Soccer Analytics (eBook)

Successful Coaching Through Match Analysis

Ian Franks, Mike Hughes (Autoren)

eBook Download: PDF | EPUB

2016 | 1. Auflage
340 Seiten
Meyer & Meyer (Verlag)
978-1-78255-749-4 (ISBN)

Infos

Match analysis in soccer has become more and more important in recent years. Nowadays, no professional soccer club plays a single match without having analyzed their own and their opponents' matches to find the best possible match plan and maximize their success. In this book, Ian M. Franks and Mike Hughes explore soccer analyses and use the results to develop realistic, progressive practices to improve the performance of the individual players and the team. Research from human decision making and motor skill acquisition is directly applied to the coaching process and technical and tactical practices are designed to accommodate these findings. Not only is the players' behavior during practice and matches analyzed but the coaches' as well. This helps evaluate different coaching practices to find your ideal coaching style. Any coach reading this book will find help in developing and improving their coaching. Anyone who wishes to delve more into the science of soccer analysis will find ample material as well as a comprehensive bibliography to better understand the science of soccer!

Ian Franks gained his full English Football Association Coaching Award in 1975. He was intricately involved in developing the Canadian Soccer Association's Coaching Program in the early 1980s. He then took a position as Director of the Centre for Sport Analysis at UBC where he developed the first computer analysis system for soccer. Mike Hughes has conducted research into Match Analysis for over 30 years. He has published over 160 research articles and written and contributed to 24 books. His areas of expertise include modeling, sport system perturbations, tactical and technical game analyses as well as individual movement analysis.

Textauszug

PART 1
MATCH ANALYSIS

Chapter 1
INTRODUCTION

Spectators of a soccer game want to be entertained while observers search for critical information. Needless to say, we all would like to be entertained, but for the coach, watching a soccer game is hard work. Observing playing behaviour is one of the most important tasks a coach has to accomplish, and early research into the process of behavioural observation revealed some interesting findings in the field of social psychology that are relevant when examining the observational skills of soccer coaches. Darren Newtson found that adult observers used breakpoints in action sequences to organize behavioural units of ongoing events for later recall. These units then became units of comprehension and memory. For example, breakpoints in soccer game action for the observer coach could be something as simple as a change in ball possession and each component of memory could be one possession. Unfortunately, due to the length of the game and the myriad of other factors that affect memory recall, most of what the soccer coach observes and remembers about the game is not accurate. This is not surprising since a lot of events (e.g. breakpoints, changes in possession and critical incidents) take place in a 90-minute game. Research findings from our own lab at UBC have shown that at most levels of coaching, from novice to international, observations by coaches are in error by more than 50 % when trying to recall such things as how goals were scored and how shooting opportunities were created.

How then can coaches improve their ability to remember key events during a game? We know that skilled observers develop a specialized set of predictive features and adopt certain monitoring priorities. Therefore the soccer coach must understand key factors in expected performance and have a clear vision of what that expected performance should look like. In order to provide an accurate recall of all game events it would obviously require a considerable amount of practice in observing playing behaviours. However an alternate method of maintaining an accurate account of a 90-minute soccer match is to use a memory aid. This could be as simple as a pencil and paper checklist or as complex as an interactive computer-video analysis system. The level of sophistication of the system is not important; the key elements of any system of analysis are accuracy, relevance and usefulness of information that is collected by the system. For instance, a video recorder can collect and store most of the information from a game, but the game still needs to be analyzed in a manner that can assist the coach in making decisions. The statistical accumulation of such things as number of possessions and number of passes is not informative if the coach requires the information to assess team or individual performance and make preparation for the next game. By way of example let us examine a simple forward pass. Diagram 1 illustrates a pass that is identical in all aspects except the position of one defending player (diagram 1a) or in its location on the field (diagram 1b). In diagram 1a the pass in case 2 is more penetrative and offers more of an attacking threat than does the pass in case 1. Also if we move the position of the pass from the middle of the field to the top of the penalty area as shown in diagram 1b, it is obvious that the pass in case 2 would be considered much more difficult to execute than the pass in case 1 and should eventually lead to a shot on goal.

Diagram 1a

Diagram 1b

Collecting only the number of completed passes without taking into consideration the context in which they were made reduces the richness and informative nature of the data. Therefore, it is important to understand what opportunities these passes and possessions create. Hence the sequential nature (i.e. what leads to what) of data capture then becomes a critical feature in any analysis system. However if this were to be achieved by simply video recording the game without any systematic analysis system to guide the viewing then many of the problems encountered on an initial live viewing of the event may still exist.

How then can a team’s performance be analyzed in a systematic and progressive manner? During an average soccer game each team has possession of the ball approximately 200 times, and since the objective of the game is to score goals, these 200 possessions could possibly translate into 200 goals. Not a very likely occurrence given that league champions usually average only 2 or 3 goals per game. The remarkable fact is that about 99 % of all team possessions end up being lost to the opposing team without a goal being scored. Analyzing a soccer game is therefore a process of recording how, where and why the team lost and regained possession. A detailed analysis of these lost possessions should provide the coach with an overall view of the key factors that were responsible for both good and bad team performance.

Let us take a brief look at these lost possessions. Each possession is lost in a particular area of the field and each loss can be attributed to a particular action. It is obvious that goals can only be scored from certain areas of the field, let’s say within 40 meters of the goal (we realize that there have been exceptions but these account for a very small percentage of all shots). Therefore, the task for the attacking team is to move the ball into an area of the field from which shots on goal can be taken. Team possessions that are lost in these areas would satisfy a large part of the final objective, which is to produce a shot on target that results in a goal, whereas a sub-objective would be to create the shooting opportunity. If possession of the ball is continuously lost in these shooting areas and shooting opportunities do not arise, the problem for the coach is less formidable than if ball possession is lost in areas other than the shooting area. Using this simple logic we can now identify priorities for possession loss as: first, lose possession in scoring a goal (usually 1 % per game for winning teams); second, lose possession in taking a shot at goal (10 % per game for winning teams); third, lose possession after creating a shooting opportunity (20 % per game); and fourth, lose possession in and around the shooting area (40 % per game). As you can see, we are now developing a method for systematically observing the game.

Collecting and categorizing information about how the team lost and gained possession can cover all the attacking and defending events the game has to offer. If we just recorded numbers and totals of discrete events this would provide interesting statistics for spectators and TV viewers, but the observing coach wants to know more. For example, what events led to the shot that missed the target, the lost possession in the defending third of the field or the free kick outside the penalty area? This type of information can only be gathered if a sequential history or story of the game is recorded that allows us to ask “What led to what?” Over the past 30 years notational analysis researchers have developed computer analysis systems that record the sequence of game events; these are then time locked to the video recording. Some systems automate the process such that the camera recognizes the image of all players, the ball and the officials every second of the game. Because of these systems some coaches can now view the statistics (as either numbers or graphs) after the game (or at half time) and then recall video excerpts of key events in the game. This then becomes a very powerful and useful coaching tool and provides important visual feedback for players. In addition to being an excellent coaching tool these notation systems have allowed researchers to collect data from many major soccer competitions to form a large database of information that may provide answers to several questions such as:

• How are goals scored, and what leads to the creation of shooting opportunities?

• What type of defensive tactic yields most regained possessions?

• What are the technical demands of performance?

Let us look in a little more detail at some of the tentative answers to these questions that have been gleaned from soccer match analysis research over more than 60 years.

How Are Goals Scored?

It appears that after reviewing a considerable amount of data from past games, the approach play that led to goals was not significantly different than the approach play that led to shots that did not result in goals. Goals were thought to occur randomly from a population of all shots. These facts were uncovered in one of the earliest examples of comprehensive match analysis undertaken by Charles Reep using a sophisticated hand notation system. In 1968 he published samples of his extensive data collection in what is now the classic paper by Reep and Benjamin. They reported on the collection of match data from 3,213 top level soccer games between 1953 and 1969. The results of the analysis were later supported by several subsequent studies from such coaches as Egel Olsen (former Norwegian national coach), Dick Bate (English FA staff coach), Charles Hughes (director of coaching for the English FA) and from researchers in laboratories throughout the world (see bibliography). For an excellent recent discussion on what has since been termed the winning formulae see an article written by Joao Medeiros, science editor for Wired.co.uk (also cited in the bibliography).

The predominant finding that has surfaced from nearly all analyses is that approximately 80 % of goals are scored from team play...

Produkt-Details

• Erscheint lt. Verlag: 9.9.2016
• Verlagsort: Aachen
• Sprache: englisch
• Themenwelt: Sachbuch/Ratgeber ► Sport ► Ballsport
• Schlagworte: Coaching • Die Mannschaft • Dribbling • Football • german soccer • match analysis • NSCAA • passing game • science of soccer • Tactics • The Team • Training
• ISBN-10: 1-78255-749-0 / 1782557490
• ISBN-13: 978-1-78255-749-4 / 9781782557494