Latest technologies now permeate all areas of life, including the sports analysis and betting industries. As a result, subjective expert assessments are gradually taking a back seat, replaced by mathematical models.
The analysis of betting markets now depends on the speed of processing large datasets and technological solutions. Modern technologies allow for much more accurate probability assessments. This is relevant for sites such as 1xBet official and other platforms in this segment.
xG Estimation for Modeling
One of the key metrics in soccer is expected goals (xG). Data providers such as Stats Perform (Opta) have developed a model for evaluating scoring opportunities, assigning them a rating from 0 to 1.
xG algorithms are based on machine learning. Training is conducted using archives of over 300,000 historical shots. The calculation takes into account more than 20 variables. The most significant of these are the distance to the goal and the angle of the shot, the type of assist—a ground pass, a cross, or a free kick—the level of defense, and the goalkeeper’s position at the moment of the shot.
For example, a standard penalty kick has a fixed xG value of 0.76. xG statistics are accumulated and allow for the identification of discrepancies between goals scored and the quality of scoring opportunities. This is a key indicator for long-term forecasting of results in betting markets.
Electronic Performance Tracking Systems
These systems record player metrics in real time. Specifically, they track precise positioning, movement speed, acceleration, total distance covered, and passing patterns. This information is then transmitted to analytics centers. Performance matrices are generated based on the data. Coefficients are then calculated, and teams’ physical condition is assessed.
Computer Vision and Optical Tracking
Artificial intelligence facilitates remote data collection without the need for sensors attached to the athlete’s body. To achieve this, high-resolution cameras are installed around the perimeter of the stadium. A sufficient number of cameras is required, positioned from various directions and angles. Video footage from these cameras is processed using computer vision technologies. Systems such as ChyronHego (TRACAB) or Genius Sports use neural networks for object recognition. The technology operates according to the following algorithm:
- Object segmentation. Algorithms isolate the silhouettes of players, referees, and the ball from the field background.
- Identification. Artificial intelligence recognizes jersey numbers as well as specific features to assign a unique ID to each subject.
- 3D tracking. Modern cameras are capable of tracking up to 29 points on a player’s body at a rate of up to 50 frames per second. This means that the movements of the limbs, joints, and head are assessed separately to create individual mathematical models.
Thanks to this approach, the necessary data can be calculated—from the exact trajectory of the ball to the goalkeeper’s reaction time and even the angle of a defender’s body turn. The coordinates obtained are converted into digital metrics. And mathematical models assess the probability of a successful pass or interception.
Real-Time Data Streams
The speed of information transmission is particularly important for analyzing live betting markets. Providers use ultra-low-latency technologies. This allows information to be transmitted from the stadium to analytical platforms in literally fractions of a second. Protocols such as WebSocket and Apache Kafka are used for this purpose. This ensures a processing latency of less than 300 milliseconds.
Moreover, these data streams contain not only basic events occurring on the field—such as goals, corner kicks, or cards—but also comprehensive statistical data sets. This information includes, for example, ball possession percentage or pressing intensity. Thanks to this speed, mathematical algorithms instantly adjust odds in accordance with the game situation.
Input Data for Models
Modern technologies are based on predictive modeling, not simply analyzing win/loss trends. Algorithms use multi-level data matrices. The main goal of this approach is to distinguish randomness from patterns. Mathematical simulations help with this. The input data architecture for machine learning algorithms includes:
- Match histories. Archives of previous games are used. Depending on data availability, matches from as far back as ten years ago—or even earlier—may be included. The system takes into account team performance indices. In addition, injuries, transfers, and even weather conditions are evaluated.
- Strength assessment. An analog of the dynamic position evaluation from chess theory is used. A combination of factors reflects a team’s strength in mathematical terms.
- Simulation matrices. Event probabilities are determined using the Monte Carlo method. The software simulates the same match 10 to 100 thousand times, based on the input metrics.
The result is a probability distribution, which is then converted into numerical odds.
Using Innovations in Betting
Thus, sports analytics is actively integrated with modern technologies. Whereas in the past, one had to rely exclusively on observations—which were not always objective—everything is now automated. Modern methods minimize human involvement in the analysis. Consequently, they eliminate bias.
Currently, technology continues to evolve, with a primary focus on artificial intelligence. As a result, forecasting tools are becoming more detailed, but they still cannot guarantee fully accurate outcomes. Every model depends on the quality of its data, and sport always leaves room for context that numbers may not capture.