high), especially away.
• Missing DM likely increases vulnerability.

Expected goals:

• City United home attack vs weakened River defense:
• Reasonably expect ~1.8–2.1 xG.
• River Town attack vs City United defense:
• Maybe ~1.0–1.2 xG (City solid, but some rotation/fatigue possible).

Total expected xG: ~2.8–3.3 → leans toward Over 2.5.

Step 5: Betting Angles

• Match result (1X2)
• Odds imply City United ~52.6% (1 / 1.90).
• Based on xG and home advantage, you might estimate:
• City ~58–60%
• Draw ~22–24%
• River ~18–20%
• This suggests slight value on City United win if your assumptions are sound.

• Totals (Over/Under 2.5)
• With expected xG per match around 3.0 and River’s defensive midfielder out, Over 2.5 at 1.85 (implied ~54%) could be reasonable if you estimate ~60%+ chance of 3+ goals.

• BTTS
• City United rarely concede at home, but River’s attack is not terrible.
• xG suggests River could get 1 goal often enough, but this might be less clear value than Over 2.5 (which benefits from City possibly scoring 3 alone).

From here, you:

• Decide which angle (City win, Over 2.5, maybe City & Over) offers the clearest edge.
• Stake appropriately and record the rationale.

---

11. Final Thoughts and Next Steps

To read football statistics effectively for betting:

• Master the basics
• Goals, shots, home/away, recent form.

• Use advanced metrics intelligently
• xG and xGD to see beyond the surface.
• Player-level xG/xA to judge key absences and props.

• Always add context
• Opponent strength, sample size, schedule, injuries, style match-ups.

• Connect stats to specific markets
• Use what the numbers tell you to pick the right bet type:
• 1X2 / Asian Handicaps.
• Over/Under.
• BTTS.
• Player props.

• Think in probabilities, not certainties
• Stats help you find value, not guaranteed winners.

If you’d like, I can:

• Walk through a real upcoming match with you using live stats.
• Or help you build a simple spreadsheet template to track and interpret these statistics consistently.