Predicting the in-game status in soccer with machine learning using spatiotemporal player tracking data.

An important structuring feature of a soccer match is the in-game status, whether a match is interrupted or in play. This is necessary to calculate performance indicators relative to the effective playing time or to find standard situations, ball actions, and other tactical structures in spatiotemporal data. Our study explores the extent to which the in-game status can be determined using time-continuous player positions. Therefore, to determine the in-game status we tested four established machine learning methods: logistic regression, decision trees, random forests, and AdaBoost. The models were trained and evaluated using spatiotemporal data and manually annotated in-game status of 102 matches in the German Bundesliga. Results show up to 92% accuracy in predicting the in-game status in previously unknown matches on frame level. The best performing method, AdaBoost, shows 81% precision for detecting stoppages (longer than 2 s). The absolute time shift error at the start was ≤ 2 s for 77% and 81% at the end for all correctly predicted stoppages. The mean error of the in-game total distance covered per player per match using the AdaBoost in-game status prediction was - 102 ± 273 m, which is 1.3% of the mean value of this performance indicator (7939 m). Conclusively, the prediction quality of our model is high enough to provide merit for performance diagnostics when teams have access to player positions (e.g., from GPS/LPM systems) but no human-annotated in-game status and/or ball position data, such as in amateur or youth soccer.

Real Time Quantification of Dangerousity in Football Using Spatiotemporal Tracking Data.

This study describes an approach to quantification of attacking performance in football. Our procedure determines a quantitative representation of the probability of a goal being scored for every point in time at which a player is in possession of the ball-we refer to this as dangerousity. The calculation is based on the spatial constellation of the player and the ball, and comprises four components: (1) Zone describes the danger of a goal being scored from the position of the player on the ball, (2) Control stands for the extent to which the player can implement his tactical intention on the basis of the ball dynamics, (3) Pressure represents the possibility that the defending team prevent the player from completing an action with the ball and (4) Density is the chance of being able to defend the ball after the action. Other metrics can be derived from dangerousity by means of which questions relating to analysis of the play can be answered. Action Value represents the extent to which the player can make a situation more dangerous through his possession of the ball. Performance quantifies the number and quality of the attacks by a team over a period of time, while Dominance describes the difference in performance between teams. The evaluation uses the correlation between probability of winning the match (derived from betting odds) and performance indicators, and indicates that among Goal difference (r = .55), difference in Shots on Goal (r = .58), difference in Passing Accuracy (r = .56), Tackling Rate (r = .24) Ball Possession (r = .71) and Dominance (r = .82), the latter makes the largest contribution to explaining the skill of teams. We use these metrics to analyse individual actions in a match, to describe passages of play, and to characterise the performance and efficiency of teams over the season. For future studies, they provide a criterion that does not depend on chance or results to investigate the influence of central events in a match, various playing systems or tactical group concepts on success.

Structure-Based Identification of Inhibitory Fragments Targeting the p300/CBP-Associated Factor Bromodomain.

The P300/CBP-associated factor plays a central role in retroviral infection and cancer development, and the C-terminal bromodomain provides an opportunity for selective targeting. Here, we report several new classes of acetyl-lysine mimetic ligands ranging from mM to low micromolar affinity that were identified using fragment screening approaches. The binding modes of the most attractive fragments were determined using high resolution crystal structures providing chemical starting points and structural models for the development of potent and selective PCAF inhibitors.

A three-stage biophysical screening cascade for fragment-based drug discovery.

This protocol describes the screening of a library of low-molecular-weight compounds (fragments) using a series of biophysical ligand-binding assays. Fragment-based drug discovery (FBDD) has emerged as a successful method to design high-affinity ligands for biomacromolecules of therapeutic interest. It involves detecting relatively weak interactions between the fragments and a target macromolecule using sensitive biophysical techniques. These weak binders provide a starting point for the development of inhibitors with submicromolar affinity. Here we describe an efficient fragment screening cascade that can identify binding fragments (hits) within weeks. It is divided into three stages: (i) preliminary screening using differential scanning fluorimetry (DSF), (ii) validation by NMR spectroscopy and (iii) characterization of binding fragments by isothermal titration calorimetry (ITC) and X-ray crystallography. Although this protocol is readily applicable in academic settings because of its emphasis on low cost and medium-throughput early-stage screening technologies, the core principle of orthogonal validation makes it robust enough to meet the quality standards of an industrial laboratory.

Dissecting fragment-based lead discovery at the von Hippel-Lindau protein:hypoxia inducible factor 1α protein-protein interface.

Fragment screening is widely used to identify attractive starting points for drug design. However, its potential and limitations to assess the tractability of often challenging protein:protein interfaces have been underexplored. Here, we address this question by means of a systematic deconstruction of lead-like inhibitors of the pVHL:HIF-1α interaction into their component fragments. Using biophysical techniques commonly employed for screening, we could only detect binding of fragments that violate the Rule of Three, are more complex than those typically screened against classical druggable targets, and occupy two adjacent binding subsites at the interface rather than just one. Analyses based on ligand and group lipophilicity efficiency of anchored fragments were applied to dissect the individual subsites and probe for binding hot spots. The implications of our findings for targeting protein interfaces by fragment-based approaches are discussed.

High-throughput interrogation of ligand binding mode using a fluorescence-based assay.

Probing the pocket: A high-throughput fluorescence-based thermal shift (FTS) assay utilized different forms of a protein (in gray) to establish the binding mode of a ligand (see picture). The assay serves in the rapid evaluation of structure-activity binding-mode relationships for a series of ligands of Plk1, an important target of anticancer therapy.

Synthesis of cyclic endiamino peptides.

Several building blocks for endiamino peptides, as well as several cyclic endiamino peptides themselves, and pyrazin-6-one, which embodies the endiamino group, were prepared. The variety of synthesized compounds shows the potential of this synthesis in the preparation of many different groups of compounds.