A risk-reward assessment of passing decisions:comparison between positional roles using tracking data from professional men's soccer

Introduction: Performance assessment in professional soccer often focusses on notational assessment like assists or pass accuracy. However, rather than statistics, performance is more about making the best possible tactical decision, in the context of aplayer's positional role and the available options at the time. With the current paper, we aim to construct an improved model for the assessment of pass risk and reward across different positional roles, and validate that model by studying differences in decision-making between players with different positional roles. Methods: To achieve our aim, we collected position tracking data from an entire season of Dutch Eredivisie matches, containing 286.151 passes of 336 players. From that data, we derived several features on risk and reward, both for the pass that has been played, as well as for the pass options that were available at the time of passing. Results: Our findings indicate that we could adequately model risk and reward, outperforming previously published models, and that there were large differences in decision-making between players with different positional roles. Discussion: Our model can be used to assess player performance based on what could have happened, rather than solely based on what did happen in amatch

The tactics of successful attacks in professional association football:large-scale spatiotemporal analysis of dynamic subgroups using position tracking data

Association football teams can be considered complex dynamical systems of individuals grouped in subgroups (defenders, midfielders and attackers), coordinating their behaviour to achieve a shared goal. As research often focusses on collective behaviour, or on static subgroups, the current study aims to analyse spatiotemporal behaviour of dynamic subgroups in relation to successful attacks. We collected position tracking data of 118 Dutch Eredivisie matches, containing 12424 attacks. Attacks were classified as successful (N = 1237) or non-successful (N = 11187) based on the potential of creating a scoring opportunity. Using unsupervised machine learning, we automatically identified dynamic formations based on position tracking data, and identified dynamic subgroups for every timeframe in a match. We then compared the subgroup centroids to assess the intra- and inter-team spatiotemporal synchronisation during successful and non-successful attacks, using circular statistics. Our results indicated subgroup-level variables provided more information, and were more sensitive to disruption, in comparison to team-level variables. When comparing successful and non-successful attacks, we found decreases (p < .01) in longitudinal inter- and intra-team synchrony of interactions involving the defenders of the attacking team during successful attacks. This study provides the first large-scale dynamic subgroup analysis and reveals additional insights to team-level analyses

Prediction Aided Tapering In rheumatoid arthritis patients treated with biOlogicals (PATIO): protocol for a randomized controlled trial

Background: Biological disease-modifying anti-rheumatic drugs (bDMARDs) are effective in the treatment of rheumatoid arthritis (RA) but are expensive and increase the risk of infection. Therefore, in patients with a stable low level of disease activity or remission, tapering bDMARDs should be considered. Although tapering does not seem to affect long-term disease control, (short-lived) flares are frequent during the tapering process. We have previously developed and externally validated a dynamic flare prediction model for use as a decision aid during stepwise tapering of bDMARDs to reduce the risk of a flare during this process. Methods: In this investigator-initiated, multicenter, open-label, randomized (1:1) controlled trial, we will assess the effect of incorporating flare risk predictions into a bDMARD tapering strategy. One hundred sixty RA patients treated with a bDMARD with stable low disease activity will be recruited. In the control group, the bDMARD will be tapered according to “disease activity guided dose optimization” (DGDO). In the intervention group, the bDMARD will be tapered according to a strategy that combines DGDO with the dynamic flare prediction model, where the next bDMARD tapering step is not taken in case of a high risk of flare. Patients will be randomized 1:1 to the control or intervention group. The primary outcome is the number of flares per patient (DAS28-CRP increase > 1.2, or DAS28-CRP increase > 0.6 with a current DAS28-CRP ≥ 2.9) during the 18-month follow-up period. Secondary outcomes include the number of patients with a major flare (flare duration ≥ 12 weeks), bDMARD dose reduction, adverse events, disease activity (DAS28-CRP) and patient-reported outcomes such as quality of life and functional disability. Health Care Utilization and Work Productivity will also be assessed. Discussion: This will be the first clinical trial to evaluate the benefit of applying a dynamic flare prediction model as a decision aid during bDMARD tapering. Reducing the risk of flaring during tapering may enhance the safety and (cost)effectiveness of bDMARD treatment. Furthermore, this study pioneers the field of implementing predictive algorithms in clinical practice. Trial registration: Dutch Trial Register number NL9798, registered 18 October 2021, https://www.trialregister.nl/trial/9798. The study has received ethical review board approval (number NL74537.041.20)

Smart data scouting in professional soccer:Evaluating passing performance based on position tracking data

Sports analytics in general and soccer analytics, in particular, have evolved in recent years due to the increased availability of large data amounts of (tracking) data. Especially in terms of evaluating tactical behavior, data science could change the way we think about soccer. In this study, we evaluate passing performance in soccer to prove the hypothesis that tactical behavior in team sports can be analyzed based exclusively on tracking data. To prove this point, we explore the relationship between changes in spatiotemporal variables in relation to passing and key performance indicators. Based on our results that demonstrate the ability of spatiotemporal variables to predict pass accuracy and key performances indicators on an individual level, we confirmed our hypothesis. Furthermore, we calculated a simple composite performance indicator to evaluate passes and players based on tracking data. In conclusion, our results can be used as an approach for real-time evaluation of tactical behavior and as a new method to scout and evaluate players in soccer and team sports in general

Machine learning in men's professional football:Current applications and future directions for improving attacking play

It is common practice amongst coaches and analysts to search for key performance indicators related to attacking play in football. Match analysis in professional football has predominately utilised notational analysis, a statistical summary of events based on video footage, to study the sport and prepare teams for competition. Recent increases in technology have facilitated the dynamic analysis of more complex process variables, giving practitioners the potential to quickly evaluate a match with consideration to contextual parameters. One field of research, known as machine learning, is a form of artificial intelligence that uses algorithms to detect meaningful patterns based on positional data. Machine learning is a relatively new concept in football, and little is known about its usefulness in identifying performance metrics that determine match outcome. Few studies and no reviews have focused on the use of machine learning to improve tactical knowledge and performance, instead focusing on the models used, or as a prediction method. Accordingly, this article provides a critical appraisal of the application of machine learning in football related to attacking play, discussing current challenges and future directions that may provide deeper insight to practitioners

Not Every Pass Can Be an Assist:A Data-Driven Model to Measure Pass Effectiveness in Professional Soccer Matches

In professional soccer, nowadays almost every team employs tracking technology to monitor performance during trainings and matches. Over the recent years, there has been a rapid increase in both the quality and quantity of data collected in soccer resulting in large amounts of data collected by teams every single day. The sheer amount of available data provides opportunities as well as challenges to both science and practice. Traditional experimental and statistical methods used in sport science do not seem fully capable to exploit the possibilities of the large amounts of data in modern soccer. As a result, tracking data are mainly used to monitor player loading and physical performance. However, an interesting opportunity exists at the intersection of data science and sport science. By means of tracking data, we could gain valuable insights in the how and why of tactical performance during a soccer match. One of the most interesting and most frequently occurring elements of tactical performance is the pass. Every team has around 500 passing interactions during a single game. Yet, we mainly judge the quality and effectiveness of a pass by means of observational analysis, and whether the pass reaches a teammate. In this article, we present a new approach to quantify pass effectiveness by means of tracking data. We introduce two new measures that quantify the effectiveness of a pass by means of how well a pass disrupts the opposing defense. We demonstrate that our measures are sensitive and valid in the differentiation between effective and less effective passes, as well as between the effective and less effective players. Furthermore, we use this method to study the characteristics of the most effective passes in our data set. The presented approach is the first quantitative model to measure pass effectiveness based on tracking data that are not linked directly to goal-scoring opportunities. As a result, this is the first model that does not overvalue forward passes. Therefore, our model can be used to study the complex dynamics of build-up and space creation in soccer

Healthcare utilization and economic burden of difficult-to-treat rheumatoid arthritis: A cost-of-illness study

Objectives: To determine the impact of difficult-to-treat rheumatoid arthritis (D2T RA) on (costs related to) healthcare utilization, other resource use and work productivity. Methods: Data regarding healthcare utilization, other resource use and work productivity of 52 D2T (according to the EULAR definition) and 100 non-D2T RA patients were collected via a questionnaire and an electronic patient record review during a study visit. Annual costs were calculated and compared between groups. Multivariable linear regression analysis was performed to assess whether having D2T RA was associated with higher costs. Results: Mean (95% CI) annual total costs were €37 605 (€27 689 - €50 378) for D2T and €19 217 (€15 647 - €22 945) for non-D2T RA patients (P<0.001). D2T RA patients visited their rheumatologist more frequently, were more often admitted to day-care facilities, underwent more laboratory tests and used more drugs (specifically targeted synthetic DMARDs), compared with non-D2T RA patients (P<0.01). In D2T RA patients, the main contributors to total costs were informal help of family and friends (28%), drugs (26%) and loss of work productivity (16%). After adjustment for physical functioning (HAQ), having D2T RA was no longer statistically significantly associated with higher total costs. HAQ was the only independent determinant of higher costs in multivariable analysis. Conclusions: The economic burden of D2T RA is significantly higher than that of non-D2T RA, indicated by higher healthcare utilization and higher annual total costs. Functional disability is a key determinant of higher costs in RA

Difficult-to-treat rheumatoid arthritis: contributing factors and burden of disease

OBJECTIVES: Treatment of difficult-to-treat (D2T) RA patients is generally based on trial-and-error and can be challenging due to a myriad of contributing factors. We aimed to identify risk factors at RA onset, contributing factors and the burden of disease. METHODS: Consecutive RA patients were enrolled and categorized as D2T, according to the EULAR definition, or not (controls). Factors potentially contributing to D2T RA and burden of disease were assessed. Risk factors at RA onset and factors independently associated with D2T RA were identified by logistic regression. D2T RA subgroups were explored by cluster analysis. RESULTS: Fifty-two RA patients were classified as D2T and 100 as non-D2T. Lower socioeconomic status at RA onset was found as an independent risk factor for developing D2T RA [odds ratio (OR) 1.97 (95%CI 1.08-3.61)]. Several contributing factors were independently associated with D2T RA, occurring more frequently in D2T than in non-D2T patients: limited drug options because of adverse events (94% vs 57%) or comorbidities (69% vs 37%), mismatch in patient's and rheumatologist's wish to intensify treatment (37% vs 6%), concomitant fibromyalgia (38% vs 9%) and poorer coping (worse levels). Burden of disease was significantly higher in D2T RA patients. Three subgroups of D2T RA patients were identified: (i) 'non-adherent dissatisfied patients'; (ii) patients with 'pain syndromes and obesity'; (iii) patients closest to the concept of 'true refractory RA'. CONCLUSIONS: This comprehensive study on D2T RA shows multiple contributing factors, a high burden of disease and the heterogeneity of D2T RA. These findings suggest that these factors should be identified in daily practice in order to tailor therapeutic strategies further to the individual patient

Monocyte activation and disease activity in multiple sclerosis. A longitudinal analysis of serum MRP8/14 levels

In active multiple sclerosis (MS) lesions macrophages expressing myeloid related protein (MRP) 8/14 are present. The aim of this study was to determine whether serum levels of MRP8/14 complexes are related to disease activity in MS. In a longitudinal study of 16 relapsing remitting (RR) MS patients that underwent monthly gadolinium diethylentriaminepenta acid (Gd-DTPA) magnetic resonance imaging (MRI), the relation between serum MRP8/14 levels and disease activity was investigated. Patients were participating in a monoclonal antibody study targeting a specific T cell population (Vbeta5.2/5.3+ T-cells). In time, within patients large variations in serum MRP8/14 levels were observed. Serum MRP8/14 levels were not related to changes in clinical disease activity or increase in Gd-DTPA lesion enhancement. Neither did comparison of active (>1 relapse in follow-up period) with inactive (0-1 relapse) MS patients reveal any differences in MRP8/14 levels. Therefore, we conclude that although MRP8/14 expression is a good histopathological marker for monocyte activation, serum levels of these proteins do not correlate with disease activity in RR MS