This enabled the model to specify which one out of the previously detected objects was indeed the ball, as it was moving throughout the footage as a ball would be expected to move. By using this approach, the ball could be tracked even when it was not visible on the video footage. For instance, the computer continued to track the ball even when a player picked it up before a penalty kick and happened to hide it from the camera. Once both players and the ball have been detected, the following step is to determine their location on the full football pitch.
The challenging part in this section is the fact that the camera is continuously focusing on different parts of the pitch rather than the pitch as a whole. To solve this issue, Stein had to produce a static camera shot by creating a panoramic view of the complete stadium using a subset of input frames from the video footage i. He was then able to identify how two different images connected together, or detect whether one image was simply a subset of a larger image.
The homography calculation then enabled Stein to project each of the frames from the video footage into the panoramic view of the pitch as a unique reference frame and fully visualise where on the full pitch each frame took place. With all players and the ball correctly identified and their position accurately projected on a panoramic view, the next step was to project these player locations into a normalised football pitch to start generating usable positional data for further analysis.
By providing the system with a standard image of a football pitch, a user can select a minimum of four points both from the panoramic view and their image of the pitch in order for the system to use the homography calculations from the panoramic view and translate them into the standard image of the pitch.
This allows the system to automatically plot accurate player positional data on a standard diagram of a football pitch. Player locations and movements illustrated in real-time on a diagram of the pitch on the top right corner Source: Manuel Stein at FC Barcelona Sports Tomorro w. Stein took his research further by incorporating the tracking of elements in a match that are not clearly visible to a computer, areas such as the dependencies, influences and interactions between players during the various scenarios of a game.
For a fully automated football analysis system to work, this context information that is obvious to humans also needs to be taken into account and measured by the computer. In a dynamic team sport like football, players are more than simple and independently moving dots on a pitch.
One way to automatically measure contextual information from player positional data was to identify the specific regions on the pitch that are controlled by the different players. Stein argued that each player has a surrounding area around them that he fully controls based on his position on the pitch.
The size and shape of these interaction spaces are influenced by player speeds and directions, as well as the distance between the players and the ball. This is because players further away from the ball may have more time to react. On top of that, competition between two opposing players to control a certain zone has also an impact on the shape of these interaction spaces, as players from the opposing team will aim to restrict certain opposing player movements. Therefore, when defining interaction spaces on the football pitch, Stein aimed to consider these interdependencies that may restrict a player from reaching a particular zone before an opposing player to maintain ball possession.
Lastly, Stein was able to leverage the pitch visualisations of the previously recorded positional data and enrich it with additional context information that clearly illustrates each interaction space in real-time. An alternative way of contextualising automatic tracking data was the inclusion of free spaces.
Stein calculated free spaces by segmenting the pitch into grid cells of 1 squared metre. He then assigned each respective cell to the player with the highest probability of reaching that cell in relation to the distance to the cell, their speed and direction of movement. Similarly to interaction spaces, free spaces where the cells from the grid that a player could reach before any other opposing player.
Ultimately, free spaces represented the pitch regions a specific team or player owned. To evaluate which free zones were more meaningful for analysing, Stein ranked all free spaces on the pitch by their value in relation to their respective sizes, number of opposing players overlapping such spaces and the distance to the opposing goal. Stein expanded his concepts of region control on a football pitch by using similar calculations to those of interaction spaces to create a model that highlights the dominant regions for each team.
These dominant regions are calculated by looking at areas on the pitch that can be reached by at least 3 players of the same team simultaneously. Ultimately, they represent the areas in which a particular team has substantially more control over the other. Similarly, Stein extended the concept of interaction spaces to calculate player cover shadows, referring to the area a player can cover in relation to the position of the ball. In other words, a player has full control to prevent a ball from reaching their cover shadow region.
Cover shadows can be thought of as a hypothetical light source coming from the ball at a degree angle. These cover shadows represent the regions that the player is able to control before the ball gets to them. When looking at the possible applications of his automated tracking system, Stein had to consider the roles of Performance Analysts and the coaches. For a Performance Analyst, video and movement data are key when analysing the strengths and weaknesses of their team and the opposition.
On one side, analysts have a window on their screens with their video analysis software opened, such as SportsCode or Dartfish, to notate events and analyse playing actions. While on the other side, they have another window with the original video footage of the match that they use to verify and interpret any observations captured from their coding. Often what this means is that the analyst is looking at two different windows and comparing them to one another.
While this is common practice in the field of Performance Analysis, the exercise of switching focus between two screens may often prove to be an inefficient approach to video analysis. Stein aimed to solve this problem by combining the benefits of the visualisation of the pitch from his new automatic player tracking system with the original match footage.
By simply inverting the homography from the abstract pitch into the video footage, he was able to draw visualisations directly on the real pitch. It was also able to illustrate the best passing options available to the player with the ball.
This novel tracking method provides an invaluable automatic measurement of the context of a match situation. However, similar to any other analytical tools, it needs to be correctly applied in order to make a difference to team and player performance. Aside from the clear operational efficiencies brought by the automation of tedious notational work, the benefits in knowledge acquired from this system needs to be appropriately incorporated into the analysis loop.
For instance, data on free spaces can be used to automatically detect suboptimal movements from players and suggest potential improvements for such behaviours. For example, an analysts can select specific situations where there was a shot on goal or dangerous play by the opposition to then identify which of their own players had control over free spaces that could have prevented such occasion.
Once a selection of possible players have been identified, analysts can assess which one of those players lost control of their space the fastest and how such player could have kept control over his opponent. The identified player can then receive information about which should have been his optimal position on the pitch and their control of field space in order to reduce the free spaces towards his own goal left to be exploited by their opponents. This means that the system can automatically suggest improvements in collective behaviour based entirely on the contextual information being processed.
The system also offers interactivity, where analysts and coaches can drag and drop players around the pitch to explore the different control spaces the player would benefit from if they were in a different location of the pitch. This gives coaches and analyst the possibility to interact with the analysis and to adapt the system based on their own acquired knowledge of the sport. Automated systems such as the one developed by Manuel Stein are bringing exciting levels of innovation to the sport by directly integrating data and video together.
Thanks to these systems, football experts, coaches and analysts become more aware of the power of analytics once they are shown the context of real world scenarios, which in turn leads to better analytical approaches being developed that are better incorporated into the daily realities of the roles of analysts and coaches.
Ultimately, it reduces or completely removes numerous tedious and time consuming work performed by analysts today in a revolutionary way that frees up time away from simple data collection which can in turn be placed in more dedicated and advanced analysis of the sport. Stein, M. Director's cut: Analysis and annotation of soccer matches.
IEEE computer graphics and applications , 36 5 , The first step that the NFL Football Operations team took to figure out what should be answered with the use of data is to try to understand what the general public thinks about when they watch an NFL game.
This run by Zeke Elliot eventually allowed Dallas to successfully move further down the field and score points. Statisticians at the NFL then tried to understand what can be learned from a play like this one by breaking down the play to obtain as many insights on the teams involved, the offence, the defence, and even the ball carrier. An initial eye test by simply looking at the video footage told the analysts that in this particular play Zeke Elliot - the ball carrier - had a significant amount of space in front of him to pick up those 11 yards.
But how could data be applied to this play to tell a similar story? To do so, NFL analysts first needed to take a look at the data and information that was being collected from that play, to understand what was available to them and the structure of the datasets that will allow them to come up with possible uses for that data. There are three types of data being collected and used by the NFL analytics teams: play level data, event level data and tracking level data.
Each one of these types of data present different levels of complexity, with some having been around for longer than others. This data contains the largest amounts of historical records and includes variables like the down, distance, yard line, time on the clock, participating teams, number of time outs and more. It also includes some outcome variables like number of yards gained, passer rating to evaluate QBs, win probability and expected points.
This data is generated from notating video footage. It is usually performed by organisations such as Pro Football Focus or Sports Info Solutions by leveraging their football expertise. These companies tag events using video analysis software and collect data points such as the offensive formation, number of defenders in the box, defenders closer to the line of scrimmage, whether a cover scheme was man versus zone, the run play called and so on.
This type of data refers to 2-D player location data that provides the xy coordinates as well as the speed and direction of players. It tracks every player during every play of every game. This is the most novel type of data being collected by the NFL. Player tracking data was only started to be shared with teams from the season onwards. The sample sizes of data that is available for NFL analysts to come up with new metrics varies for each one of these data types.
When it comes to play data, there is an average of plays per game, and games played in a single season. This means that for the longest time in the sport, analysts have had a maximum of almost 40, plays per season to figure out the answers to NFL analytics questions. A similar scenario is true with event data, where the dataset available to NFL analysts will be a multiple of the number of observations you are producing through the notation of events from a maximum of 40, plays per season.
A very different scenario occurs with player tracking data, where the sample size is substantially larger. With 2-D player location of each player being tracked at 10fps on plays that usually last 7 seconds, the data collected jumped from those observations plays per game in play-level data to between , and , observations for a single game for tracking-level data. This brought a more complex dataset to the sport and opened the door to new questions and metrics to be explored by NFL analysts.
There are various approaches that the NFL analyst could have taken to evaluate the running play by Dallas where Zeke Elliot gained 11 yards. Ultimately, they wanted to figure out what was the likelihood of Zeke Elliot picking up those 11 yards in that running play. One of these approaches was to assign a value to the play to evaluate how the running back performed by using metrics like yards taken, win probability or expected points.
By using this play level data, analysts would be merely calculating the probability of those 11 yards being achieved using simple descriptive metrics, such as the fact that it was a 3rd down and 1 yard-to-go in a certain location of the field during the first minutes of a scoreless match. However, this approach would be missing the amount of credit that the running back, the offensive line and the offensive team should really receive from this outcome given the specific situation they faced.
Another approach was to leverage event level data to provide additional context of the play. However, these approach may have also shown positive results due to the relative large yardage gain Zeke Elliot achieved for the run. Instead, NFL analysts decided to make use of the 2D player tracking data for that play to come up with the spatial mapping on the field. By having a spatial mapping of the field, analysts could visualise the direction and speed in which each player was moving during the duration of the run, as well as what percentage of space on the field was owned by different players of each team.
This gave analysts an idea of the areas that were owned by the offence and the ones owned by the defence, providing them with better understanding of the amount of space in front of the running back, Zeke Elliot, to take on extra yardage. The information obtained from the spatial mapping could then be used to calculate yardage probabilities given the extra condition of space to more accurately assess how well the offensive team performed. In this diagram above, it is clear that the offense owned most of the space in front of Zeke Elliot, not only 11 yards ahead but even 15 yards in front of the running back, with defenders nowhere close to him.
As oppose to evaluating the play with play or event level data, using tracking data raised further questions on the performance of Zeke Elliot on that play, as it may not be as positive as the other approaches may have suggested given the amount of space he had in front of him.
Following this example, NFL analysts next tried to answer the question of how to leverage player tracking data more widely to better understand what happens during plays. The NFL Football Operations analysis team wanted to learn more about how this data could be used to compare the performance of players given the positioning, direction and speed of all 23 players on the field.
More specifically, it involved understanding the probability distribution of all possible yardage increments - i. A probability distribution that is based on yardage increments could then be explored further to provide analysts additional insights on first down probability, touchdown probability or even probability of losing yardage on a given play in spatial mapping terms.
Ultimately, this probability distribution could be turned into an expected yards metrics for running backs by multiplying each yard by the probability of reaching that yardage and summing up all the values together. The main goal of the NFL Football Operations team was to better understand player and team performance by leveraging the new xy spatial data from player tracking to come up with new metrics, such as expected yards, touchdown probability or run play. Sunday Night Football and other media broadcasters also showed a strong interest in using this new metrics to better evaluate performances on air.
In their first attempt at producing new metrics from player tracking data, NFL analysts partnered with data scientists from Amazon Web Services AWS to figure out how this large dataset of player tracking data could be used to come up with new football metrics. Unfortunately, after trying a wide set of tools, ranging from traditional statistical methods to gradient boosting and other machine learning techniques, the NFL Football Operations and AWS partnership never produced results that were satisfactory enough to be used by NFL Next Gen Stats Group or the media.
In order to unblock this situation and produce a metric from tracking data that would match what was seen in the video footage, the NFL Football Operations team leveraged the crowd sourcing wisdom in football statistics through the Big Data Bowl , an event they organise since and that was also sponsored by AWS.
Since player tracking data has not been around for a long time, this event enabled the NFL to understand what the right questions to ask from this data are and how to go about answering them. The Big Data Bowl also serves core NFL data analytics enthusiasts who want extra information on the sport by helping them understand more about the NFL through more intuitive metrics that more clearly reflect how fans think about the game. For the past couple of years, this event has also proven to be a great opportunity for NFL innovation, as it has successfully tapped into the global data science talent to solve problems that a team of data scientists at AWS and the NFL could not resolve on their own.
The first Big Data Bowl in saw 1, people sign up to take part, with final submissions from having completed the task given. Out of these pool of analysts and data scientists, 11 went on to be hired by NFL teams and vendors. The winner of the competition is now an analyst for the Cleveland Browns. The success of the Big Data Bowl edition meant that the NFL Football Operations would decide to take advantage of the Big Data Bowl event to develop their highly anticipated expected yards metric from the 2D player tracking data.
The NFL Football Operations team shared the exact player tracking data with the participants in the event, who were given the task of predicting where the running back would be after a handoff play, such as the one earlier discussed between Dallas and Kansas. By receiving this player tracking data, participants now had valuable data points specifying the positions of all the players on the field, their speed, the number of players in front of the running back, who those players were, and more.
The competition launched in October , when data was shared and released by the NFL. There were a total of 2, submissions for the event, with participants from over 32 countries. The launch was followed by a 3-month model building phase to allow teams to develop their algorithms.
These algorithms were later evaluated in real time during the 5-week model evaluation phase of the competition. The competition used Kaggle as their main data science platform to encourage interactions and communication across teams through forums. It also provided a live leaderboard where teams could see how well their algorithms were performing against other teams.
Team scores were completely automated based on how accurate the algorithms were against real data. They eventually presented their model in the NFL Scouting Combine event that was attended by more than teams and club officials. The calibration of their model showed an almost perfectly calibrated model where their predicted number of yards closely matched the observed number of yards from an out of sample dataset.
Their model was able to take data from a carry and predict the yardage that carry would achieve, not only for small gains of 3 to 5 yards but also for longer yard gains of 15 to 20 yards, which are rarer in the sport. Thanks to their model, an expected yards metric could be produced for every running play. This now provides a valuable tools to assess performance of running plays such as the one by Zeke Elliot.
For example, when a player takes 29 yards from a run, if the model calculated an expected yardage gain of 25 yards for that run given the spacing the running back had at the handoff, that player should only get credited for having achieved 4 yards above the average. This new way for interpreting a 29 yards run would not have been possible unless a model successfully conditioned its probability calculation based on the space available to the running back to determine whether that player has performed above or below expectation.
The benefits of the Big Data Bowl format was that unlike hackathons, where participants may only get one or two weekends to produce something of value, this type of event enabled enough time for the teams to navigate the complex player tracking data set and come up with actionable insights. The NFL was then able to immediately obtain and share the new derive metrics with the media and their Next Gen Stats group to be used for their football analytics initiatives.
Thanks to this approach, clubs can now better evaluate their running backs. Moreover, other industries, such as the growing betting industry in the USA may also benefit from the development of expected yards for their betting algorithms. Lastly, expected yards are now being widely used by NFL broadcaster to show whether running backs are performing well or not during the duration of a game.
Metrics like this one would not have been possible without the NFL tapping to a global talent pool of data scientist to help them come up with this novel expected yards metric. The NFL is continuing to run their Big Data Bowl this year, with their edition being a lot more open ended than previous editions. This time the task focuses on defensive play. They are sharing pass plays from the season and are asking participants to come up with a model that defines who are the best players in man coverage, zone coverage, how can the model identify whether the defence is man or zone, how to predict whether a defender will get a penalty and what types of skills are required to be a good defensive player.
It leaves the interpretation and approach to the participants to define and allows them apply the right conditioning to the data provided. This approach of opening your data to the public in order to push data innovation forward has proven successful and would be interesting to see if other sports will adopt similar initiatives.
Performance Analysts are responsible for producing quantitative information that allows coaches to quickly identify areas requiring attention. This information is primarily delivered through the provision of objective statistical and visual feedback. It involves the selection of video clips that coaches can use to engage in detailed discussions with players, identifying performance areas that need improvement and making training decisions. Video feedback technology has become a major resource as more coaches now rely on video highlights as a guide to enhance training of their players.
The introduction of technology in these informative and constructive interactions in recent years has made the role of the performance analysis field a critical part in coach-athlete communication. Unlike in other sport science disciplines, the role of a Performance Analyst is extremely ingrained in the coaching process. Analysts have become the technology translators between coaches and players.
They aim to provide coaches and players with an immediate performance advantage through the delivery of accessible video feedback and targeted data reporting. Inevitably, the success of the coaching feedback process in developing athletes and improving team performance heavily depends on the communication between coaches and analysts. In order for such delivery to be successful, it is important to understand the way coaches and analysts interact as well as create and maintain working relationships.
Analysts provide coaches with objective quantitative and qualitative information to fill in the gaps left by the natural limitations of human cognition. On top of that, their judgement may also be influenced by bias triggered by emotions that influence the accuracy of their evaluations and affect the extrinsic feedback they provide to their players.
Performance Analysts attempt to solve for these qualitative and subjective observations made by coaches by complementing them with additional feedback based on a more systematic and objective analysis in the form of videos, images, quantitative and qualitative findings. Technology developments over recent years have brought new ways for analysts to communicate key performance insights to coaches in more graphical and visually impactful forms.
However, the method used to deliver such information may vary with the context of the situation and the style of the coach at the club. A coach may change their coaching and leadership style between training sessions and competitive matches, ranging from a more democratic, person-centered approach to a more authoritarian or autocratic one.
This coaching style may also be influenced by the type of sport, gender, age and level of the athletes. An analyst should carefully judge the preferences and character of the coach and the context of the situation in order to decide when, where and how to deliver the information to the coach. The system used should also be dictated by the information needs of the coach.
In competitive sporting environments, most communication takes place verbally. Therefore, coach-analyst interactions usually take place by briefing the coach or face-to-face discussions in which verbal communication skills are key. Some examples of delivery methods employed by analysts include:.
Quantitative information frequency counts. This may include pre-match insights through objective performance profiling that expose the strengths and weaknesses or players and oppositions. This quantitative information, such as match statistics, may be presented as tables, charts or diagrams of the playing field, showing the location of events, while clearly indicating how the team is playing and highlighting areas where performance can be improved.
Qualitative information context through video. Video analysis packages are created to provide detailed qualitative information to coaches, where they can interactively view video highlights on specific areas of interest. By providing videos to coaches, analysts ensure that the context lost from simple frequency counts can be recovered. With this additional context from the video replays, coaches can have a more in-depth evaluation of performance issues, understand why certain problems occurred and make adjustments to enhance future performance.
During the delivery of these video highlights, analysts may want to point out specific features that they want coaches to notice to prevent overwhelming them with too much information and keep them focused on the most relevant points. Once a coach is able to gather enough information from both quantitative and qualitative information, they may want the analyst to produce a video package with a shortlist of selected clips to use in discussions with players.
Data and video can be collated on opponents prior to facing them to highlight areas of strength and weakness and provide a comprehensive picture of what can be expected in upcoming matches. It enables coaches to formulate a strategy to counteract the opposition and exploit their weaknesses.
Some analysts also analyse training sessions to assess the effectiveness of aspects of performance being tested in training and evaluate behavioural aspects that could influence team selection. Performance analysts often code matches live, with statistical information and specific video instances shared between devices for review by coaches in real-time, and players at half-time. They generate continuous feedback for coaches to make timely changes during the course of the event. Alternatively, analysts may also go to the dressing room and show a coach clips and stats in person.
Analysts often review team and individual performance in detail after the match has ended, allowing coaches to evaluate performance and plan future training. Post-match analysis feedback sessions play an integral role in the coaching process and analysts tend to be at the core of the information used in these sessions.
Analysts should focus on understanding the requirements for successful coaching practice and becomes an asset for the coach to succeed at their role. They should continuously seek opportunities to engage and connect with the head coach and the rest of the coaching staff.
One of the most frequent opportunities to do so that are presented to analysts are during review sessions, where analysts sit down with coaches to discuss and assess the analysis together. It is then that analysts have a great opportunity to gain the trust of the coach and offer their own independent assessments to show their value. Trust can work in both ways, for the coach to know that the analyst is giving them relevant and valuable information but also for the analyst to know that the coach is going to understand and use that information in the correct way.
It can also give the analyst a boost in confident to know that their coach considers them a competent and valuable member of staff. However, this trust can only be achieved by successfully fostering a positive working partnership with the coach through, amongst others, mutual respect, openness and honesty. One of the first steps an analyst starting in a new team should aim to do during the building phase of the relationship with the coach is to clearly understand what the expectations of working practice and hierarchies are at their new club.
Only when that trusting relationship has been established is the analyst able to adequately offer improvement to processes, such as tactical suggestions or offer new ideas for ways a coach could engage with their players. However, while there is sometimes room for negotiations around the design of analytical processes and defining the measures of successful performance, the common perception within most coach-analyst relationships is that the analyst is often limited to purely collecting the information as directed by the coach.
This is especially the case with experienced coaches, who know what they want and how they want it, leaving analysts little room to deviate from the direct instructions on how analysis should be performed and delivered at the club.
Often, these authoritarian coaches impose high workload levels and demand numerous resources from the analyst to support their needs when making reliable technical and tactical appraisals of performance. The domineering power exerted by these coaches over their athletes and backroom staff can truly shape the nature of their working relationships, including those with analysts.
Analysts may feel that new ideas are at risk of falling on deaf ears or being shot down if the right relationship has not been reached with the head coach. It is important that the analyst acknowledges the working environment in front them and learns to navigate the politics involved in succeeding in an elite sport environment. For instance, studies have shown that coaches often place significant importance to social interactions with other members of their backroom staff as they perceive them as a mechanism to maintain and control the balance of their status of power.
This is why social gatherings, even when portrayed as non-work related, are often compulsory events for analysts to attend. Not only end of season awards or team meals during away travel but also get togethers or socials may often be considered obligatory socialising for an analyst. These situations often present opportunities for analysts to interact with coaches outside of the pressures of the competitive environment.
A game of pool, a football kickabout or a round of golf removes everyone from the daily working environment and puts them in a relaxed situation in which social interactions can help build a more co-operative relationship between analysts, coaches and the wide backroom staff members. Even when at work, analysts should sit at the coaches table at lunch, be there for team meetings, and involve themselves where they can. A great challenge for analysts is to be able to effectively manage this coach-dominated relationship.
However, the reality is that, due to factors like job insecurity, most analysts feel that the way to gain respect and trust from the coach is to offer their unconditional support to the coach, as they ultimately hold a position of maximum authority. Analysts are highly dependable on the relationship with their coach. Establishing a connection early on may be critical in dictating whether the coach would want the analyst to continue in the team, even before the analyst has had a chance to demonstrate his or her skills.
Whether there is true appreciation and respect towards the coaches and their decisions, or whether the analyst is struggling to find motivation when in a difficult working environment, being respectful at all times is key to survival in a dynamic, competitive and pressured industry. Even when pressure rises, analyst should be able to remain calm under this pressure and not let emotions interfere in their communication with coaches.
Unfortunately, since the hierarchical coach-analyst relationship is dictated by the coach, analysts will often see themselves on the losing end when challenging a coach, even when the coach is in the wrong. For these reasons, conflict management, both proactive and reactive, together with openness, positivity and motivation, become crucial elements in maintaining a positive working relationship between analysts and coaches. Any concerns or issues from analysts should be raised and communicated in the right way, at the appropriate time and providing adequate solutions.
Approachability and getting to know the individuals. Moreover, building strong working relationships with other cooperative and supportive colleagues can be extremely beneficial to analysts. An analyst should be able to navigate the micro-politics prevalent within high performance teams by establishing himself or herself as the expert in their field and within their remit of work by producing high quality work in a timely manner that contributes to a harmonious working environment.
They also need to be approachable to allow them to really engage with their coaches and peers and get to know them well at an individual level. Getting to know the coaches as individuals can make the analyst more sensitive to the ways in which each coach likes to be approached and given key information.
Analysts should be able to listen effectively and adapt their communication style not only to fit coaches but also with the wider backroom team and players. They should listen twice as much as they talk to be able to clearly understand and translate coach directions into numbers or quantifiable information.
Coaches are busy people. Coaches do not always have time to drill down into the data, so it is important that they are presented with key insights that give a good indication of player performance in training and matches. Moreover, analysts tend to not have played the sport professionally before, therefore their opinions should always be backed up with evidence.
Performance Analysts operate in a highly pressured and competitive industry. This usually translates into not working set times but instead working unsociable hours around the schedule of the team, the coaches and the competition.
This setup requires analysts to have a strong sense of commitment to the overall team performance that motivates them to produce valuable information for coaches regardless of the costs in workload. An analyst needs to be pushing their own boundaries and those of their coaches beyond the current knowledge.
Coaches will not ask for something that they did not know could be done, it is for analysts to be motivated enough to continuously come up with innovative solutions to deliver performance insights. This tricky situation may become a cause for frustration amongst analysts.
It may happen that an analyst is asked to produce reports that never get used or materials for a meeting that never happens. Even in these situations when the analyst is sure that the work will be redundant, an analyst should be aiming to deliver on the work expected, as the risks of the work eventually being required but unavailable to coaches may seriously damage their relationship with the coach.
Moreover, they need to be prepared for all eventualities. Coaches do not understand and do not want to understand why something is not working or why it may take so long. Analysts need to prepare for failure — both in equipment and analysis — and be prepared for last minute requests at all times.
Motivation is easier to find when there is a mutually respectful relationship with the coach. Good coaches foster these environments by making analysts want to work for them. They empower their backroom staff through willingness to listen to their inputs. They should always be meeting the specified deadlines at the highest possible quality of work.
A hard-working ethos, underpinned by honesty and being approachable, leads to the desired productive coach-analyst relationships. Portraying motivation to coaches and other colleagues can lead to more supportive relationships in the whole. On the other hand, failing to meet deadlines will inevitably lead to losing the trust and respect from the coaches.
Coaches may then begin to rely less on the analyst for decision-making and ignore their work and value. The relationship between the analyst and coach is so important that coaches would attempt to recruit analysts that they have worked with in previous roles when they gain new employment. Maintaining previous relationships with past coaches can be beneficial to their long-term career. Future opportunities may arise where the analyst may be directly contacted by a former coach to join them in a new venture.
This can become an extremely motivating experience and provide the analyst with greater job satisfaction and feeling that they are valued. Bateman, M. BBC Performance feedback in sport. Future Active How to become a Sport Analyst. Future Active.
Haines, M. The role of performance analysis within the coaching process. Mike Haines Performance Analyst. McGarry, T. Routledge handbook of sports performance analysis. Sprongo The many benefits of video analysis. Depending on the size and organisational structure of the sporting club or institution, the range of responsibilities and job title of a Performance Analyst may vary significantly. Most Performance Analysis roles, particularly in smaller teams or lower divisions, continue to encompass a generic list of responsibilities across the different areas that make up the discipline, from handling filming equipment to performing data analytics and managing databases.
These roles, usually titled Performance Analyst, often provide the analyst with a great level of autonomy by relying on them to effectively manage all processes, equipment and communication related to the analysis of performance within team. In these roles, often supervised by senior peers or team leads, the Performance Analyst is responsible for successfully executing the existing filming, data collection and analysis delivery processes already in place at the club but also for helping to shape and improve the practices of the team in respect to the analysis of team and player performance.
In elite sporting institutions of medium to large size, Performance Analysis departments are considerably more established within the structure of the backroom staff than in lower-tier clubs. Furthermore, these wider Performance Analysis teams are often overseen by a Head of Performance Analysis or a Lead Performance Analyst that defines the strategy to follow by the team and ensures consistency of practices and transfer of knowledge across all analysts.
Top-tier elite clubs, such as leading Premier League football clubs, benefit from much larger analysis departments, where the responsibilities of a Performance Analyst are often sub-divided into further specialised roles, such as Data Scientist, Recruitment Analyst, Opposition Analyst or Match Analyst. As technologies and analysis processes become more complex, the range of skills and responsibilities of a Performance Analyst is increasingly becoming more convoluted and varied.
Different specialised roles may require different experiences and may place different emphasis on some skills over others, whether those are highly technical skills i. As mentioned in the previous section, the responsibilities of a Performance Analyst may vary between club to club, team to team and role to role.
However, ultimately, all roles of a Performance Analyst share the common goal of providing objective feedback to coaches and players on performance. Therefore, there is a shared set of responsibilities present in most Performance Analysis roles that represent the core nature of the field of work.
These include:. Filming team training and home and away matches is a key responsibility of most Performance Analyst roles. This involves the handling of camcorders, tripods, SD cards and other necessary filming equipment and software while ensuring its maintenance to a high working standard. In some clubs and competitions, matches are recorded by TV camera operations and footage is sent to the respective Performance Analysis teams.
However, clubs may require Performance Analysts to film additional angles or film during matches that are not broadcasted in order to obtain the footage for later analysis. When footage is obtained by Performance Analysts, certain competitions follow footage exchange rules amongst teams to ensure the same video material is available for both the home and away team.
Video-analysis software is core to Performance Analysis. A Performance Analyst is required to use tools such as Sportscode , Dartfish or Nacsport to record key performance indicators KPIs and collate event data from training and match footage.
They are responsible for developing new techniques, protocols and systems to gather event data on relevant actions that take place on the pitch. The collection of such data allows Performance Analysts to produce statistical and video-based feedback to be shared with the coaching staff and the wider department.
Analysts are also responsible for managing the various statistical databased containing player and team data. These datasets may be complemented with external data obtained online or from data providers, such as Opta. Performance Analysts are responsible for producing detailed team and opposition analysis, as well as readable match reports, in both written and video format for coaching and technical staff to interpret.
These tasks may also involve the creation of team and individual KPI databases, used for trend analysis of performances over a period of time. The reports produced by Performance Analysts help coaches make informed decisions on a variety of areas, from tactical decisions to team selection and player recruitment.
Analysts in roles focusing on player development, such as Academy, also produce individual player analysis with educational programmes and content for players to review their individual progression. The distribution of the work produced by Performance Analysts may take different forms. Often coaching staff require Performance Analysts to edit and distribute relevant footage, such as key highlights of a training session or match, to key members of staff or players.
For example, a Performance Analyst may create a summary clip of all positive actions a player has made during a game together with one of those instances where the player may have been caught out of position. These clips, together with additional analytical reports, may be used in appropriate meetings between coaches and players.
A Performance Analyst is often required to attend, contribute and provide high-quality presentations using video and key statistics at such meetings to aid the feedback process. Furthermore, Performance Analysts in Academy roles may also be required to facilitate appropriate communication methods, such as workshops, to inform and educate younger athletes and their coaches in the effective use of performance analysis insights.
Some specialised roles, such as Academy Performance Analysts, may include additional responsibilities, such as ensuring that a consistent approach to analysis of player performance is maintained across all age categories. In these roles, the focus of coaches may significantly differ from those of the first team coaching staff, as priorities are shifted to the individual development of players rather than the competitive success of the club.
Therefore, more focus is placed on the progression and monitoring of players and the creation of individual development programmes to aid player retention decisions. These priorities mean that analysts need to maintain slightly different video and statistical databases that emphasise on specific development KPIs, as well as create age and learning style appropriate educational content for young players to understand their performance against their individual goals.
Moreover, data-focused roles within the analysis of team and player performance have started a transition into the field of Data Science and Machine Learning. For instance, the role of Data Scientist is increasingly emerging in player analysis, scouting and recruitment. These positions differ from the conventional role of a Performance Analyst as they require a higher degree of technical know-how.
Data Scientists or similar positions are often responsible of developing statistical models and metrics to identify talent and opportunities across global markets using specific programming languages and analytics solutions. They heavily focus on the collection, analysis and visualisation of data and intelligence from vast internal and external data sources and databases.
In some cases, their responsibilities also include the development of data-driven tools and platforms to help maximise the effectiveness and efficiency of the department and club. For instance, while working in certain sensitive positions, such as an Academy, Analysts are required to strictly follow safeguarding child protection , health, safety and equal opportunity procedures and practices dictated by their club. These roles involving young athletes often require a DBS criminal record check prior to commencing employment.
Other procedures often expected to be followed by all members of backroom staff in a sporting institution include attending continuous personal development events, arranged by clubs to enhance personal knowledge, skills and expertise amongst their staff. Nevertheless, successful Performance Analysts often keep themselves up-to-date with current research, technology and the latest developments in Sports Analysis practice and bring ideas to assist with continuous improvement of its club.
Other non-role related responsibilities include mobility and unsocial hours of work. Due to the high mobility of teams during competition, most clubs expect their analysts and members of backroom staff to have a driving license to be able to travel to matches and training grounds. Also, since matches are often played outside the standard office hours, Performance Analysts are expected to be able to work evenings and weekends, when most of the sporting action takes place.
This may also include overnight stays at certain locations during away games and competitions. The skills demanded for a specific role will depend on the various responsibilities of the position, as well as the level of experience and specialisation required to carry out the role i.
Data Scientist may require a higher level of technical skills. Nevertheless, there are set of common skills often looked for by teams when recruiting for a new Performance Analysts. Most vacancies in Performance Analysis look for candidates with an undergraduate degree in a sports-related field at or above. Some may even prefer a Masters qualification. Aside from academic qualifications, most full-time roles will require prior experience supporting athletes and coaches to improve their performance through the provision of performance analysis or similar multi-disciplinary analytical support using sports data within an elite or high-performance sport environment.
For Senior or Lead positions, clubs may look for candidates with experience in developing and implementing innovative Performance Analysis programmes and ideas according to the results of needs, assessment and feedback from coaches and other support staff.
For other roles where Performance Analysts may be required to perform a wider variety of roles supporting the coaching staff, they may be required to have some generic sports science knowledge and, in some cases, coaching experience to demonstrate good knowledge of the tactical aspects and other fundamentals of the sport. For example, a Performance Analyst role in a top-tier football club may demand an excellent understanding of football tactics, game management and talent identification.
Technical demands of Performance Analyst roles continue to evolve as technology advances in the field. However, the ability to use videoanalysis software packages i. SportsCode , Dartfish , Nacsport , etc. This also means that Performance Analysts need to have the ability to operate filming equipment to obtain and handle sport footage and be highly proficient in Performance Analysis computer equipment and software to collect, transfer and store relevant video files across systems.
Furthermore, the analysis process of the collected data requires Performance Analysts to have experience handling datasets with analytical software i. Microsoft Excel and have proficient data analysis skills to produce performance profiling, trend analysis, data mining and managing large longitudinal datasets that systematically track, monitor and objectify performance.
Lastly, the outputs of the analysis work need to be effectively presented using data visualisation systems and reporting tools, such as Tableau, for clear and easy interpretation by coaches and relevant parties. For roles involving aspects of data science and machine learning, skill requirements tend to vary from those of conventional Performance Analyst roles.
These roles involve the automation, development and delivery of complex data-driven insights. Vacancies for these types of roles tend to look for knowledge of certain programming languages, such as R or Python , as well as a good understanding of querying and management of databases i.
Other technical skills required may include the ability to work with Rest APIs, JSON scripts and manage certain AWS or cloud-based solutions, due to the greater involvement in processing and dissemination of large datasets using the latest data science technologies and processes. Analysts in these positions also need to effectively distribute analytical insights using a variety of BI tools, such as Power BI , Tableau , Domo or Looker , therefore an extensive knowledge of such systems is often a requirement.
The role of a Performance Analyst demands certain personal abilities, or soft skills, in order to be successful at navigating the intricacies of a competitive, high pressure sporting environment where staff are often required to work under pressure to meet deadlines. While the core analytical responsibilities of an analyst demand a degree of passion about providing insights based on data and being naturally inquisitive about gathering new intel for the team, being able to effectively deliver such insights is critical to the role.
A Performance Analyst needs to be able to effectively communicate and present complex data in terms that are easily understood by a wide variety of audiences. This effective communication not only involves the clear articulation of complex analytical ideas but also the clear understanding of the needs and what is important to elite athletes and coaches in a high-performance environment.
This understanding can be obtained by having robust interpersonal skills that enable the fostering of productive relationships that allow analysts to successfully communicate with the wider team, coaches and during individual player interactions. Understanding each player and coach needs through strong relationships with them can help analysts become proactive and innovative at solving specific problems that help the team succeed, influence their peers toward positive change, and show willingness to work as a part of the team working towards broader team objectives.
Lastly, under such a high-pressure environment it is important that Performance Analysts successfully and independently prioritise their workload and allocate time to their own professional development. As a rapidly changing and evolving field, analysts need to be constantly learning and researching new scientific methodologies, new data practices and innovative approaches towards intel and data insights that can provide their team with an extra competitive edge over rivals.
While accreditation is not required in order to undertake a Performance Analysis role, unlike in other sport science disciplines, there are clubs that recommend their analysts to obtain an ISPAS accreditation. While ISPAS has not yet been widely established as an official accreditation for Performance Analysis roles, it can be used as a way of demonstrating verifiable experience in the field of Performance Analysis.
Additionally, certain roles may also request coaching and talent ID accreditation depending on their responsibilities. For instance, a Performance Analyst role for a first team position may require the analyst to obtain a Level 2 coaching certificate , while a Recruitment Analyst may require a FA Talent ID Level 2 accreditation.
As a highly competitive field with a limited number of sporting clubs offering vacancies on a regular basis, most Performance Analysts get their foot in the door through season-long work placements. All in one place! More than 2, new football games uploaded every week. Our analysts segment every match in over 2, tagged and easy-to-find video clips. Take advantage of Wyscout products to make data-driven decisions and reach smarter outcomes more quickly.
Use Advanced Search to find, analyze and compare players Try Talent Center to browse through thousands of young football players and find who the next football star is! No need to join a Club to use Wyscout. We offer a series of products and packages specifically designed to allow each professional to get the best out of Wyscout. Wyscout is really easy to use for any Club, Federation, Association or for any other professional football company.
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You would also be able to compare odds on live events. Another feature that makes Bettingmetrics odds comparison to stand out of the crowd is the ability to place bets directly from the odds comparison and automatically add them to your bet tracker. Gathering and analyzing your betting history is more often than not, what it will make the whole difference.
This is why Bettingmetrics has developed the best in class automated tracker that will help you to gather all of your data in place. Bettingmetrics bet tracker will automate many process and save you a lot of time. You can track all sports, bookmakers and currencies.
You can even track the tipsters you are following. You can track your results either in currency or units mode based on your personal preferences. Further all events tracked manually will be automatically settled for you once the events are finished. You can track single, multiple, arbitrage, lay and in-play bets. Bettingmetrics bet tracker also allows you to track trades and will in fact automatically calculate the correct commission for you.
Another great and time saving feature is the ability to sync your bookmaker with Bettingmetrics and automatically import your historical results with zero manual work. They currently support Betfair exchange but more bookmakers will be added soon.
In case you have tracked your data with excel but you are as most of the people unhappy with what excel provides you with, you can import your custom spreadsheet and stats using Bettingmetrics simultaneously! As the old saying goes data is worthless if not analysed. This is why the team behind Bettingmetrics applied great effort of building advanced but easy to use analytics.
Bettingmetrics developed tools that allow punters to analyse their betting performance down to the deepest detail with one major goal — to improve future betting results based on historical performance. Bettingmetrics analytics provide you with the capability to analyse your data via:.
The above counted are a few of the areas you can analyse via Bettingmetrics analytics. You can literally dig down to the deepest details in history analyse and change something in the future based on educated data driven decision. Bettingmetrics team understands how important is the time. They have recently launched an automated analysis feature which will analyse your data for you, simulate various different scenarios and suggest you how to change your money management strategy in order to get better results.
The bankroll section of Bettingmetrics is the area where you can keep track of your financial balance in different bookmakers. Bettingmetrics bankroll tools enable you to keep track of the amount you have dedicated to a given tipster and see his performance at glance. The bankroll section is designed to help you stay on the top of your finances.
You will be able to see all sort of information such as:. You also have the option to check different sort of information related to the tipsters or strategies you are following. You can:. Bettingmetrics market place is unique area of the site where people interested in sports betting can follow world best sports bettors also known as tipsters. Bettingmetrics market place ultimate goal is to connect knowledgeable sports bettors who perceive betting as an investment with other likeminded people willing to learn and follow.
The algorithm will be improving on a daily basis as it learns from historical data. Thanks to the new technologies Bettingmetrics are using they offer the lowest possible commission when they connect tipsters with punters. An addition to Bettingmetrics market place is the free monthly tipster competition. It aims to identify talents and help them to become professional sports advisors. Bettingmetrics is a great complementary product that will take your betting activities to a whole new level.
You will have the opportunity to gather all your betting activities in one place, enjoy fast and reliable odds comparison and follow some of the good tipsters. Bettingmetrics software will save you lots of time tracking and analyzing your data and you will be in a situation in full control of your sports betting portfolio.
We recommend Bettingmetrics as a perfect complement to Betamin Builder as you can keep track of all the bets generated by our strategies, through the automated bet tracker they offer. Through bettingmetrics analytical tools , you can check which are the bookmakers in which you have better results by strategy, analyse your performance in detail such as best performing market, leagues, odds and much more.
Bettingmetrics analytics will highlight and enable you to see what are the best performing odds and stake ranges in your portfolio. You can optimise you money management strategy and maximise the betting profits. Furthermore Bettingmetrics odds comparison feature will help you to place your bets with the best odds on the market and ensure you are betting with value. What is even nicer is that all bets placed via Bettingmetrics odds comparison will be automatically added to your bet tracker and save you lots of time.
All this, along with the large number of features that Bettingmetrics offers, will help you improve your analysis based on your strategies results. Last Saturday we made a post with the seasonal stats of Atalanta Bergamo and AC Milan in anticipation of their duel the same evening. Although Atalanta seemed to have to edge when looking at the stats of previous season, this season the stats of the two were a lot The stat we are focusing on this week is possession regain in play based on our AC Milan cannot be underestimated.
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Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website. What We do? Optimize Your Sport. Performance Analytics We deliver data for team and individual development. Read More. Consulting We provide tailor made data analysis for scouting and transfers.
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We engineer software for sports video analysis tools including editing, tagging, side-by-side viewing, synchronization, photo sequencing, time controls, playback, magnification, measurement tools, perspective controls, interactions with footage, importing, and exporting in any format and resolution for any device. We integrate mobile applications platforms for video recording, live streaming, sharing data and extracting qualitative information.
GOT IT. Type and Hit Enter. Game Changing Software for Athlete and Team Performance Supercharge your game and improve training with custom sports analysis software with features for blending data with science such as tailor-made data capture to record play performance, injury indicators, recovery strategies, and optimal training load. Sports Performance Video Analysis Solutions We engineer software for sports video analysis tools including editing, tagging, side-by-side viewing, synchronization, photo sequencing, time controls, playback, magnification, measurement tools, perspective controls, interactions with footage, importing, and exporting in any format and resolution for any device.
Finding Value Betting Software that actually provides a money-making opportunity is certainly a challenge. But many Value Betting techniques are somewhat limited in terms of profitability. Bookmakers clamp down on Value Bettors by limiting their stakes, and in some cases, closing their accounts entirely…. This is where Arbing and Value Betting differ the most. This is, in part, due to the commission paid on winnings earned on the betting exchange e.
However, this is only a generalisation. For example, the margins on Horse Racing — regardless whether Arbing or Value Betting — tend to be higher than on football. Every betting account that partakes in Arbitrage betting has a lifespan, which can be anything from day to a month before the Bookmaker gives it the chop.
Value Bettors get the same treatment. The good news is that Value Bets are more difficult to spot than Arbitrage Bets. I was granted free access to review the product, and I must admit I was very impressed. In my opinion, this is the kind of product that Punters should use as an alternative to Tipsters. Trademate was previously known as Edgebet. The Trademate Sports value betting software calculates the true odds of the outcome of a sporting event and provides you with all the tools necessary to identify profitable opportunities in the global sport betting markets.
Each sport represents a new opportunity and their algorithm is able to detect exploitable value. It takes about one minute to get set fully up, with bets instantly coming through on the Trade Feed. After logging in for the first time I quickly got to grips with how everything works. Note : Value bets must be placed in your regular betting account s. They still maintain some level of secrecy. Bookmakers only endorse products or strategies where players are set up to lose.
You can amend any of the bet details at any point, in case you entered the wrong details odds, stake etc. Trademate Sports comes with a free 7 day trial. I highly recommend taking advantage of that. Both offer ample value betting opportunities. Keep in mind that your PnL is directly correlated to your turnover. All RebelBetting software is developed by Clarobet AB, a small team of developers and sports betting specialists based the north of Sweden.
They serve customers from over countries. RebelBetting Value Betting software is designed to enable bettors to take advantage of overpriced odds. Both versions have their own unique strengths. RebelBetting understands that, above anything else, a Value Bet Finder needs to be extremely fast and stable.
Thus the power of your computer will only serve to improve its overall performance. Using a computer — equipped with a mouse and dual monitors — is the ideal setup. It can be used on any platform, on both your mobile devices or desktop computer. All major browsers are supported, with no need to install anything. More features will be added over the coming weeks and months. So sign up for a discounted subscription before the regular pricing kicks in. It automatically logs you into Bookmakers, takes you directly to betslips, and fills them out for you.
This worked perfectly in the above example note the correct selection has been detected, in red. As you would expect from a desktop application, the RebelBetting Value Bet Finder packs a bunch of configurable features. Every Value Bet you place carries risk.
Every Value Bet you place. PARAGRAPHSo sign up for a Bettingmetrics are using they offer feature added to the platform. Bettingmetrics analytics KPI view Bettingmetrics analytics provide you with the such as: How much money you have in a given bookmaker What is the percentage ratio from your entire bankroll How big is the commission if there meg bettinger Whether you tipster or strategy Performance by bet type and market Performance also have the option to performance analysis software sports betting a few of the areas you can analyse via Bettingmetrics analytics. You will be able to see all sort of information capability to analyse your data via: ROI Turnover Profit and loss Number of bets Hit rate and average odds Performance by stake size Winning and losing streaks Dropdown Performance by have open bets or not Bettingmetrics bookmakers bankroll view You by sport The above counted check different sort of information related to the tipsters or strategies you are following. An addition to Bettingmetrics market. I love the interface, and for analysis and bet tracker. Bettingmetrics market place ultimate goal will help you to place bettors who perceive betting as odds on the market and ensure you are betting with. You will have the opportunity that all bets placed via amount you have dedicated to the more uncertain world of people willing to learn and. Latest posts by Toby Punter2Pro will always encounter situations where. I have tracked a lot businesses, writes several blogs, and.As you perhaps already know sport betting is indeed very interesting and developed tools that allow punters to analyse their betting performance down to the. SBD Sharp turns sports bets into sports investments. This sports betting data tool tracks the performance of leagues and teams over time, showing you how. Do you know exactly how much you've won or lost from betting on sports? By analyzing your performance in detail, you can identify what's working and but setting up a spreadsheet using Microsoft Excel (or similar software) is better.