Unlocking the full potential of combat sports analysis hinges on mastering the core data. Every piece of player history, from their accuracy and volume of strikes to the number of successful takedowns and a fighter’s record of wins and losses, are vital performance indicators that can help you accurately predict the outcome of a game.
Statistical analysis of fighter data can help provide more accurate game predictions. Machine learning models identify patterns among players, making it easier to bet on the best fighters.
Key Performance Indicators in Combat Sports
Strike accuracy and volume
In combat sports, accuracy is a measure of hitting the desired spot. The more accurate a strike is, the more correct it comes to hitting the intended target. Accuracy matters because missing a target could mean self-harm.
Volume refers to having more strikes. Usually, the goal of volume strikers is to tire out the opponents before finding an opportunity to throw in more powerful punches. Usually, whoever wins isn’t always the fighter with a higher volume of strikes. Accuracy and power heavily contribute to the “significance” of strikes and affect how the judges award the win.
Takedown success rate
A takedown is a technique in which an attacker destabilizes an opponent and brings them to the ground. The more successful a fighter is at takedowns, the more likely they are to win. Some of the most effective takedown techniques include double leg, single leg, ankle pick, and foot sweep.
Submission attempts and defenses
It helps to know how many times a player has submitted in the ring and how many submission attempts they’ve made during their career. More importantly, it matters what kind of technique a fighter uses to get their opponents to submit and how they escape submissions. Joint locks and chokeholds are common ways to force submissions, while parries, chokes, and kneebars are effective defenses against submission attempts.
Fighter’s win/loss record and method of victory
One of the best ways to understand a fighter and predict a match is to look at their win/loss record. Additionally, analyzing past fighting techniques can help you understand how a player’s skills match up against an opponent and how a fight may unfold.
Advanced Metrics
Significant strike differential
The strike differential measures the difference between the number of significant strikes a player makes against another. For example, a player with a 3.8 striking differential throws 3.8 significant strikes for every strike the opponent throws. Naturally, a player with a higher significant strike differential is likelier to win.
Control time
In combat sports, control time is the time a fighter spends dominating an opponent on the ground or in a clinch position. Fighters gain more points if they can secure more control time. Fighters who can secure more control time are more likely to land significant strikes and successful submissions.
Damage absorption and output
Damage absorption is the damage a fighter takes up in a fight, consequently affecting their performance over time. On the other hand, damage output assesses how a fighter’s actions significantly harm their opponent. Damage can be both physical or mental and, ultimately, affects a fight outcome. Understanding both sides of the damage coin helps you predict which fighter is likely to win or lose.
Historical Data Analysis
Importance of past performance
Analyzing a fighter’s record is the best way to understand them and predict match outcomes. This includes looking at their fighting styles, opponent analysis, and fight IQ while inside the ring, moments of growth, wins, and losses. In MMA matches, understanding fighting styles heavily informs your prediction, as players can mix different styles.
Analyzing patterns in fighter careers
Looking into patterns, especially how players respond to attention and discouragement before matches, how they react to wins and losses, and their preferred fighting styles, helps you predict a fighter more.
Patterns go beyond a player’s performance inside the ring. They extend to training discipline, coaching relationships, interactions with other players, and commitment to their sport. All the physical or psychological patterns matter when trying to predict a fighter’s behavior. Being an expert and digging into the details could give you the edge you need to make better predictions.
Contextual Factors
Weight class dynamics
Usually, fight organizers group players according to their weight class to create fairer fights. Different classes can have different outcomes. For instance, fights in lower weight classes tend to involve more movement and volume of actions, as opposed to those in heavier weight classes, where power and force are dominant. Stronger athletes tend to have higher upper limb strength than lightweight athletes. Generally, losing weight reduces a fighter’s strike accuracy.
Stylistic matchups
In combat sports like MMA, players can combine different styles, including Muay Thai, Brazilian Jiu-Jitsu, and boxing. Understanding each fighter’s style makes it easier to predict match outcomes. However, just because a fighter has a more diverse style doesn’t mean they’ll win because the level of skill matters.
Impact of age and experience
Time is an essential factor in analyzing a fighter. On the one hand, players with more experience are more likely to win since they’ve had more opportunities to win, lose, and analyze other players. However, as athletes age, they also lose strength and become more susceptible to permanent injuries, making them more likely to lose matches.
Limitations of Statistical Predictions
The human element and intangibles
While statistical predictions try to be as accurate as possible, they are heavily susceptible to biases. For instance, what counts as a significant strike depends on the judges’ assessment. Furthermore, while a player’s fighting history can point to some speculations, it doesn’t provide conclusive insight into their lives and mental states. Personal issues such as emotional upheavals and injuries outside the ring are not factored into statistical analysis and heavily impact match outcomes.
Similarly, in other areas like gaming, statistics can help predict trends but might not capture the unpredictability of human behavior. For instance, while probability models can enhance user engagement in games, there’s always room for surprises beyond the numbers.
Adapting to new techniques and strategies
Over time, fighters adapt to new techniques and strategies to secure more wins. As such, depending solely on past performance doesn’t account for changes in a fighter’s strategy. Just as in other games, the perceived versus actual fighting capacity varies.
Case Studies
Examples of successful statistical predictions
- Vicente Luque vs Rafael Dos Anjos 2013: Vicente Luque won as predicted by models.
- Alexander Volkanovski vs Ilia Topuria 2024: Ilia Topuria won as predicted.
Instances where statistics failed to predict outcomes
- Charles Oliveira vs Beneil Dariush in 2023. Predictions assumed Beneil Dariush would win, but Charles Oliveira won.
- Michael Johnson vs Clay Guida in 2021: Predictions had Michael Johnson in the lead but Clay Guida clenched victory.
The Future of Fight Prediction
More and more predictions are turning to machine learning and AI to predict fight outcomes. In a paper titled “Predicting UFC matches using regression models,” researchers Sebastian Apelgren and Christoffer Eklund applied statistical inference methods to data from UFC MMA matches from April 2000 to April 2024. Using logistic regression and Bayesian regression, they created models with 60% and 70% outcome accuracy, respectively.
Conclusion
Ultimately, making better predictions is a delicate balance between expert insight and statistical analysis. While the statistical models used to predict fight predictions will continue to evolve, they cannot fully encapsulate the unknowable. As such, the best way to make a good bet is to invest in the data straight from the source while retaining a healthy attitude about the unknowable.