The increasing popularity of sports betting has certainly had a big impact on the way people analyze football matches.
The introduction of data, modelling, and market intelligence has transformed gut-feeling predictions into structured, probability-driven insights.
Despite what some people may believe, gambling isn’t all just about hopping into an online casino like Lemon Casino to play table games and slot machines.
Sports betting has also become a big part of the gambling industry, accounting for an ever-increasing share of gambling revenue and giving sports fans access to a wider, more comprehensive set of data and insights to inform their game predictions.
Taking Predictions From Opinions To Probabilities
Essentially, sports betting analysis takes predictions and reframes them as probabilities rather than certainties.
Instead of assuming that a team “will win”, they imply that a team has “a certain percentage chance of winning under these conditions.”
Through this framing analysis, bettors are able to compare their own estimates to bookmaker odds, identify value bets, and avoid making emotionally-driven decisions.
Using Expected Goals As A Prediction Metric
Expected goals (xG) are a metric that estimates the probability that a shot on target will result in a goal.
This is measured based on historical shot data like distance, angle, or the type of shot.
Instead of just counting shots, the xG tells you:
- The quality of the scoring opportunities
- Which teams are actively creating goal opportunities
- Which teams are overperforming or underperforming
This more detailed data allows you to distinguish between a quality performance and pure luck.
If we look at the current 2025/26 Premier League season, Manchester United’s expected goals are sitting on 1.78 per game.
This is the highest of any other team so far this season.
Their actual goals scored have also been very closely matched by this prediction, showing a notable consistency between their performance and results.
The league’s lowest xG is around 1.10 per game for certain clubs, highlighting their struggles to create quality chances.
By looking at these metrics, you’re able to predict goal totals and also ascertain whether a team should have won or lost, even if the actual scoreline was unexpected.
Comparing Expected Goals With Actual Goals For More Accuracy
By comparing the xG with the actual goals scored during a game, you’re able to see which teams are scoring more than xG, which shows clinical finishing.
You’re also able to see which teams are underperforming due to creating goal-scoring opportunities but not following through.
Teams tend to move more toward their xG over a few matches as the season progresses.
When we look at the previous Premier League season, Manchester United actually underperformed their xG because they were creating chances but not scoring as expected.
In contrast, Liverpool outperformed their xG because they scored far more goals than expected.
Using Individual Player Metrics For More Precise Predictions
Sports betting analysis doesn’t just stop at measuring a team’s performance.
It can also be used to focus on individual players.
This is calculated using metrics like:
- Expected goals per game (xG per 90 min)
- Shots per game (and non-penalty xG)
- Profiling the quality of a player’s finishes and chances created
For example, when analyzing Erling Haaland’s profile, it’s clear he has elite scoring consistency, as evidenced by his high expected goal totals and shot-quality metrics.
You can use these metrics to make predictions for:
- First goalscorer bets
- Total shot markets
- Player performance
Contextualizing Goal Scoring Metrics With Match Events
Making predictions isn’t all just about the numbers.
You also need to layer in some contextual match information, like:
- Injuries and suspensions
- Home vs Away splits (Teams often have a higher xG when they’re playing at home)
- Squad rotation (particularly around European fixtures)
- Travel fatigue and match congestion
- Recent form
- Tactical trends
These factors may help to explain why the numbers may shift from match to match.
Clubs that have shown trends of a strong pressing intensity or high-quality goals in the final third have a tendency to create a higher xG total across fixtures.
Certain teams have consistently outperformed their xG, and it’s assumed that this is due either to superior finishing or even just luck.
Betting analysis treats these kinds of deviations rather cautiously, expecting them to return to their average xG over time.
Market Odds Are A Form Of Collective Intelligence
Boomakers odds themselves are also a rather powerful analytical tool.
Due to the fact that sportsbooks aggregate massive volumes of bets and react to breaking news faster than the media, they effectively act as betting markets in and of themselves.
Analysts study the movement of the opening and closing lines, any sudden shift in the odds, and discrepancies between sportsbooks.
They use this information to understand where informed money is flowing and why.
Facts Over Feelings
Perhaps the most beneficial aspect of sports betting analysis is that it can either reduce or eliminate bias.
It helps counter things like fan loyalty, media perceptions, and overreacting to small sample sizes.
It’s also a great way to combat recency bias, which tells you that since your team won last week, they’ll probably win this week as well.
By grounding your predictions in verifiable data and probabilities, analysis forces you to make predictions that are based on facts, not feelings.
