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This step by step guide outlines how easy it is to link the data you are collecting back to team performance and injury history. In particular, the analysis of performance data of other winning teams can be key to understanding and shaping the pathway to progress your own team’s performance. By aggregating and connecting this data, expert knowledge is supported with objective insights, optimising day-to-day decision making and long term planning to help secure the best outcomes for both individual athletes and the team.


Both day-to-day decision making and strategic long-term planning are crucial components of athlete management. But with so many variables to consider, how to start can often be the most challenging step.

Sir Dave Brailsford, General Manager at Team Sky Cycling, suggests that we should always start by “analysing the demands of the event.” In this post we discuss how analysis of the present situation and understanding the demands of the event – i.e what it will take to win – are essential in order to plan for optimal performance.


Firstly, how do we determine what it takes to win? Typically, there are a number of common approaches to this question. For example, tactical experts (such as a coach) are often relied upon to communicate the demands of events to other practitioners on the performance team.

“We need to dominate the set-piece.”

“Winning relies on the start of the race.”

In addition, tactical experts are also valuable in identifying specific facets of performance that may presently be unsatisfactory.

“He’s not strong enough.”

“She doesn’t start well.”

Furthermore, objective data is often used to supplement expert opinion. With objective markers of performance, we are able to quantify a team or individual athlete’s current situation and compare this to other teams or athletes, often termed a “gap” analysis.



This high level “gap” analysis is the starting point in any sport from a multi-stage (e.g. Tour de France or Formula 1), to multi-game team sports season (e.g. football, baseball, ice hockey etc.)  or a discrete event (e.g. 100m sprint or 50m freestyle).


In the plot above we see both the average win rate by competition ranking for the last five seasons of Major League Baseball. Teams who reached the MLB playoffs had an average of 86 wins with the average difference in between each rank equal to 1.2 wins.

Examples from other sports are:

Formula 1

381 points to win

14 points between each position


87 points required to win the English Premier League (EPL)

3 points between each position

Men’s 100m

9.57 to beat Usain Bolt

0.03 seconds between each position in 100m final


Determining the gap between a team or an individual’s current performance and their target, is the first step in analysing “what it takes to win.” The next step is to break up the event, season or race into its component parts.

The table above looks at averages, but let’s take a specific example from the 2016-17 English Premier League and look at the highest ranked relegated team (18th). The difference between their 34 points and safety (17th position) was six points. As such they would have required another seven points in order to avoid relegation.

Knowing the very obvious fact that the team who scores the most goals in each game wins, we can look back over the last five seasons at goal difference in relation to points. It appears that for any team to gain seven more points in the league they would require an increase in goal difference of +10 (goals scored minus goals conceded) across the course of the season.

In order to close this type of gap not only in EPL but in a similar situation across any sports –  we would need to break each performance down into its own component parts.

How are goals, tries, runs, baskets scored? How is a race or routine  won or lost?



In the plot below is an objective, impact assessment of five million rugby union in-game actions across twenty leagues and over five seasons, with the plot detailing what actions correlates to a team’s chance of scoring. On average teams who score more points than the opposition are more effective in specific elements of performance, such as kicking.

By comparing the expected tries (or goals, baskets, points, etc) to the actual goal across the course of one season, we not only validate this method of analysis but also objectively determine the required improvement for specific elements of performance in order to score more. For example, if my team has an average kicking effectiveness of 74% and the top team has a kicking effectiveness of 82%, I know by closing this gap my team will have a more positive outcome on scoring points but now I also know exactly how much is needed to have that impact.

This type of assessment can help you understand which elements need to be maintained and which elements need development to close the performance gap. Using the previous EPL example, the goal target to move from 18th place to 17th place would be ten points.

In addition, this type of insight enables you to drill down and see each individual athlete’s effectiveness in all of the elements of performance, often termed “individualised objective performance indicators.”




It is important to remember that making both good day-to-day decisions and creating a long term plan requires more than just an understanding of your team’s gap. As practitioners we also need to determine the specific underlying factors which are limiting each element of performance. 

Event/ Season > Race/ Game > Performance Element > Underpinning Factor

By correlating the objective performance indicators to individual technical, tactical, physical and psychological abilities, we can determine which traits are linked to performance, and also those which appear to have little or no relationship.


The above plot is a good example of how in rugby union a physical skill such as squat power has a tight correlation to in-game carry effectiveness. Another example below for soccer tells a similar story in tabular form, showing which metrics could be manipulated in order to improve performance.




Benchmarking is a simple way of assessing an athlete’s current ability for any component of performance with the end goal to develop an individualised performance plan. By assessing an athlete’s technical, tactical, physical or psychological strengths and weaknesses, we can easily build an individual profile of not only in-competition ability, but also map out the underlying factors which will improve overall performance.

This “individual needs” analysis should direct our long-term training plan for that athlete in order to maintain their strengths and improve any weaknesses.

Over time we can also review the success of our interventions on the factors which underpin performance in order to continue interventions which have a positive effect and avoid those which don’t. Using the above example from rugby union, where squat power appears to be highly correlated with in competition carry effectiveness, Gannon et al. (2016) demonstrated the positive impact of lower limb resistance training on athletes’ squat power production across the course of one season. As such we would also expect this intervention to be positively related to in competition carry effectiveness.




We can also link transient variables to performance such as an athlete’s current level of fatigue or an unexpected change in mobility. This “monitoring” should direct our short-term training plan for the athlete in order to reduce any risk of poor performance.

In basketball for example, subjective fatigue appears to correlate with competition performance (above). Again, only by understanding the underpinning factors which affect fatigue are we actually able to intervene effectively. Lachlan Penfold, whilst in the role of Performance Director at Golden State Warriors, suggested that “a tremendous amount of fatigue – mental and physical – develops over the course of a long NBA season and (different) players can be more or less resistant to its effects.”

As such we would expect there to be a negative impact on the performance of players who are not prepared for the stresses imposed during the course of a season. An objective understanding of these stresses allows us to develop off-season and pre-season plans which will give athletes the best chance of being resilient to the onset of fatigue both across the course of the season and within competition.


Obviously, the athlete or team who are the most effective in competition are also the most likely to win. Whilst enhancing the factors that underpin competition performance is one way of improving the chance of winning, a more immediate means is to ensure the best performing athletes are also the most available for competition. As such, “we’ll be OK if we stay healthy” is a common narrative from coaches and GMs alike at the start of each season.

So what can we do in order to give athletes the best chance of “staying healthy?”


In the same way we analyse data to provide the underpinning stable and transient effectors of performance, we can also do the same with injury risk.


The example above (American football) demonstrates the tight correlation between injury risk and the ratio of the number of decelerations in the last seven days and the last 28 days*.

*A ratio of two means the athlete decelerated twice as many times on average over the last seven days compared to the the previous 28 days.

And again, we can also look more easily at multiple metrics in tabular format (left, soccer). To set our thresholds based on these metrics, instead of benchmarking our athletes as we did when looking at performance, we need to decide on how much risk we are willing to take.


Kitman Labs provides bespoke, team-specific reports which analyse all metrics in relation to performance and injury, including entry from the athletes themselves or a third-party source such as GPS. These allow staff to instantly see which metrics are important and where thresholds should be placed in order to maximise performance and reduce injury risk.

Decision makers are able to use objective fact as opposed to subjective opinion in order to intervene in athletes’ programs on a day-to-day basis, and build long term plans. The insight collected from the data aggregation and analysis can also be used to communicate more meaningfully with tactical experts or key decision makers, such as coaches.

For the first time ever…

  • Strength coaches are able to demonstrate which training methods have the largest impact on performance,

  • Medical staff are able to have clarity over when an athlete is ready to take the next step in rehabilitation or return to play,

  • Sports scientists are able to go to a decision maker and say, “Coach, 4 out of 5 times a player has been in this situation in the past they have got injured.”

The action is still in the decision maker’s hands, but now this can be made of objective fact as opposed to subjective opinion

What if a team doesn’t have any data? Kitman Labs utilises its combined data set across sports which comprises thousands of performances and injuries in order to better understand risk. As such, we are even able to set thresholds for teams who have no historic data!


Brailsford, D (2015) Investors in People – Sir Dave Brailsford, Outperformance Roadshow. YouTube

Major League Baseball (2017) The Official Site.

International Association of Athletics Federation (2017)

Formula 1 (2017)

English Premier League (2017)

OptaSports (2017) Rugby Union Performance Stats.



  • Athletes
  • Data
  • Injury
  • Sport
  • Sports Science
  • Technology

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