INSIGHT ANALYSIS PROVIDES SPORTS PERFORMANCE ANALYSIS AND CONSULTANCY to help professional sports organisations maximise efficiencies, achieve KPIs and deliver insights to improve individual and team performance.
Since its creation in 2003 by Ex-Bath and England player and coach John Hall, INSIGHT has used its knowledge and experience with data in sports to work with an impressive range of clients including England Rugby, Bath Rugby, the Football Association (FA), British Athletics, British Sailing, GB Snowsport, the Lawn Tennis Association (LTA) and Team Sky.
Owen Farrell, England, British Lions and Saracens: ‘When in camp for England I have always found the analysis provision provided by INSIGHT to be excellent. They leave no stone un-turned in trying to provide players with whatever video or data they require in order to maximise off-field learning’.
METHODS
Our methods are based on a chain of data collection, data management and data analysis, although our relationship and communication with our clients remains key throughout, be they performance analysts, backroom staff, coaches or the athletes themselves.
Data collection is completed using methods necessary to achieve the level of desired detail for relevant insights to be extracted. Occasionally, the clients provide the data in an accessible format, though more often than not a certain amount of creative endeavour is required. With many of the sports we cover, there are elements of manual data collection or ‘coding’ of videos of the sports events in question. This might be to focus on certain key instances in competitions which are not easily extracted from the data using other methods. In rugby, we code entire matches from the domestic and international calendar in order to build the most detailed and useful dataset of the English game available. This ensures we have a robust, trusted dataset that delivers much richer insights than the common data feeds.
Data management is an important part of the process, since data inaccuracy leads to errors in the analyses. We manually check data in specific and general cases for our clients, which require the highest level of accuracy. When speed of delivery is a higher priority for the client, then the data is cleaned for general cases as well as the most obvious specific cases of inaccuracy. Data in sports can often be messy, and the politics of sports organisations often provides a further challenge to working with the best possible data available to aid the athletes’ performances.
The methods of data analysis we adopt are driven by the challenges of the problem and desires of the client in question. On one extreme, we have sports specialist performance analysts embedded in teams and organisations that are able to use the data and knowledge of the sport to provide meaningful insights in preparation for and during sports events. On the other end of the scale, we perform data driven tasks using methods in Operational Research and Artificial Intelligence when necessary. Naturally, there are many tasks which lie within such a spectrum. Across our services, we aim to utilise our knowledge of sports and data to achieve the best possible outcomes for our clients and athletes.
I have always found the analysis provision provided by INSIGHT to be excellent. They leave no stone un-turned in trying to provide players with whatever video or data they require in order to maximise off-field learning
As communication is so central to our success it can take many forms. Naturally, we maintain consistent and clear communication throughout, using all the ‘usual’ channels over our entire data process. However, in order to keep the clients engaged we are constantly trying new methods of delivery. Verbal channels are complimented by creating multimedia reports and annotating video in a way so that a story can be told with the data and analysis. Increasingly, we move to methods of web-based delivery so that such stories are far more interactive and engaging for ourselves and the clients.
Dan Hunt, Performance Director – GB Snowsport: ‘I can honestly say that their manipulation of data and dissemination of information is second to none in sport in this country’.
CASE STUDY: PREDICTING PLAYER RANKING PROGRESSION
In sports, it is of interest to be able to map out the potential career journey of a particular team or athlete and then make decisions as to how best to support them over their career. Many sports administrations and fans are interested to know who the best teams and players are and so ranking tables are created. By utilising information in these ranking tables over time, it is possible to look at the progression of given teams and athletes of interest.
Typically, a new athlete or team will start low in the ranking and then as they start to succeed in their sport their ranking will improve. This is followed by a period of maturity when the team or athlete stays near the same rank, then entering a period of decline where their rank gets worse and/or they stop competing. Of course, there are exceptional cases of athletes and teams which do not strictly follow this pattern, but looking at the data as a whole will often display this overall trend. An example of an athlete’s career progression is visually presented in , with date as the x-axis and rank as the y-axis. By observing , it is clear to see that for this particular athlete their career can be broken up into growth (before 2002), maturity (2002–2006) and decline (2006 onwards).
FIGURE 1 EXAMPLE OF AN ATHLETE’S RANKING OVER THEIR CAREER
Within the career pattern of growth, maturity and decline, it is interesting to understand the progress for each athlete or team within this overall trend. For example, we can explore questions relating to athlete rank and rates of achievement. Such answers will be different for each athlete and team, and so using the data, it may be of interest to model the probability of an athlete or team reaching given threshold ranks over time, such as breaking into the Top 100, Top 50 and Top 20.
By using data from similar teams and athletes at comparable points in their own progressions, it is possible to make a prediction of how likely they are to break into the Top Ranks over given timelines. The results of such calculations can be displayed graphically with future time as the x-axis and the cumulative probability of breaking the given threshold ranks displayed on the y-axis. An example of this with some data is shown in .
FIGURE 2 EXAMPLE OF AN ATHLETE’S PREDICTED PROBABILITY OF BREAKING INTO THE TOP 100, TOP 50 AND TOP 20 RANKS OVER THE NEXT 4 YEARS
shows that in this particular example, the athlete is more than likely to break into the Top 100 within 3–4 years. The athlete is also nearly 40% likely to break into the Top 50 in this time. However, their probability of breaking into the Top 20 is only around 10%. This is another indicative pattern found in sports, in that with each ranking success, it becomes increasingly difficult for an up and coming athlete to break into the higher ranks of that sport.
In addition to graphical analysis, further insight can be displayed and delivered to our clients through a bespoke website with full interactivity, allowing them to consider general cases as well as specific cases to help them understand the nature of ranking progression as well as monitoring specific athletes/teams of interest. Web delivery has advantages over other methods because the nature of interaction allows each user to have a personalized non-linear journey in which they can view and extract the most relevant insights. It is easy for us to customise the web platform to allow for extra data or requests by the users with the possibility of offering a service to be the ‘hub’ for their data management and analysis requirements.
Chris White, Performance Intelligence and Analysis Lead, Lawn Tennis Association (LTA): ‘INSIGHT provides bespoke delivery, a flexible approach and constant innovation as data and technology grows. Moreover, their analysts know how to integrate and deliver impact to their clients. INSIGHT is the benchmark for Performance Analysis across all team sports’.
SPORT INSPIRING INNOVATIVE RESEARCH
When completing the ranking progression work, it was necessary to explore many possible methods to make such ranking progression predictions. One of the challenges was the natural hierarchical and nested structure to the data. For example, if an athlete has broken into the Top 50, they have also broken into the Top 100. Some of the more traditional methods available, although mathematically sound, failed to appreciate this fact. Occasionally, the model produced predictions for a Top 50 break being higher than a Top 100 break at certain time points, which makes little sense since the Top 50 ranks are contained within the Top 100.
As the baseline method provided such favourable results, we are currently exploring a method to develop the driving mathematics to appreciate and allow for the hierarchical nested nature of the events. This works by exploiting known results in probability theory and modifying their applications to deconstruct the probability calculation into constituent parts which themselves can be calculated and implicitly respect the nested nature of the event space. Although the calculated predictions will be less biased, applying this ‘deconstruction’ method introduces extra levels of uncertainty which have to be understood and accounted for. We are currently working on a submission for an academic journal using these results and developing the methodology further.
With our extensive work in Rugby and with the England team we worked flat out in support of the team this year with the final preparations for the 2019 Rugby World Cup in Japan in the Autumn.
LOOKING TO THE FUTURE
INSIGHT continues to work to develop its methods to help support the successes of British athletes. We continue to work with exciting new clients and new sports, developing our methods to help provide them with the best possible results.
we worked flat out in support of the team this year with the final preparations for the 2019 Rugby World Cup in Japan in the Autumn
Many of our other British clients work on a four-year Olympic cycle, which will culminate in the Summer Olympic Games in Tokyo 2020, and the Winter Olympic Games in Beijing 2022. Just like the athletes, our work in support of the athletes and teams has these major goals and timelines in mind. So, as you watch British athletes compete in competitions in the future, remember they may well have been assisted by implementing operational research and the work we do at INSIGHT.
Additional information
Notes on contributors
Nick Lade
Nick Lade has spent the last year as a data analyst within INSIGHT, driving a data orientated approach to sports analysis and insights. He has recently moved to a data science role within the parent company MyLife Digital with a central focus of looking to productise our expertise in sports and other industries to help improve the outcomes for our clients.