For the past three weeks as I’ve been travelling around Australia presenting on The power of data analytics in tennis, I’ve seen articles, stories, blogs and courses on data everywhere. It’s like when you’re thinking about buying a red Toyota and then you see red Toyota’s everywhere – I see data examples everywhere!
I looked it up and apparently this phenomenon is called frequency illusion and it’s how your brain subconsciously filters distractions to provide you with what it considers relevant information.
Anyway, there is no doubt a heap of information out there about data at the top of the game, (generalised data) but there isn’t as much on how to use your own data at a grassroots level to improve your players.
That’s what I’m going to focus on this week.
In the past three blogs, I’ve covered:
WHY you need to use data in your everyday coaching,
WHAT you can use to collect that data, and
HOW to use generalised data to shape your on-court coaching.
This week I’m going to stick with the HOW and look at how you can combine ATP/WTA generalised data with the individual data you’ve collected to improve your players’ outcomes.
Let’s have another look at the ATP and WTA generalised data and some developmental principles that I have created from it.
So, the generalised data gives us a blueprint of what we need to achieve for our players to become successful, while our development strategy and principles help us create the actions.
But tennis is an individual sport and players have different strengths, abilities, personalities, and game styles. So, for each of our individual players to achieve their best, we need to also use their individual data to further shape their development.
Areas to apply individual insights.
I find it useful to break down a player’s individual data, combine it with generalised data and then apply it to these five areas:
- General athlete development
- Off season training blocks
- In season training blocks
- Match Strategy
- Match Analysis
Below is a summary of this idea.
For example, the data shows that WTA players hit an average of 500 balls a match, 70 serves and 50 returns. I can use this to set loadings for my players and use their individual data to see how they compare. From this I can see if they need to serve more in a session or during the week or maybe hit less to prevent injury.
By filming tournament block matches I can compare an individual’s data to the generalised data goals to then guide their lessons and prioritise what to work on in a subsequent training block. It’s a great opportunity to correct or emphasise trends that have occurred in matches and to prepare better for upcoming ones.
I can also use the match data to develop specific drills to practice and achieve desired behaviours in future matches. For example
- to encourage a player to take control of the point on a second serve.
- to drill a player on specific patterns that make the most of their strengths.
- we might see our player playing down the line too early in the point and exposing their court, therefore we could change their pattern to 1 cross court and 1 down the line, using the cross court to create space.
I can use the data from charted matches to give accurate feedback and track improvement against the generalised data. I can use match video to provide visual evidence to back up my opinions.
By analysing collected data, combining it with generalised data, and then applying insights, data can add a lot of value to a player’s development. But it’s always important to keep in mind the individual.
Adjustments need to be made for:
- Game styles, a counter puncher vs an aggressive baseliner.
- Gender, male metrics vs female
- Physical and mental capabilities: the professional data helps sets some goals, but each player has a different path to those goals which is very much dependant on their physical and mental capabilities.
- Personality and learning styles.
Actioning data insights
Now that you have collected the data, analysed and combined it, and gained some valuable insights, you need to implement those insights.
How and when you do this is important if you want good outcomes, because different changes typically take a different length of time to implement - some changes take time, some are quick wins.
Below is an example of some actions you might want to implement, the area of development they fall under and how latent they are. Actions with a high latency typically take longer to implement than actions with a low latency.
I find it useful to plot an individual player’s actions on a chart like this so that I can make sure the actions I am prioritising work together. I usually want no more than 2 -3 actions in each focus area, eg technique, tactical, physical and mental.
How latent those actions are will depend on where we are in the player’s schedule. If the player is about to start a few weeks training block, I might include more actions that require a longer latency. But if they are in the middle of a tournament block, then low latency actions are more appropriate.
The graphic below is a visual summary of how to use data in tennis coaching and pulls together what I’ve covered in this blog series.
Although the information I’ve presented in the past four weeks is a brief look at how data can be used in tennis coaching, I hope you’ve gained enough knowledge to create a strong foundation in your own practice.
At the very least, I hope you have been inspired and challenged to start using data in your everyday coaching.
I’d love to hear your comments.
By Marc Sophoulis