The challenge of evaluating the intensity of short actions in soccer: a new methodological approach using percentage acceleration

There are several approaches to quantifying physical load in team sports using positional data. Distances in different speed zones are most commonly used. Recent studies have used acceleration data in addition in order to take short intense actions into account. However, the fact that acceleration decreases with increasing initial running speed is ignored and therefore introduces a bias. The aim of our study was to develop a new methodological approach that removes this bias. For this purpose, percentage acceleration was calculated as the ratio of the maximal acceleration of the action (amax,action) and the maximal voluntary acceleration (amax) that can be achieved for a particular initial running speed (percentage acceleration [%] = amax,action / amax * 100).MethodsTo define amax, seventy-two highly trained junior male soccer players (17.1 ± 0.6 years) completed maximal sprints from standing and three different constant initial running speeds (vinit; trotting: ~6.0 km·h⁻¹; jogging: ~10.8 km·h⁻¹; running: ~15.0 km·h⁻¹).ResultsThe amax was 6.01 ± 0.55 from a standing start, 4.33 ± 0.40 from trotting, 3.20 ± 0.49 from jogging and 2.29 ± 0.34 m·s⁻² from running. The amax correlated significantly with vinit (r = –0.98) and the linear regression equation of highly-trained junior soccer players was: amax = –0.23 * vinit + 5.99.ConclusionUsing linear regression analysis, we propose to classify high-intensity actions as accelerations >75% of the amax, corresponding to acceleration values for our population of >4.51 initiated from standing, >3.25 from trotting, >2.40 from jogging, and >1.72 m·s⁻² from running. The use of percentage acceleration avoids the bias of underestimating actions with high and overestimating actions with low initial running speed. Furthermore, percentage acceleration allows determining individual intensity thresholds that are specific for one population or one single player

Measuring physical load in soccer: strengths and limitations of 3 different methods

To investigate the strengths and limitations of different indicators to measure physical load. Furthermore, indicators were evaluated for discrimination between performance levels and playing positions. Methods: Ninety positional match files from 70 elite players and 91 match files from 69 subelite players were collected during 14 official under-18 matches using a local position measurement system. Indicators are calculated from speed, absolute acceleration (acc-abs), or percentage acceleration (acc-%). The acc-% describes the level of acceleration depending on the maximal voluntary acceleration (amax) for each initial running speed. Effect sizes (ES) were used to determine discriminative ability. Results: The number of high accelerations largely depended on the method (absolute threshold [>3 m·s−2 and >4 m·s−2] 120 and 59 efforts; high percentage threshold [>75% amax] 84 efforts). Only a small number of highly accelerated efforts reached speeds considered high-speed running (>19.8 km·h−1: 32.6%). More high acc-% exists from initial running speed >2 m·s−1 (23.0) compared with acc-abs (>3 m·s−2 14.4, >4 m·s−2 5.9). Elite players achieve higher values in most performance indicators, with ES being highest for the number of high acc-% (ES = 0.91) and high acc-abs (>3 m·s−2 ES = 0.86, >4 m·s−2 ES = 0.87), as well as for covered distance in jogging (ES = 0.94). Conclusions: Estimated physical load, discriminative ability of physical indicators, and positional requirements largely depend on the applied method. A combination of speed-based and acc-% methods is recommended to get a comprehensive view

Introduction

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Using position data to estimate effects of contextual features on passing decisions in football

Passes are a performance-relevant parameter in many team sports. They must be played in the highly dynamic and unpredictable contexts of interactive team competitions. The difficulty to plan passes in advance requires real-time decisions and highlights the importance of the information provided by current game contexts. This study estimates the relation between contextual information and passing decisions by analyzing position data of 1379 passing situations tracked during football competitions. In support of previous findings of a scenario-based investigation, open passing lanes, spatial proximity to the ball carrier, team members’ positions in front of the ball carrier, and loose defense by opposing players all significantly increased team members’ odds for receiving passes. In the total sample, the four kinds of contextual information enabled the correct prediction of 41% of the passes played. The prediction rate compares to a base rate of 11% and is substantially higher than that reported for passing decisions made in static game scenarios. Separate analyses of passes categorized according to teams, playing positions, and playfield zones revealed that spatial proximity and open passing lanes were significantly related to passing decisions in all pass categories, while effects of positions in front of the ball carrier and loose defense were found less constantly. Shedding light on the relationship between position-related information and passing decisions, the results indicate what contextual information may help in anticipating passing decisions and ways in which team members may affect these decisions by actively taking corresponding positions

Outplaying opponents—a differential perspective on passes using position data

In recent years, the availability of new tracking technologies has enabled new perspectives on passes played in football. One such perspective includes measuring the number of opponents that are outplayed by a pass (NOO). Various studies substantiate that this measure qualifies as one aspect of the passes’ offensive quality. Given the latent consensus that high-NOO passes indicate clever passing decisions, one aim of this study was to analyze how athletes prioritize contextually embedded passing options when playing passes that differ with regard to their NOO. Another aim was to determine the contributions of pass receivers to completing passes with different NOOs. To this end, position- and speed-related features of 12,411 passing options from 1,379 passing situations tracked during championshipmatches were analyzed. Overall, the findings indicate that decisions to play high NOO passes differ from decisions to play low NOO passes with regard to how contextually embedded passing options are prioritized. The passes’ NOOs increased as the decision-makers’ tendency to pass to loosely defended teammembers with open passing lanes and positions near the ball carrier decreased. Furthermore, higher physical contributions on the part of the pass receivers were observed when pass receivers completed passes with higher NOOs. Based on the findings, passes with a high NOO could be considered risky passes. The presented approach could be adopted to further analyze the circumstances that allow athletes to play such passes compared with those that absolutely do not, which could represent an important step concerning educational programs in football

Packing in football: A differential ecological perspective on passes

Introduction Packing has had its major appearance at the UEFA European Championship 2016. It indicates how many opponents are packed (“taken out” of the game) by a pass (http://www.impect.com/de) and has established as an inherent part of game statistics. In general, passes are more probably played to team members with open passing lanes, standing relatively close to the ball carrier, positioned in front of the ball, and defended loosely by opponent players (unpublished data). The goal of this study was to test whether passes with different packing differ in their relationships to ecological features of the game context. Methods Game data from five football competitions between some of Switzerland’s best-ranked U-18 teams were collected by the Local Positioning Measurement System of the Swiss Federal Institute of Sport. The system records the positions of opponent teams with little time latency and a high data resolution. Time synchronized videos were used to identify passing situations. 1778 completed passes were identified and categorized according to their packing. For each pass, the positions of all 22 players were exported to calculate the team members’ distance to the ball carrier, the openness of the passing lane, the defensive coverage, and the position before or behind the ball. Logistic regressions for binomial data were specified to estimate the effect of the ecological variables on passes with different packing. Results In contrast to the significant effects found when considering all passes, the openness of passing lanes was no ecological information that significantly affected passes with a packing of greater-than-or-equal to two. In a similar way, no effect of defensive coverage was found for passes with a packing of greater-than-or-equal to three. Discussion Passes with a packing of three and more do not show the characteristic effects of open passing lanes or loose defence of the intended receivers. They could, compared to passes with a lower packing, be considered risky passes. The findings may cautiously be interpreted in regard to the athletes’ specific use of ecological information. It might be argued that athletes playing passes with high packing oppress ecological information that are usually more heavily weighted in passing decisions. Or, they more heavily weigh or rely on other information to guide their passing behaviour. More research is required to prove the adequacy of this interpretation

Der Einfluss ökologischer Eigenschaften auf Passentscheidungen im Fussball

Passspiele können in entscheidendem Ausmass zur Teamleistung im Fussball beitragen. Passentscheidungen müssen in komplexen und dynamischen Spielsituationen getroffen werden. Gemäss ökologisch-orientierten Überlegungen nehmen Fussballer in der Umwelt liegende Hinweisreize als Passaffordanzen wahr, welche im Sinne von Handlungsangeboten Passentscheidungen beeinflussen. Eine Szenario-basierte Untersuchung ergab, dass Positionierungen vor dem Ball, in der Nähe des Ballführers, lose Verteidigung sowie offene Passwege ökologische Aspekte darstellen, die Pässe begünstigen (Steiner, 2015). Das Ziel dieser Untersuchung war, die Untersuchung mit echten Spieldaten zu replizieren. Spieldaten von fünf Meisterschaftspartien der höchsten Schweizerischen U-18 Liga wurden analysiert. Die Daten wurden mit dem Local Position Measurement Systems des BASPO erhoben. Das System ist in der Lage, die Positionen aller 22 Spieler mit hoher zeitlicher und räumlicher Auflösung zu erfassen. Mit Hilfe von Videoaufzeichnungen wurden 1778 Situationen mit komplettierten Pässen identifiziert. Zu jeder Passsituation wurden die Positionen aller 22 Spieler exportiert. Für jeden Mitspieler wurden die Positionierung in Relation zur aktuellen Ballposition, die Distanz zum Ballführer, die Verteidigung durch Gegenspieler sowie die Offenheit des Passweges bestimmt. Zusätzlich wurden die Laufgeschwindigkeiten aller Spieler berücksichtigt. Mit logistischen Regressionen wurden die Effekte der ökologischen Variablen auf Passentscheidungen geschätzt. Alle Variablen hatten signifikante Effekte auf Passentscheidungen (alle p < .001). Die odds ratios für Pässe waren erhöht, wenn Mitspieler vor dem Ball positioniert waren, nah zum Ballführer standen, lose verteidigt wurden und offene Passwege aufwiesen. Für Mitspieler mit hoher Laufgeschwindigkeit stieg die Wahrscheinlichkeit eines Passzuspiels zusätzlich an. Das Modell ergab ein Cox & Snell R2 von .137 und ein Nagelkerke’s R2 von .279. Durch das Modell konnten 8.9% der Pässe bzw. 99.1% der Fälle, die keinen Pass erhielten, korrekt vorhergesagt werden. Eine nach Passsituationen getrennte Betrachtung ergab, dass diejenigen Mitspieler mit den höchsten Wahrscheinlichkeitswerten innerhalb einer Situation in 40% aller Fälle dem tatsächlichen Passempfänger entsprachen. Die Resultate bestätigen bisherige Befunde zu Passentscheidungen in computerbasierten Spielszenarien und weisen sogar höhere Pseudo R2-Werte aus. Dies könnte ein Hinweis darauf sein, dass Passentscheidungen im zeitgedrängten Wettkampfsetting noch stärker durch ökologische Hinweisreize beeinflusst werden, als dies in computerbasierten Untersuchungen der Fall ist. Während die statistischen Kennwerte die Bedeutung ökologischer Hinweisreize bestätigen, weist die prädiktive Leistung des Modells Optimierungsbedarf auf. Die berücksichtigten ökologischen Eigenschaften stellen nicht immer ausreichende Bedingungen zur Entscheidungsvorhersage dar. Sie können als Anreiz stiftende (affording) bzw. einschränkende (constraining) Faktoren interpretiert werden (Nitsch, 2009). Ergänzende Untersuchungen zu subjektiven Entscheidungskriterien im objektiv-ökologischen Umfeld sind wünschenswert