Embedded Sensing Group ESG Institute of Computer Science in V orarlberg ICV, University of St. Gallen HSG, Switzerland E-mail: leonardo.rossi@student.unisg.ch, Abstract--Triathlon training, which involves high-volume swimming, cycling, and running, places athletes at substantial risk for overuse injuries due to repetitive physiological stress. Current injury prediction approaches primarily rely on training load metrics, often neglecting critical factors such as sleep quality, stress, and individual lifestyle patterns that significantly influence recovery and injury susceptibility. We introduce a novel synthetic data generation framework tailored explicitly for triathlon. This framework generates physiologically plausible athlete profiles, simulates individualized training programs that incorporate periodization and load-management principles, and integrates daily-life factors such as sleep quality, stress levels, and recovery states. We evaluated machine learning models (LASSO, Random Forest, and XGBoost) showing high predictive performance (AUC up to 0.86), identifying sleep disturbances, heart rate variability, and stress as critical early indicators of injury risk. This wearable-driven approach not only enhances injury prediction accuracy but also provides a practical solution to overcoming real-world data limitations, offering a pathway toward a holistic, context-aware athlete monitoring. Triathlon is a demanding multi-sport discipline that combines swimming, cycling, and running.

This paper introduces SePA (Search-enhanced Predictive AI Agent), a novel LLM health coaching system that integrates personalized machine learning and retrieval-augmented generation to deliver adaptive, evidence-based guidance. SePA combines: (1) Individualized models predicting daily stress, soreness, and injury risk from wearable sensor data (28 users, 1260 data points); and (2) A retrieval module that grounds LLM-generated feedback in expert-vetted web content to ensure contextual relevance and reliability. Our predictive models, evaluated with rolling-origin cross-validation and group k-fold cross-validation show that personalized models outperform generalized baselines. In a pilot expert study (n=4), SePA's retrieval-based advice was preferred over a non-retrieval baseline, yielding meaningful practical effect (Cliff's $δ$=0.3, p=0.05). We also quantify latency performance trade-offs between response quality and speed, offering a transparent blueprint for next-generation, trustworthy personal health informatics systems.

Building fires pose a persistent threat to life, property, and infrastructure, emphasizing the need for advanced risk mitigation strategies. This study presents a data-driven framework analyzing U.S. fire risks by integrating over one million fire incident reports with diverse fire-relevant datasets, including social determinants, building inventories, weather conditions, and incident-specific factors. By adapting machine learning models, we identify key risk factors influencing fire occurrence and consequences. Our findings show that vulnerable communities, characterized by socioeconomic disparities or the prevalence of outdated or vacant buildings, face higher fire risks. Incident-specific factors, such as fire origins and safety features, strongly influence fire consequences. Buildings equipped with fire detectors and automatic extinguishing systems experience significantly lower fire spread and injury risks. By pinpointing high-risk areas and populations, this research supports targeted interventions, including mandating fire safety systems and providing subsidies for disadvantaged communities. These measures can enhance fire prevention, protect vulnerable groups, and promote safer, more equitable communities.

In this paper, we present a novel sequential team selection model in soccer. Specifically, we model the stochastic process of player injury and unavailability using player-specific information learned from real-world soccer data. Monte-Carlo Tree Search is used to select teams for games that optimise long-term team performance across a soccer season by reasoning over player injury probability. We validate our approach compared to benchmark solutions for the 2018/19 English Premier League season. Our model achieves similar season expected points to the benchmark whilst reducing first-team injuries by ~13% and the money inefficiently spent on injured players by ~11% - demonstrating the potential to reduce costs and improve player welfare in real-world soccer teams.

Anyone can admit that baseball is hard to predict1, probably one of the reasons it can be so exciting. I've definitely done my share of modeling game and at-bat outcomes, but I wanted to pose a different challenge for this post: predicting injury risk. Injury risk is an important element of season long unpredictability. A bunch of injury research focuses mostly on pitcher health, looking at pitch load, movement, even video analysis. I'm sure teams also have their own complex systems for modeling and monitoring player health.

Popularised in the Brad Pitt film Moneyball, groundbreaking analytics almost saw the Oakland A's crowned the kings of baseball back in 2002. General manager Billy Beane's evidence-based, sabermetric approach allowed the small-market franchise to compete against teams with much bigger budgets by finding undervalued players through revolutionary statistical analysis. The concept sparked the adoption of more data-driven principles across a myriad of sports – with teams and coaches all trying to gain a competitive advantage – but the latest innovation may be the biggest game-changer of the lot. Invented by artificial intelligence company Zone7, the new Silicon Valley algorithm is being used by teams in the NBA, NFL and Premier League as a way to detect injury risk and recommend pre-emptive action. One of those clubs, Liverpool FC, has deployed it to great success this season in their hunt for an unprecedented quadruple, cutting the number of days players have lost to injury to 1,008 from more than 1,500 in 2020/21.

Popularised in the Brad Pitt film Moneyball, groundbreaking analytics almost saw the Oakland A's crowned the kings of baseball back in 2002. General manager Billy Beane's evidence-based, sabermetric approach allowed the small-market franchise to compete against teams with much bigger budgets by finding undervalued players through revolutionary statistical analysis. The concept sparked the adoption of more data-driven principles across a myriad of sports – with teams and coaches all trying to gain a competitive advantage – but the latest innovation may be the biggest game-changer of the lot. Invented by artificial intelligence company Zone7, the new Silicon Valley algorithm is being used by teams in the NBA, NFL and Premier League as a way to detect injury risk and recommend pre-emptive action. One of those clubs, Liverpool FC, has deployed it to great success this season in their hunt for an unprecedented quadruple, cutting the number of days players have lost to injury to 1,008 from more than 1,500 in 2020/21.

Simply put, for the last 200 years, advisers have worked on the principle of information asymmetry, where they have better information than their clients. Today, we are at the point where machine intelligence is gaining information asymmetry over advisers, and that's only going to get more acute and asymmetrical as time goes on. The only possible hope for human advisers is that they co-opt machine intelligence into their process.

Zone7 bases its analysis on more than five million hours of performance data. While it has started pilot programs in MLB and the NHL, its focus is on global soccer, with about three dozen clients spanning Bundesliga, Serie A, Ligue 1 and the English Football League Championship, which is the second division below the Premier League. Its most high-profile success (that it is able to disclose) has been Getafe CF, which is currently in fifth place in Spain's La Liga despite a team wage bill in the league's bottom half. By some measures, they've reduced injuries by 65% with Zone7.

Premier League players could be 25% more susceptible to injury when football resumes because of the intense schedule, research shows. Premier League chief executive Richard Masters is "as confident as we can be" of restarting in June. Time will need to be found for the FA Cup before the 2020-21 season starts in "late August, early September". Based on Project Restart's provisional return date of 20 June, Manchester City players could face 13 games in 49 days. City have played one fewer match than the majority of Premier League clubs but these figures do not take into account the completion of the Champions League, which has the potential of adding another four games should they reach the final - if the tournament can be concluded. Research conducted by artificial intelligence platform Zone7, which specialises in injury risk forecasting and works with 35 professional football teams worldwide, shows that playing eight matches in a 30-day period increases the incidence of injury by 25% when compared with playing four to five matches in the same timeframe.