Building Individual Player Performance Profiles According to Pre-Game Expectations and Goal Difference in Soccer.

Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players' energy expenditure in 5-min intervals, alongside recording the goal timings and the win and lose probabilities from betting sites. A mathematical model was developed that considers pre-game expectations (e.g., favorite, non-favorite), endurance, and goal difference (GD) dynamics on player effort. Particle Swarm and Nelder-Mead optimization methods were used to construct these models, both consistently converging to similar cost function values. The model outperformed baselines relying solely on mean and median power per GD. This improvement is underscored by the mean absolute error (MAE) of 396.87±61.42 and root mean squared error (RMSE) of 520.69±88.66 achieved by our model, as opposed to the B1 MAE of 429.04±84.87 and RMSE of 581.34±185.84, and B2 MAE of 421.57±95.96 and RMSE of 613.47±300.11 observed across all players in the dataset. This research offers an enhancement to the current approaches for assessing players' responses to contextual factors, particularly GD. By utilizing wearable data and contextual factors, the proposed methods have the potential to improve decision-making and deepen the understanding of individual player characteristics.

Extended Energy-Expenditure Model in Soccer: Evaluating Player Performance in the Context of the Game.

Every soccer game influences each player's performance differently. Many studies have tried to explain the influence of different parameters on the game; however, none went deeper into the core and examined it minute-by-minute. The goal of this study is to use data derived from GPS wearable devices to present a new framework for performance analysis. A player's energy expenditure is analyzed using data analytics and K-means clustering of low-, middle-, and high-intensity periods distributed in 1 min segments. Our framework exhibits a higher explanatory power compared to usual game metrics (e.g., high-speed running and sprinting), explaining 45.91% of the coefficient of variation vs. 21.32% for high-, 30.66% vs. 16.82% for middle-, and 24.41% vs. 19.12% for low-intensity periods. The proposed methods enable deeper game analysis, which can help strength and conditioning coaches and managers in gaining better insights into the players' responses to various game situations.

Sopele music dataset.

Data presented in this article was created using a Croatian instrument called sopela - a traditional hand-made wooden aerophone of piercing sound, characteristic to the Istrian peninsula in western Croatia. The instrument is always played in pair (plural form: sopele), which consists of two voices: a small sopela and a great sopela. The data contains Waveform Audio File format (WAV) files, capturing every possible distinct tone of both sopele, as well as their polyphonic combinations. Additional data encompassed in the provided dataset are music scales and real music pieces, which contain specific traditional melodies. Every melody has a corresponding music sheet, presented in a Portable Document Format (PDF) file, which describes it in a human-readable manner. The specific Istrian scale music notation was applied while creating the music sheets. The data presented here was successfully utilised for developing, training and testing an automatic music transcription (AMT) solution, capable of converting sopele audio recordings into musical scores [1].