By Julie Street, University of Indianapolis Women’s Volleyball Assistant Coach & Recruiting Coordinator

Data analytics is rapidly growing in the sport of volleyball. This is the use of raw data to notice patterns, make predictions, and make changes based on the results to gain a competitive advantage. Conducting data analytics does not have to be complicated or expensive, but the benefits of finding trends and changing behaviors based on those trends can be greatly beneficial. At the University of Indianapolis, we use data analytics in all parts of the sport such as determining play time between our athletes, what defense to use against an opponent, and how to allocate practice time effectively. This article will go over the basic steps of creating analytics strategy by discussing how to identify a purpose behind the analysis, select which variables to measure and how to do so, and interpret the results to make actionable change.

1. Purpose – It is important to consider why you are collecting the data. To collect data to just have data without intending to do analysis is a waste of time. To find the purpose of collecting the data, think about what questions you are trying to answer. These questions should have actionability and the results should be used to improve your team’s chance of success. Some questions could be: Where do attackers hit most often? What passer location yields the highest passing efficiency? Who is the most efficient defender on my team? By asking what questions you would need to answer, you can identify the purpose of the data collection and drive what data needs to be collected. It is very important to consider the purpose before the start of data collection to ensure its actionability rather than collecting data and having to sort out what you can do with it after the data is collected.
2. Variables to Measure – The next step is thinking about what variables you need to measure to answer your question. For example, if you wanted to look at serve and serve receive, you would need to measure the variables of serve and serve receive, such as server location, passer location, serve speed, and passer’s platform location. The most important variable that you must measure is your target variable, which is the variable used to measure success. It is the variable that all other variables are compared to for changes in efficiency. For sports analytics, the target variable is typically winning or losing. The target variable could also be some other measure of efficiency that impacts winning such as passing efficiency or attacking efficiency. The target variable is the most important variable to measure because it is the outcome that you are trying to change as you are trying to increase your team’s chances to achieve success. It is important to identify all the variables that you should measure for your analysis and to not forget the target variable.
3. Additional Variables – Now that you have identified the variables that you need to collect, are there any additional variables that could be beneficial to have and do not take much additional time to add to the data collection? For example, should you add player number when looking at attacking comparison? Maybe the question that you are trying to answer is how is your team’s attack percentage down the line compared to those in your conference? To answer this question, you do not need to have the player number as a variable, but if your next question is, who is the best line attacker on my team, you will need to collect the data at the player level. If you decide to add player number during the initial data collection, it will take much less time than going back to re-collect the data after the fact. Other variables that you might want to consider recording is the opponent, match date, location of the match, etc. Considering other variables that you may not anticipate needing but would be easier to collect during the initial data collection process will help save time in the long run.
4. Quantify the Variables – Every variable must be measured in some way. How you measure that variable and quantify it is up to you. It is important to be very specific in how you quantify the variable so you can have standardization between data collectors. It is imperative to write the definitions of each variable and how to define it in a separate document to help all parties involved (coaches, players, and scouters) understand the data. It is important for those collecting the data to know how to collect the data and why it matters. Variables can be quantified in a variety of ways; how you quantify a variable will depend on the intended analysis and time constraints. When quantifying passer location, you could divide the court into six zones (most common). You have other ways to quantify the location of the passer such as short/deep zones, left/middle/right zones, nine zones, etc. The depth of quantification of your variables is up to you. You can always combine smaller categories into bigger categories but cannot go the other direction. For example, if you chose to quantify passer location by short and deep zones, you cannot look at the pass efficiency out of zone 5 (left back), but if you measure by the six zones, you can combine 1/5/6 for the deep zones and 2/3/4 for the short zones. The more detailed you quantify the variable, the more ways you can analyze it in the future, but the more options you have in each variable, the higher the possibility for inputting an incorrect value. More options in a variable also take more time to train others how to collect the variable. It is important to spend some time thinking about how you would quantify the variables you would like to measure.
5. Data Collection Process – After you have identified the purpose and how to collect your variables, the next thing you should consider is the data collection procedures. You should consider what supplies you currently have access to. You need to select a software that allows you to easily store and view the data. Microsoft Excel is a simple way to store data and is a software in which many are proficient and have access. Your Microsoft Excel spreadsheet should be set up with your variables as your column headings and your data will be collected into the rows of the table. Video access software such as VolleyMetrics can be used to rewatch the matches to collect additional variables. If the data collection is not occurring live, other materials of a computer or tablet will be needed to view the matches after the fact. If the data collection occurs live, a device will still be needed to record and store the data. The data collection procedures are important to consider.
6. Data Analysis/Results – To run the analysis, you will want to consider resources at your disposal. I have worked with our university’s data analytics faculty to help run our data analysis as well as view the data from a non-volleyball perspective. As a beginner without many resources, you can run simple results by comparing results of certain parameters to the average. For example, you might calculate the pass rating for each member of your team and then are able to select the player with the highest passing efficiency to be the libero. Something else to consider is how to effectively present your results. You need to think about if you want to share the results with your team and how you would do so. Some learners prefer tables and other learners need the visualization of an image. It is important to consider how much of the data you want to share with the team and how you want to present it to them. Sharing data analytics with your team should be well thought out. If you have not introduced your team to analytics, you might want to think about which players would be interested in analytics and start with them. As a beginner, it is important to start small with data collection and analysis and then as you gain experience, you can get more sophisticated with the analysis you run and how you present the data.
7. Data Analytics Application – Data collection is important but is only useful if it is actionable. Looking at the results, what are some changes you can make to impact your chance of success? If you found that your attackers are less efficient attacking down the line, maybe you choose to spend more time working on-line attacks. If you found that a different defense would allow you to dig more attacks, maybe you change the players’ defensive position. If your results showed that cross court serves result in lower passing efficiency, maybe you choose to spend more time passing cross court serves or maybe you choose to serve your opponent cross court more often. Without data collection and analysis, you might not have noticed the patterns and trends that were occurring. The actionable piece is the most important part of sports analytics, which is the use of data analysis during sports to yield a competitive advantage.
8. Future Data Analysis – Looking at your results, what are new questions that you have? If the results surprised you, you could replicate the study to see if human error contributed to the change. If there is human error in your current analysis, maybe you need to spend more time training your data collectors or perhaps your measured variables have too many options. Your results and application might provoke you to ask more in-depth questions. If you found that your team had the highest hitting percentage in the conference, maybe you want to know what zone your attackers hit to the most or maybe you want to look at what zone they are most efficient at hitting. It is important to consider the new questions that arise following the interpretation of results and application of past data collection.

Data analytics is beneficial when being applied to the sport of volleyball. It allows for a competitive advantage and an unbiased opinion when looking at the factual results. To create an analytics strategy, there are a few steps to consider such as the purpose of the data collection, how to collect and store the data and how to apply the results. At the University of Indianapolis, we use data analytics in all aspects of the game; we find data analytics to be a useful tool when looking at the efficiency of our team, how to combat an opponent, and even to learn more about volleyball itself. Using data analytics does not have to be complicated or expensive, will allow you to see the sport of volleyball in a different light through numbers and patterns, and will help to impact your team’s chance of success.