WeakRisk and training load

The use of algorithms based on scientific literature improves player performance and prevents injuries.
For professional sports clubs, the collection of data on players‘ performance in training and in matches, with the analysis of internal and external loads, is indispensable for injury prevention and the development of ever more advanced strategies to improve players’ performance.
The IT solutions adopted by WeakRisk are innovative and only use sources from the scientific literature. The Apps where data such as RPE BORG CR10, GQR, TIA and sleep analysis can be sent autonomously to the players are an example of this and reduce the error margins and the amount of work for the athletic trainer.
Before the training session begins, you already have the situation visible in the Training Load section of each individual player, derived from previous sessions.
You can use the methodology of your choice such as SRPE – EEE-KJ -EEE>20Wkg or others.

You can also see the TBS (Training Stress Balance) obtained with the indicator you want and the total work done with the parameters you have identified. The most significant of the last few days such as total distance, speed >24Km/h or distance covered >20WKg with a further distinction between training and match.

Then check the metrics for the last 28 days or use periods that interest you and make dynamic comparisons over several periods for each individual player. You can check everyone’s athletic condition against previous periods using the sum function or average the loads you want to monitor.

You have at your disposal two areas of injury prevention that indicate whether the player is at risk: the first exclusively on load management, the second based on the intersection of several areas or skills (workloads, heart rate, age, tests and also meticulous physiotherapy screening).

This is today, but now it is necessary to take a leap into the future:
WeakRisk is an Unstoppable company
the A.I. Project is our next goal !

Estimation of Individual Injury Probability

In the sports field, the use of mathematical models to predict athletes’ injury risk takes into account variables such as volume, intensity, and frequency of training, in addition to physiological and biometric data. These models are highly personalized to reflect not only aspects of training but also individual recovery capacity, resilience to physical and psychological stress, and predispositions to injuries.

The concept of Acute: Chronic Workload Ratio (ACWR), which compares “acute” workload with “chronic” workload, is crucial for identifying safe training. An imbalance in this ratio may indicate a high risk of injury. This parameter is currently used by athletic trainers to monitor and optimize athletes’ workload, with the support of WeakRisk.

The introduction of artificial intelligence and machine learning expands the possibilities of examining the correlation between ACWR variations and injuries, integrating risk factors such as the history of previous injuries, individual characteristics, environmental conditions, and data from wearable devices and performance monitoring systems.

Thanks to real-time analysis, it is possible to obtain a conditioned estimate of the injury probability updated in real-time, which also considers contextual variables capable of significantly influencing the risk. Among these are the simulation of training, that is, the role the player will have to play compared to his usual role, the possible absence of key figures on the field that he will have to replace, weather conditions, the quality of the playing field, temperature, travel for away games, and even the average fouls committed by the opposing team.

Furthermore, it will be possible to include other certain cases, for an even more accurate prediction of the risk, such as schedules and close frequency of matches, thus obtaining a conditioned probability that integrates additional dynamic variables.

The use of artificial intelligence (AI) and machine learning techniques represents a breakthrough in the prevention of sports injuries, offering advanced methodologies for predictive analysis based on large volumes of data. Among the most employed techniques in this field are:

Neural Networks and Deep Learning:These models are particularly effective in identifying complex patterns within large datasets, making them ideal for analysing the subtle correlations between training load and the risk of injuries. Thanks to their ability to autonomously learn relevant features from the data, they can provide accurate predictions based on physical, biometric, and performance parameters.

Classification algorithms: Techniques such as decision trees, random forests, and support vector machines (SVM) can be used to classify athletes into risk categories, based on variables such as training volume, physiological data, and environmental conditions. These models help identify key factors contributing to the risk of injury, facilitating the development of personalized training programs..

Reinforcement Learning: This technique allows models to learn through experimentation and subsequent evaluation, adapting strategies in response to changes in the training context and the athlete’s physical condition. It can be particularly useful for optimizing training and recovery regimes in real-time, maximizing performance while trying to minimize the risk of injuries.

Predictive Analysis: Using historical series and temporal data, predictive analysis allows anticipating the risk of injuries based on trends identified in the athlete’s and team’s historical data. This approach can integrate ACWR and other indicators to provide a risk assessment that takes into account long-term training dynamics using algorithmic indications obtained from Neural Networks and Deep Learning.

Natural Language Processing (NLP): Sebbene meno intuitivo, l’NLP può essere utilizzato per analizzare report testuali sugli infortuni, note mediche e feedback degli atleti, estrapolando informazioni utili che potrebbero non essere catturate da dati numerici o biometrici. Questo arricchisce l’analisi con insight qualitativi, contribuendo a una comprensione olistica del benessere dell’atleta.

Generative Pre-trained Transformer (GPT): A further application of artificial intelligence techniques in the prevention of sports injuries involves the use of advanced language models, like GPT, to transform complex data sets analysed into understandable discursive reports. These reports can be particularly useful for management, coaches, and technical staff, providing them with a clear and direct interpretation of the analytical results without the need to navigate through raw data or statistical technicalities. This ensures that each stakeholder receives the most relevant and useful information for their role and responsibilities.

The adoption of these advanced mathematical models, real-time analysis, and natural language outputs offers an innovative and scientifically validated approach to the prevention of sports injuries, allowing for the personalization of training and recovery strategies according to the specific needs of each athlete.

Interdisciplinarity, combining movement sciences, data engineering, and sports psychology, is crucial for developing increasingly effective solutions in this area.

Professional Soccer Scouting Supported by Artificial Intelligence

The primary objective of scouting is to identify players who not only demonstrate excellence in technical, tactical, and physical terms but also perfectly integrate with the playing style, formation, and culture of a team.

Using AI, we can develop dynamic systems that analyse and compare players effectively across a range of predetermined parameters.

Multidimensional Analysis: Creating detailed player profiles that include not just data on on-field performance, such as successful passes, shots, tackles, ball recoveries, but also advanced metrics like xG (expected goals), xA (expected assists), and movement maps on the field. The analysis can be extended to physical evaluations, such as endurance, speed, and agility, and psychological aspects, such as leadership and resilience.

Machine Learning for Matching: Implementing machine learning algorithms to “match” players based on their compatibility with specific roles or formations. For example, an algorithm might identify central defenders excellent in 4-back defences who could adapt well to a 3-back defence, considering their skills in aerial play, tackling, and game reading.

Understanding a player’s growth potential is crucial for scouting. AI can help project an athlete’s development trajectory, providing valuable insights for long-term purchase decisions.

Predictive Models: Developing regression models or neural networks that use players’ historical data to predict their growth in terms of technical, tactical, and physical skills. These models can consider various factors, including age, playing experience, past injuries, and progression in previous teams, to estimate how a player might evolve in the coming years.

Scenario-Based Simulations:

Using AI to simulate how a player might adapt and grow within specific team contexts, analysing the interaction between his skills and the game system, teammates, and coach strategies. This approach can help identify not only how a player might improve individually but also how he might contribute to team dynamics.

The adoption of AI technologies in scouting requires an iterative approach, with continuous evaluations and adjustments based on feedback and emerging outcomes. The integration of AI systems must be accompanied by close collaboration with scouts, coaches, and performance analysts to ensure that technical analyses are aligned with human tactical and strategic knowledge.

The key to effective AI-based scouting lies in balancing the advanced analytical capabilities of technology with the intuition, experience, and football knowledge of human professionals. By combining these elements, it’s possible to create a scouting ecosystem that not only identifies promising talents but also maximizes their potential for growth and integration into the team.

Youth Scouting with Artifial Intelligence

Determining the likelihood that a youth talent can reach and compete in major leagues, such as Serie A or the French championship, requires a holistic analysis that goes beyond mere technical abilities. The physicality, athleticism, and adaptability of a young player are key factors in predicting their success at the higher levels of football.

Multivariate Predictive Models: Implement regression models that evaluate a wide range of variables, such as physical parameters (height, weight, speed, endurance, strength) and current performance in youth leagues. These models will be enriched through feature engineering to capture the nuances of players’ physical and athletic characteristics, allowing for the prediction of the likelihood of meeting specific performance criteria (e.g., playing at least 10 matches in Serie A).

Geographic and Ethnic Context Analysis: Integrate comparisons based on potential physical aspects related to geographic origin and ethnic composition into the evaluations. Such analysis can offer valuable insights on how genetic and environmental factors influence the physical and athletic development of youths, improving the accuracy of predictions.

Predicting the level of performance, a young footballer can achieve in the coming years is crucial for youth scouting. This involves not just positive performances, such as goals scored, but also disciplinary aspects that can affect a player’s career.

Predictive Modelling Approach: Develop regression or classification models that use historical data on players’ performances and growth trajectories, considering variables such as age, physical characteristics, and evolutionary trends. These models can estimate the likelihood that a player will reach specific benchmarks of success or encounter particular challenges, such as the number of yellow cards or expulsions.

The effectiveness of predictive approaches in youth scouting depends on their ability to adapt and learn from new data and outcomes. Integrating continuous feedback, analysing the causes of early dropout or obstacles encountered by young talents, and constantly revising predictive models are essential for refining the ability to predict success and identify the most promising talents.

Retrospective Analysis: Understanding and analysing retrospectively the paths of those who have prematurely left professional soccer can offer insights into which factors, beyond technical and physical abilities, are crucial for long-term success, including psychological, motivational, and environmental aspects.

Through an approach based on predictive analysis and continuous monitoring, youth scouting can transform, becoming more accurate and proactive in identifying and developing the soccer talents of the future. The key lies in balancing advanced analysis and human understanding, integrating technical knowledge and physical evaluations with a deep sensitivity to the individual and contextual dynamics that influence the development of a young athlete.

Incorporating psychological assessments and behavioural analysis into youth scouting provides a critical additional dimension for thoroughly understanding the long-term potential of a young athlete. These aspects, combined with physical and technical analyses, provide a comprehensive view of the player, not just as an athlete but also as an individual.

To anticipate the success of a young footballer, it is essential to explore their psychological characteristics, such as resilience, motivation, work ethic, and the ability to handle pressure. Through standardized questionnaires, interviews, and, if possible, observations during games and training sessions, valuable data can be collected on players’ psychological profiles.

Behavioural Analysis on the Field: Observing players’ behaviours in pressure situations, such as their reaction to a mistake or their interaction with teammates and opponents, can reveal important character traits and personality aspects, like leadership, cooperation, and stress management.

Predictive Modelling of Psychological Traits:

Using artificial intelligence to analyse data collected from psychological and behavioural assessments, creating profiles that predict a player’s likelihood of success also based on these factors. These models can include machine learning algorithms that consider how psychological traits affect sports performance and behaviour on the field.

The information obtained from psychological and behavioural assessments should be integrated into the scouting decision-making process, complementing the analysis of physical and technical performance. This integration allows scouts and coaches to identify players who not only have the technical and physical skills to excel but also possess the mental strength, aptitude, and behavioural traits indicating a high potential for success in professional soccer.

Customized Development: Use psychological assessments to inform individualized development programs that not only aim at improving technical and physical skills but also at strengthening psychological and behavioural competencies, such as anxiety management, focus, and determination.

Continuous Monitoring:

Implement a continuous feedback system that assesses the evolution of the psychological and behavioural traits of young players over time. This will allow for the adjustment of personalized development strategies and early interception of potential adaptation or motivation issues.

The addition of psychological evaluations and behavioural analyses could significantly enrich the youth scouting process, offering a deeper understanding of players and the factors that contribute to their long-term success.

This holistic approach, which considers the player in their entirety, is crucial for identifying and developing not only talented athletes but also resilient individuals and psychologically prepared for the challenges of professional soccer.

Summarizing the project in a few simple lines, here are the questions we are able to provide answers to:

Injury Area:

  • We can predict the likelihood of a player getting injured and implement timely corrections to reduce the risk of injury.
  • We can predict the severity of the injury and potential recurrences.

Pro Scouting Area:

  • We can facilitate the search for players with specific characteristics and evaluate their integration, with an analysis of the current team components and, specifically, whether the current components are suitable to enhance their characteristics.
  • We can assess the growth curve of a player from second-tier leagues in terms of game and physical metrics relevant to our league and study their integration simultaneously.

Youth Sector Internal Scouting:

  • We can determine the likelihood that a youth will make it to the first series of major leagues (e.g., play at least 10 matches in Serie A, at least 20 in the French championship), particularly considering physical/athletic growth as well as technical aspects.
  • We are also able to identify the specific reasons for those who have not reached this target so that distractions on losing profiles can be avoided.

Most importantly, we can provide a tool and a project that not only answers these questions you have posed but can be queried without any limits and can be shaped according to your philosophy.

In football and beyond , ideas,  courage, planning and methodology area needed, but the future is now!

Tiziano Testa ( Founder & Ceo WeakRisk)