A team takes 18 shots but scores only once. Another team takes eight shots and scores three.
Which attack was better? The answer depends heavily on where those shots came from and how good the opportunities actually were.
This is where big chances become useful. Instead of treating every attempt equally, big-chance statistics try to identify situations where a player would normally be expected to have a very good opportunity to score.
A one-on-one with the goalkeeper matters much more than a hopeful shot from 30 metres. For football goal predictions, this can help you distinguish between teams that simply shoot often and teams that regularly create genuinely dangerous opportunities.
But like every football statistic, big chances need context. They work best alongside xG, shots on target, finishing, defensive data and opponent strength.
What Is a Big Chance in Football?
Opta defines a big chance as a situation where a player should reasonably be expected to score. Typical examples include:
A one-on-one with the goalkeeper
A close-range shot with a clear view of goal
An opportunity with little defensive pressure
A penalty
Penalties are always classified as big chances under Opta’s definition. The important phrase is: reasonably expected to score.
A big chance is not simply any shot inside the penalty area. A difficult header under pressure from 14 metres may not qualify.
A striker receiving a square pass five metres from goal with the goalkeeper out of position probably will.
Big Chances Are About Quality, Not Quantity
Imagine two teams. Team A has 15 shots, 3 shots on target, and 0 big chances.
Team B has 8 shots, 5 shots on target, and 4 big chances. Team A has almost twice as many attempts.
But Team B has created far better opportunities. If you only looked at total shots, Team A might appear more dangerous.
Big-chance data tells a different story. For goal predictions, Team B’s attacking performance may be much more encouraging.
Why Big Chances Can Be More Useful Than Total Shots
Shots can be misleading because football teams shoot from very different areas. Suppose a team has 20 attempts.
Ten come from outside the penalty area, six are blocked, and only three are on target. There may have been plenty of attacking activity without much genuine threat.
Now imagine another team takes only ten shots but creates five big chances, including three one-on-ones and two close-range finishes. The second team has probably created the stronger scoring environment.
That is why big chances are particularly useful when analysing:
Over 2.5 Goals
BTTS
Team Total Goals
Player goalscorer markets
Big Chances vs xG: What Is the Difference?
Big chances and expected goals are related, but they are not the same statistic. A big chance is essentially a yes-or-no classification.
The opportunity either qualifies as a big chance or it does not. xG gives each shot a probability between zero and one based on the quality of the opportunity.
Opta’s xG model uses historical information from nearly one million shots to estimate how likely a chance is to become a goal. For example, Shot A is 0.08 xG, Shot B is 0.22 xG, and Shot C is 0.65 xG.
Shot C is clearly much better. A big-chance statistic may classify Shot C simply as: Big chance: Yes.
So xG provides more detail. Big chances provide a simpler way of identifying clear opportunities.
Why You Should Use Both
Imagine two teams each record 1.8 xG. But the chance profiles are different.
Team A has 10 moderate-quality attempts and no clear one-on-one opportunities. Team B has three big chances and several low-quality attempts.
Their total xG is similar. But Team B has repeatedly managed to break the defence and produce clear scoring situations.
That tactical information can be useful. The strongest analysis usually asks: Is the xG high because the team creates clear chances, or because it takes a very large number of average shots?
Big Chances Created Can Reveal Attacking Quality
A team regularly producing big chances is usually doing something right. It may be:
Breaking defensive lines
Reaching the byline
Producing cutbacks
Finding runners behind defenders
Creating one-on-ones
Winning penalties
Those actions are often more repeatable than speculative long-range shooting. For example, Manchester City’s Erling Haaland had received 36 big chances by the point of one Premier League statistical review during the 2025/26 season, considerably more than several other leading forwards.
That illustrates how an elite attacking system can repeatedly place its striker in high-quality scoring positions rather than relying only on difficult individual finishes. For team-goal predictions, repeated big-chance creation is generally a healthier sign than simply having a high shot count.
Big Chances Conceded Matter Just as Much
Do not look only at attacking numbers. A defence that repeatedly gives away big chances is a major warning sign.
Suppose Team A concedes 15 shots per match but only 1.2 big chances. Team B concedes 10 shots per match but 3.0 big chances.
Team B actually allows fewer attempts. But when opponents do break through, the chances are much more dangerous.
For Over 2.5 or opponent-team-total predictions, Team B may be the more vulnerable defence.
Practical Example: Big Chances Strengthen Over 2.5
Imagine a matchup where the home team creates 2.0 big chances per match, with 1.8 xG and 5.5 shots on target. The away team creates 1.7 big chances per match, with 1.5 xG and 4.8 shots on target.
Defensively, both teams also concede clear opportunities. The home side allows 1.8 big chances, and the away side allows 2.1.
Now several independent indicators support goals:
Good xG
Strong shots-on-target numbers
Repeated big-chance creation
Vulnerable defences
Possible scores include: 2-1, 2-2, 3-1. Big-chance data strengthens the Over 2.5 case.
Practical Example: High Shot Count but Weak Goal Prediction
Now imagine Team A averages 17 shots per match, which sounds excellent. But only 3.2 are on target, only 0.8 are big chances, and xG is 1.1.
The team shoots constantly but from poor positions. Backing Team A Over 1.5 Goals purely because it produces 17 shots could be misleading.
The quality statistics suggest the attack is less dangerous than the raw volume implies.
Big Chances Missed Can Be Misunderstood
People often treat missed big chances as automatically negative. For example: “The striker missed three big chances. He is terrible.”
That is not always the right conclusion. There are two separate questions:
Did the player finish the chances?
Did the player get into excellent scoring positions?
A striker repeatedly receiving big chances may actually have very strong underlying attacking numbers. The finishing may improve later.
Opta distinguishes a big-chance opportunity from a “big chance missed,” including situations where a player fails to get an attempt away from a clear opportunity. So missed big chances can sometimes be a positive predictive signal for future goals—as long as the player continues getting those opportunities.
Do Not Assume Every Miss Means Goals Are “Due”
This requires caution. Suppose a striker has 7 big chances missed in five matches.
You might think he must score soon. Football does not work like that.
Previous misses do not force the next chance to go in. What matters is whether the player’s overall chance generation remains strong.
Check:
xG
Shots on target
Minutes played
Big chances received
Historical finishing quality
A long-term poor finisher may continue underperforming. A normally clinical striker going through a short bad spell may be more likely to recover.
Practical Example: Poor Results, Strong Chance Creation
Imagine a team has scored 3 goals in its last five matches, which looks weak. But during those games it created 11 big chances and 7.4 xG.
The finishing has been poor. Now it faces a defence conceding plenty of clear opportunities.
The market may still be reacting to the three-goal total. A market such as Team Over 1.5 Goals could deserve more investigation.
The key is recognising that the scoreboard may be hiding healthy attacking performances.
Practical Example: Goals Hide Weak Chance Creation
Now take the opposite case. A team has scored 10 goals in five matches, which is excellent.
But it created only 5 big chances with 5.8 xG. Several goals came from long-range shots, deflections, set pieces, or exceptional finishing.
The results are real, but expecting two goals per match to continue may be too optimistic. The team’s scoring rate is not being supported by the same level of clear chance creation.
Big Chances Can Improve BTTS Analysis
BTTS requires both teams to have realistic scoring routes. Instead of looking only at whether they scored in their last five matches, check whether they actually create big chances.
Suppose Team A scored in 8 of 10 matches and creates 2.1 big chances per game. Team B scored in 7 of 10 matches and creates 1.8 big chances per game.
Both defences also concede clear opportunities. That provides a stronger BTTS case than scoring streaks alone.
Now compare a team that has scored in five straight matches but averages only 0.7 big chances. The scoring streak may be more fragile.
Big Chances Help With Team Totals
Sometimes big-chance data points towards one team rather than the full match. Imagine the favourite creates 3.0 big chances per home match, while the opponent concedes 2.6 big chances away.
But the underdog creates almost nothing offensively. Instead of Over 2.5 Match Goals, you might prefer Favourite Over 1.5 Team Goals.
That selection does not require the weaker side to contribute. Big-chance data can therefore help you choose the market that best fits the matchup.
Look at Big Chances Per Match, Not Just Season Totals
Raw totals can favour teams that have played more matches. Suppose Team A has 30 big chances in 20 games, and Team B has 27 big chances in 15 games.
Team A has the larger total. But per match, Team A is 1.50 and Team B is 1.80.
Team B is actually generating clear chances more frequently. Per-90 or per-match figures usually make comparisons cleaner.
Separate Home and Away Data
A team’s chance creation can change dramatically by venue. For example, a team creates 2.4 big chances at home and 1.2 big chances away.
If the next match is away, using the overall average can overstate the attack. Likewise, some defences allow far more clear chances away from home.
Always match the home attack vs the away defence where possible.
Adjust for Opponent Strength
Suppose a club created 12 big chances in its last five matches. Excellent, but those games were against the league’s weakest defences.
The next opponent has the best defensive record. The recent average should be adjusted downward.
Now imagine another team created eight big chances while facing several top opponents. Its raw total is lower, but the performances may be more impressive.
Big-chance statistics still need schedule context.
Penalties Can Inflate the Number
Remember that every penalty is classified as a big chance. Suppose a team recorded ten big chances, but four were penalties.
Its open-play chance creation may therefore be less impressive than the total suggests. Penalties count because they are genuinely excellent scoring opportunities.
But when predicting future open-play goals, it can help to separate penalty big chances from open-play big chances. Penalties occur less predictably than normal attacking patterns.
Red Cards Can Distort Big-Chance Data
Imagine a team creates six big chances in one match. Sounds exceptional, but its opponent received a red card after 20 minutes.
The game was no longer a normal 11-v-11 contest. If that match is included in a five-game sample, it may heavily inflate the average.
Check whether unusual events created the chances. These include red cards, multiple penalties, extra time, or extreme game states.
Context prevents you from treating unusual matches as normal.
Big Chances Are Not Completely Objective
This is another important limitation. Unlike goals or shots on target, the big-chance classification includes an element of judgement.
The data analyst decides whether the situation meets the provider’s criteria. Opta’s own definition refers to circumstances such as a clear route to goal and low-to-moderate defensive pressure.
So do not treat 2 big chances vs 1 as if it were an exact scientific measurement of superiority. It is a useful indicator, but it is not a perfect mathematical truth.
Different Data Providers May Disagree
Another practical issue is that websites can use different data providers. One platform may record 3 big chances, while another shows 2.
This can happen because definitions and collection processes differ. That is why it is better to use one consistent provider like FBref when comparing teams over time.
Do not mix statistics from several sources without checking whether they use the same definition.
Big Chances Created Can Identify Important Players
Team statistics become even more useful when combined with individual data. Suppose a club averages many big chances, but one midfielder creates a large share of them.
If that player is injured, the historical team average may become less useful. During one 2025/26 Premier League statistical update, Rayan Cherki led the competition with 14 big chances created, ahead of Bruno Fernandes with 11 and Jérémy Doku with 10 at that stage.
If a team’s main chance creator is unavailable, its expected attacking output should be adjusted.
Look at the Type of Chance
Two teams may create the same number of big chances but use very different methods. Team A creates them through cutbacks, through balls, and central combinations.
Team B relies mainly on counterattacks, set pieces, and penalties. That matters for the next opponent.
A deep defensive team might reduce Team B’s counterattacking opportunities. A side weak against cutbacks may be particularly vulnerable to Team A.
The statistic tells you how many, but tactical analysis tells you why.
Big Chances Can Be Excellent for Live Analysis
Suppose a match is 0-0 after 55 minutes, which suggests an Under. But the teams have already produced five combined big chances, 2.3 combined xG, and seven shots on target.
The lack of goals may be mostly poor finishing or strong goalkeeping. An Over 0.5 or Over 1.5 live market could be more attractive than the scoreline suggests.
Now imagine another 0-0 match with no big chances, 0.5 combined xG, and two shots on target. Those are completely different games despite having the same score.
Do Not Double-Count the Same Information
Suppose your reasons for an Over are high xG, many big chances, and lots of shots on target. Good, but remember these statistics overlap.
Big chances contribute to xG, and many big chances also become shots on target. They are not three completely independent pieces of evidence.
Use them together to describe the quality of the attack rather than treating each as a separate reason that triples your confidence.
Big-Chance Prediction Checklist
Before using big-chance statistics in a goal prediction, check:
Big chances created and conceded per match
xG and xGA
Shots on target
Home-away splits
Opponent quality
Penalty contribution
Red-card effects
Current lineup and main chance creators
Finishing performance
Whether the sample is large enough
Tactical source of the chances
Available bookmaker price
Big chances are most useful when they agree with several other indicators.
Frequently Asked Questions
What is a big chance in football? It is an opportunity where a player would reasonably be expected to score, typically a one-on-one or close-range attempt with a clear route to goal and limited pressure. Penalties are always considered big chances.
Are big chances the same as xG? No. Big chances are classified as clear scoring opportunities, while xG gives individual shots a probability of becoming goals.
Are big chances better than total shots? They can be more informative about shot quality. A team with fewer shots but several big chances may have created more genuine danger than a team taking many poor attempts.
Should I back a team because it missed many big chances recently? Not automatically. Missed big chances can show strong chance creation, but you should also examine finishing quality, xG, opponent strength and the size of the sample.
Can big chances help with BTTS predictions? Yes. When both teams consistently create big chances and also concede them, BTTS can become more attractive. However, you should still consider lineups, venue and the available odds.
Final Thoughts
Big chances are one of the most useful statistics for understanding what actually happened in a football match. They help separate shooting often from creating opportunities that should genuinely threaten the goalkeeper.
That distinction matters enormously for goal predictions. A team with 18 shots but no big chances may be much less dangerous than the headline number suggests.
A team scoring only once despite creating four big chances may have performed far better than the final score indicates. The best approach is not to choose between big chances and xG, but to use them together.
Look at big chances, xG, shots on target, opponent strength and current team news. Then ask whether the attacking pattern is repeatable.
If a team consistently creates one-on-ones, close-range opportunities and dangerous cutbacks, that is valuable information for future goal markets. If its recent scoring record comes mainly from long-range goals and penalties, caution may be needed.
Big chances will never predict football perfectly. But they can help you answer one of the most important questions in goal analysis: Is this team actually creating good opportunities—or just taking a lot of shots?
That makes them a far more useful statistic than simply counting attempts or looking at the final score.
Responsible betting: Big-chance statistics can improve your understanding of chance quality but cannot guarantee future goals. Use them with larger samples and other performance data, compare your probability with the available odds, keep stakes controlled and never bet money you cannot afford to lose.
