Football is the most popular sport in the world, with over 3.5 billion fans combined across leagues and tournaments. With this multitude of fans, the sport has the biggest betting market worth over $91 billion as of 2022. This market was predicted to grow at a CAGR rate of 10.3% and to amass a value of over $180 billion by 2030. The huge potential of this market is why the football betting space is filled with resources, from analyses on betting websites to recent AI tools for team and players’ performance analyses.
These tools are also known as advanced football prediction statistics, with different models developed over the years for tournament, league, and player analyses. As technology continues to evolve, these advanced football predictions are inching toward accuracy, providing you with odds and probabilities from fed data. These models take cognizance of teams’ past game performances to make predictions for future matches. Betting football fans have taken advantage of these tools to make wagers on teams, and most of the predictions have turned out accurate. As more success is recorded with these advanced statistics, they’re gradually becoming an inevitable part of football predictions in the betting market and beyond.
Key Advanced Football Prediction Metrics
Soccer predictions were primarily limited to the final outcome of a match, using data from past performances and the team’s form. Not many analysts could accurately predict the winner of a whole tournament or league, and when they did, it was just pure luck. However, thanks to advanced prediction metrics, you can have an AI tool that gives you insights on what team will most likely win a tournament and gives a breakdown of why that choice is. This, among many other features, is found in an array of prediction metrics, including Expected Goals (xG), Expected Goal Difference (XGD), Possession Percentage, xG Current Form, and xG League Statistics, among others.
Expected Goals (xG) is the most popular model of these advanced prediction statistics, with variations of the tool from different developers. As the name of the tool suggests, it predicts the number of goals that’d be scored in an upcoming match. It also gives an analysis of the number of goals after the match. This tool typically encompasses a few other prediction metrics as it considers the current form and league statistics of the team before predicting a scoreline. It also calculates the number of goals a team should have scored based on the quality of chances created after the match.
xG League Table is a prediction tool that updates expected goals, goals assists, and the total number of points earnable by teams in a league. It also positions them based on these statistics, so you make your wagers accordingly on upcoming matches. xG League Table is another powerful prediction tool that could help you make early predictions on what team would end up at the top of the league.
XGD (Expected Goal Difference) is a prediction tool that lets you know what goal difference will occur between two teams on a league table. It’s the difference between the xG and xGA (a measure of the quality of chances created by the opposing team). Possession Percentage tools calculate and predict which team will be in better possession of the ball for the first, second, or both halves of the game. This will help you make informed decisions on what team will create more chances and are most likely to score more goals.
xG Current Form predicts the form of a team as a home or away opponent against other teams. The data obtained from this metric greatly influences football betting tips, as teams with stronger forms are predicted to score more goals, have better possession percentages, and thrive in the league they’re playing.
How Advanced Stats Are Interpreted For Football Predictions
These advanced stats are easily accessible on designated websites, but the problem lies in how to read the data provided and use it for betting. Statistics in the metrics are presented in the form of odds, which are numbers separated by decimals or commas. For instance, when the number displayed as the xG of a team is 3.6, it means that the likely number of goals they’d score in that match is greater than three.
With xG metrics, data is usually given for home and away matches. A team could have 3.6 xG in its home match and have a 0.5 xG in its away match. An xG of 0.5 means the team is likely not to score a goal. In other prediction metrics, such as possession percentage, xGA, xCurrent Form, and League Statistics, predictions occur in the form of odds. Against an opponent team, the prediction tool highlights numbers with decimals, and the ones with higher values are the ones predicted unlikely to be better in form, possession, etc.
In scoring potential, you want to take metrics like xG, possession percentage, and current form into deep consideration. These metrics work together to elucidate the quality and quantity of chances that will be created by teams and which would likely be converted into goals. Aside from goals, you can also use these metrics to determine the defensive strength of a team. For this, you’ll need to take metrics such as xG’s current form and possession percentage into consideration.
From these metrics, you can also get an overview of the team’s overall strategy. When a team is predicted to be on the stronger side in form, possession, and expected goals, they’ll have less to do in terms of strategy. The team’s management might stick with whatever formation that made them so formidable in the first place and keep winning games with it. On the other hand, teams with lower ratings, as predicted by advanced prediction statistics, will need to explore formations that would counter that of their opponents for a win.
Practical Application In Football Betting
So far, these metrics have been used by football analysts and bettors to get an idea of what team will best match the other in an upcoming match. As AI tools become more integrated into our everyday lives, we’ve seen these metrics become a huge part of sports journalism, giving insights to even the furthest tournaments, such as the coming World Cup. However, its use is not only in the journalism world, as bettors have taken good advantage of these metrics.
Formerly, bettors had to rely on analyses from professionals, who could be influenced by emotions when predicting game outcomes. Also, these professionals may not take an objective view of the history and patterns exhibited by teams when it comes to facing certain opponents. These are eliminated in the case of advanced prediction statistics. They run predictions by analyzing the past performances of the teams, the individual strength of players, and the current form of the players.
These rule out the subjective views of human analysts and present you with pure facts and a probability of what’s going to happen. With thousands of variabilities considered simultaneously, these prediction models give accurate predictions more often than not. Hence, if you want to make informed and strategic betting decisions, you won’t go wrong using these advanced football prediction statistics.
Conclusion
As the world becomes more inclined to use AI and other technological advancements in day-to-day activities, it’s only right that it extends to the football world. As a football fan and bettor, these prediction models will save you stress and time. They’ll analyze data from hundreds of past performances, determine the current form of the team, and analyze individual player’s strengths to determine the quality of chances and goals that would be created and scored in a match. You can easily access these prediction models and make your football betting life better today.
