Can data science create an Olympic fencer? With rapid advancements in AI and machine learning, it’s becoming more realistic. Many think of fencing as “physical chess,” where every bout demands quick, fluid strategies and split-second decisions. While the in-the-moment choices can’t be entirely replaced, data science can give fencers a winning edge. By analyzing opponents’ patterns, optimizing training, and tracking performance metrics, a fencer can use data insights to adapt and improve, transforming their preparation and elevating their game to a whole new level.
Fencing requires complex biomechanical movements that blend balance, agility, strength, and precise control.
Stance and Balance (En Garde Position): Fencers maintain a low, stable position to allow quick responses in any direction.
Footwork (Advancing, Retreating, and Lunging): enables strategic distance control, with the lunge as a powerful forward thrust propelled by the back leg. This movement requires powerful lower-body strength and core stability.
Arm and Wrist Control (Blade Work): Blade movements involve precise wrist and arm control, allowing fencers to quickly change attack angles and target points. By rotating the wrist and adjusting the angle of the blade, fencers perform cuts, thrusts, and parries with precision, often needing fine motor control to execute quick, targeted strikes.
Reactive Movement and Agility: Fencers constantly adjust to their opponent’s movements, which requires rapid reaction times and agility. Quick shifts in direction, sudden lunges, or evasive steps rely on a combination of lower-body strength, balance, and flexible joint movement to adapt fluidly.
Optimizing Training Techniques: Wearable sensors can capture key metrics like acceleration, blade angle, and heart rate, providing insights into how a fencer’s body responds to different training drills. By analyzing this data, coaches and athletes can identify which exercises most effectively enhance endurance, balance, or agility.
Comparing Elite and Novice Fencers: By studying the movements and reaction times of elite versus novice fencers, we can identify patterns that predict a fencer’s skill level. These insights can inform tailored training regimens that focus on improving specific abilities to advance a fencer’s performance.
Scoring Analysis: Studying your scoring patterns—like which moves or angles result in points most often—can help fine-tune your offensive techniques. You could also analyze which moves lead to lost points, helping you avoid mistakes in future bouts.
Predicting Fatigue and Injury: Tracking physical metrics (e.g., heart rate, muscle exertion) during practices and matches can help identify when you're at risk of fatigue or injury. This data helps tailor rest and training plans, keeping fencers in top shape for competition.
In a recent study, Simona Aresta et al. used biomechanical data and machine learning algorithms to predict a fencer’s skill level (Novice or Elite) with over 90% accuracy. The study included 21 fencers (17 male and 4 female). While the study primarily focused on épée and foil, the findings have potential applications in saber—a faster, more dynamic form of fencing that could benefit from similar analytical techniques.
Well-trained elite fencers follow patterns based on their bias, strengths, and their opponent’s weakness. The fencing patterns can be collected by analyzing bout videos. Machine Learning algorithms can model complex patter and could accurately predict a fencer's next move based on past patterns. While a fencer will be unable to make use of the analytics during the bout, they can prepare and train themselves to anticipate opponents patterns.
Preferred Attack Angles: Analyzing the angles at which an opponent typically strikes can reveal their favored attacks (e.g., head vs. torso) or preferred side of the body to target. This helps identify which areas they’re most comfortable attacking and allows for more effective counters.
Preferred Attack Sequences: Identifying if an opponent often uses a series of strikes in succession (e.g., high attack followed by low attack) can help in anticipating their combinations and setting up counters.
Timing of Attacks: Recognizing when an opponent likes to initiate attacks—such as at the start of a bout, after scoring, or in response to certain movements—can reveal whether they favor aggressive openings or more cautious, reactive strikes.
Attack Distance and Range: Analyzing the distance from which an opponent typically attacks can inform their comfort range. If they consistently attack from a long range, for example, they might be less effective in close combat, which could be leveraged strategically.
Reaction Times: Tracking how quickly an opponent responds to specific movements or attacks can provide insight into their reaction speed and adaptability. This is useful for timing attacks that exploit any delays or hesitations in their responses.
Defensive Moves: Observing common defensive actions—such as frequent parries, retreats, or evasion strategies—helps predict how an opponent is likely to defend against different attacks. Knowing these tendencies can guide an offensive strategy that forces the opponent into less comfortable defenses.
Common Parries and Blocks: Noting if an opponent relies heavily on certain parries or blocks (e.g., parry 3 for head, parry 4 for torso) can inform which attacks are more likely to break through their defenses.
Reaction Speed to Attacks: By timing an opponent’s defensive response, one can assess whether they struggle with quicker attacks, allowing for faster, more aggressive maneuvers to exploit slower defenses.
Counterattack Tendencies: Some fencers favor immediate counterattacks after a block or dodge. Recognizing this allows for baiting techniques, drawing out their counter for a well-timed secondary attack.
Distance Control in Defense: Observing if an opponent retreats to a particular distance during defense reveals their comfort zone for defensive moves. This can be used to disrupt their rhythm by pressuring them to engage in close range or cutting off their retreat.
Frequency of Feints: Observing how often an opponent uses feints can indicate whether they rely on deception as a key tactic. Frequent feints suggest an attempt to manipulate the opponent’s timing or positioning, helping to anticipate moments when they’re likely to employ these moves.
Feint Timing: Analyzing when an opponent tends to feint—such as at the beginning of a bout, after scoring, or under pressure—can reveal psychological patterns. For instance, some fencers may feint early to gauge reactions, while others may rely on them when trailing in points.
Preferred Feint Types and Targets: Identifying whether an opponent prefers high or low feints, or favors certain target areas (like the head or upper torso), helps predict which parts of the body they may attack after feinting. Knowing these preferences allows for strategic adjustments in defense.
Reactions to Feint Responses: Analyzing how an opponent responds when their feint is unsuccessful—such as quickly shifting to defense or doubling down on the attack—can reveal adaptive tendencies, helping to anticipate their next move even if the feint fails.
Advancing and Retreating Habits: Tracking how often and at what pace an opponent advances or retreats reveals their preferred range and rhythm. Some fencers prefer staying at a long distance and only closing in for quick attacks, while others maintain a close range, ready to strike.
Direction Changes: Observing how often and quickly an opponent changes direction can indicate agility and adaptability. Those who frequently change direction may rely on evasive tactics, making them harder to predict but potentially vulnerable to feints or sudden, direct attacks.
Engagement Distance: Understanding an opponent’s preferred distance for engagement (close, medium, or long) helps in setting the bout’s pace. A fencer comfortable at long range, for instance, may be weaker in close quarters, providing opportunities to close the gap and gain an advantage.
Zone Control: Some fencers maintain strong positions at certain areas on the strip, such as near the center or towards the end line. Recognizing these tendencies can reveal comfort zones or weaknesses when forced outside of these areas.
Speed Changes: Analyzing an opponent’s changes in tempo—alternating between fast and slow attacks—can uncover rhythm-breaking tactics. Fencers who vary their speed frequently try to throw off opponents’ timing, which can be countered by maintaining a stable pace or mirroring their tempo changes.
Rhythmic Movements: Some fencers adopt a consistent rhythm, which, if predictable, can be disrupted by irregular timing or an unexpected attack. Conversely, those with erratic rhythm may be more challenging to predict, requiring extra attention to their subtle movements.
Response to Specific Moves: If an opponent consistently reacts a certain way to specific actions (e.g., parrying when feinted high, sidestepping to avoid low attacks), these patterns can be used to set up planned moves or exploit gaps in their responses.
Behavior Under Pressure: Observing how an opponent reacts in high-stress situations, like close score matches or when close to the end line, provides insights into their mental resilience. Fencers who become defensive or conservative under pressure might be more vulnerable to aggressive, surprise attacks.
Machine learning algorithms offer powerful tools for analyzing and predicting complex patterns in fencing, from pinpointing weaknesses in a fencer’s movements, reaction time, and speed to identifying strategic and tactical patterns. These techniques place data science at the core of fencing, transforming every practice and bout into valuable insights for becoming a smarter, faster, and more strategic fencer. While these methods hold great potential, collecting large amounts of accurate data remains a challenge. In my next blog, I’ll dive into the complexities of data collection in fencing and explore innovative solutions.
Reference:
Aresta, Simona, Ilaria Bortone, Francesco Bottiglione, Tommaso Di Noia, Eugenio Di Sciascio, Domenico Lofù, Mariapia Musci, Fedelucio Narducci, Andrea Pazienza, Rodolfo Sardone, and et al. 2022. "Combining Biomechanical Features and Machine Learning Approaches to Identify Fencers’ Levels for Training Support" Applied Sciences 12, no. 23: 12350. https://doi.org/10.3390/app122312350