The Best Way to Visualise Testing Data

Contents of Research Review

1. Introduction
2. Athletic Performance Indexes
3. Identifying Key Performance Indicators
4. Weighing Tests
5. An Example
6. A Practical Solution to the Data Blues
7. Conclusion
8. About the Author
9. References
10. Comments

Introduction

As Strength & Conditioning Coaches, we know that testing weeks and days can be an enjoyable part of the role. We get to watch the progression of our athletes’ physical journey. However, the troublesome part is afterwards. It can be overwhelming when looking at an open excel document with a bunch of data, figuring out a way to represent these numbers in a manner that speaks sense to sports coaches.

In today’s world of sports performance, data is essential. Without data, you cannot gain insight into the physical journey athletes are on and if they are improving, getting worse or staying the same. Gathering the data already presents a plethora of challenges, but presenting that data once you have it, and using it effectively is the real challenge.

Therefore, this article aims to serve as a resource for coaches who may not have the resources or opportunity to work with data specialists or high-priced data management systems. This is not an article that will teach you how to run high-level analytics and correlations with your data, it will not describe how to design a perfect excel database, but it will provide you with practical information on how to get your athletes and coaches to understand and VALUE the data you’ve worked so hard to collect.

Athletic Performance Indexes

Athletic Performance Indexes (API’s) are intended to be a comprehensive collection of data that gives a holistic representation of an athlete’s physical (and sometimes mental) capabilities. Depending on the API, these data sets may be sport-specific, or they may be generalized and geared toward more foundational athletic qualities. The scoring is usually on a scale from 0 – 100, 100 being the best score possible.

Identifying Key Performance Indicators

An API should be built by Key Performance Indicators (KPI’s) specific to the sport or position being analysed (Turner, 2014). In the sport performance world, some common KPIs are speed, strength, balance, mobility, stability, power, etc. KPIs may even be more specific, such as shoulder stability, ankle mobility, horizontal power or single-leg strength. The KPIs used in an API should be determined through the completion of a comprehensive Needs Analysis of the demands of the sport, along with coaching experience. If you haven’t conducted a Needs Analysis before, read this “Needs Analysis” article.

If Lacrosse is used as an example, in general, the physical attributes involved in the game of lacrosse are speed, horizontal power (acceleration), anaerobic and aerobic capacity, strength (upper and lower body), and change of direction (Akiyami et al., 2019; Polley et al., 2015). Whichever KPIs you choose should be determined by an evidence-based needs analysis, your personal experience working in the sport, expert opinion in the field, and the coaches you’re working with.

The importance of involving head coaches when establishing KPIs is threefold 1) It involves them in the process which makes the final product easier to understand. 2) it gives them a sense of ownership in the testing process, which in my experience, leads to higher precedence set on data and testing weeks. 3) Coaches may use tactics which mean certain KPI’s are more important (e.g. if high running and pressing are involved) then aerobic and anaerobic capacity may be a major part of your API.

In certain sports, some qualities are more important than others. Upper body strength is much more important in American football as opposed to soccer. Which means when we design an API, we need to weigh some KPIs differently than others. 

Figure 1: API weightings in lacrosse.

This API is designed for a general lacrosse athlete. This weighting structure can be developed through a need’s analysis of the physical demands of the sport, speaking to other professionals who work with the sport (including sports coaches), in addition to considering your own experience with the sport.

KPI’s may be organized differently based on specific positions within the same sport. For example, in American Football, an Offensive Lineman’s API and a Defensive Back’s API are going to look far different. An Offensive Lineman’s API may weigh Upper Body strength as the most influential KPI, whereas in the Defensive Back’s API, the most influential KPI could be speed or change of direction (Williford et al., 1994).

Weighing Tests

Once a coach has weighed the KPI’s in order of importance for their assigned sport and developed a valid and reliable testing battery, it is time to weigh each test. To weigh a test, one must establish parameters; a best, a worst, and an average score. Establishing these values will allow whichever system you use to recognize and establish a score to the athlete’s raw data. A coach may use normative data, if any is available on the test, or they may use their data gathered throughout the years to set these parameters for each test. Each test will likely have different normative values based on skill level, sport, sex, position etc (Haff & Triplett, 2016; Tanner & Gore, 2013). For instance, the average back squat for a high school American Football player is different from the average back squat for collegiate American Football players (Williford et al., 1994). To go even further, the average back squat for high school Running Backs is different than the average back squat for high school Linebackers. These variables must be considered when establishing your parameters for each test. 

Once the parameters are established for each test, your athlete’s raw data can be converted into a score representing how well your athlete has performed compared to the rest of your data set, or a global data set, for that specific test. The score on each test will subsequently lead to a score on the KPI being tested, which will subsequently lead into the overall API score. 

An Example 

Below is an example of a Lacrosse athletes’ KPI’s and the tests used for these KPI’s. An API document like this can be provided to each of your athletes. Each KPI is weighed and each test still provides a raw score.

It’s also important to know your audience, a sports coach may find it hard to digest all this information for each player. As such, something more generalized and easier to digest can be provided to coaches such as a ranking of players for each KPI, with an example below.

Figure 2: An example athlete API report card

Figure 3: Overall API ranking

Figure 4: Speed ranking

A Practical Solution to the Data Blues

Most coaches don’t see the importance of fancy metrics or how much better an athlete’s squat has increased. It also isn’t their job to know if going from a 4.83-second 40-yard dash to a 4.77 second is a significant change (Clark et al., 2019; Moir et al., 2004). What coaches DO understand and want to know is if athletes are improving and if it’s going to make a difference on the field with KPI’s that have an impact on the sport (e.g. speed, agility & strength) (Akiyami et al., 2019; Williford et al., 1994) and how players are ranking against others using scoring systems such as a 0 to 100 ranking. 

For example, reporting a player went from a 50.7 to a 53.46 in their API, or the team as a whole improved from a 60.23 to a 61.45 will help coaches easily grasp the importance of the results you are presenting, especially if the sports coaches were involved in the process of developing the API.

Conclusion 

APIs can change the way coaches interpret testing data and streamline the entire process from data analysis to presenting them to athletes and coaches in a way that will engage athletes and help coaches understand.

If you have any questions, please drop them in the comments or contact Parker via email me here.

About the Author

References

How do you visualise testing data? Let us know below…