“Great statistics are the best substitute for judgment.” This is a sticker Al Scates has affixed to the side of his briefcase.

Former Davidson College Head Volleyball Coach, Tim Cowie, is now a distinguished sports photographer. He was our catwalk at the Winthrop Coliseum and snapped this photo of my clipboard. He must have been curious the statistics I was keeping during the match. You can find that exact offensive statistic chart in the presentation/PDF below.

Last season, while out recruiting, I was standing court-side with Shelton Collier and we talked a bit about statistics. He brought to my attention the purpose of bench statistics to help win the next point or next set, versus statistics kept as a summary of what happened in a match. Quite a valid point! I’ve since altered my use of statistics during a match to help my team win, but I must admit, I do take some statistics during a match that provide an overview of a set.

Great coaches are proactive; those that provide information to a player prior to something happening. Most coaches are reactive; informing players after something happens (duh!). It is more challenging to be proactive, but also more rewarding. I continue to learn how to develop my skills as a proactive coach.

Following is from my recent USAV Palmetto Region Coaching Clinic in Charleston, SC, January 2013. All of these charts I have used in one capacity or another throughout my coaching career and some I still use today.

Here is a PDF of the presentation: Proactive & Reactive Statistics

I hope they help you in some way!

Part of the purpose of this website is to provide unbiased information on volleyball. I’ve had an opportunity to use a number of different statistical programs and each has its pros and cons. There is no perfect statistical system out there, but I don’t think there will ever be a perfect statistical software because each coach has different wants and needs. Just as different players fit a coach’s style better, so does a statistical system.

Rotate 123, which we use at Winthrop for rotation and match-ups, has developed a statistical software, SoloStats 123. I have had a quick overview of the product from co-founder Kyle Mashima, and believe it has great functionality for a single coach to take stats, thus “Solo Stats”. The software will be able to tie in to video just like iVolleyStats. I believe a coach is able to take statistics during a match using this software or iVolleyStats or other software like StatEast or even Data Volley, but what is the rate of return during the match? Are the stats you are taking helping you to win that game or are the statistics you are taking being used for post match or tournament analysis? As a coach, you have to decide the “rate of return” on coaching the match vs. taking stats.

Following is from a comparison of iVolleyStats and SoloStats 123 from NC Volleyball Academy, Coach Jim Crossett. Jim is also an executive at IBM and graduate of Harvard Business School Jim provided this information to the Operations Manager at NC Elite and has allowed me to post this information:

“I haven’t used iVolleyStats myself but have looked at it, seen other coaches use it, and gotten some feedback from people I trust.

I think your choice depends on what you as a coach is trying to accomplish. I apologize for the long note below.

Pros for programs like iVolleyStats: Can capture in-rally stats, including non-point producing items like attack effciency as attack attempts are recorded, successful digs are also recorded, and often passing accuracy (i.e. 0-4 scale) can be collected by these in-rally tools.

Cons for programs like iVolleyStats: Using programs like this is difficult, if not impossible, for the head (active) coach to use while attempting to coach the game. Ideally you need an assistant coach or coaches, or possibly tech savvy parents to use the program to correctly collect the data. The lower end, affordable programs like iVolleyStats do not capture data by rotation, which often is very important to a coach for practice planning and can be heavily leveraged during a tournament day.

Pros for using SoloStats123: With practice, a head coach can record every point earned or lost by player and the skill used when winning or losing the point, while coaching the game. I find that helpful as I don’t have an assistant coach with me at tournaments and I don’t have any parents this season who really want to collect stats for me. So this is a life saver. As this program only records what caused the point to be earned or lost, I just touch 3 buttons (name, action, and enter) to record the rally result.

So every rally recorded in SoloStats123 has a record of the last touch (earned point or error) by your team and your opponent. The detail programmed in that collection through use of serving order, and therefore lineup rotations, means you receive a large amount of useful data real time and for analysis later via its web reports.

For example, Suzy on your team serves an ace. In this program you touch (I punch) the button labeled “Suzy”, touch the green earned button labeled “Ace” and then touch the button “Enter”. The program automatically updates the score. Records that ace for both Suzy and that rotation, and shows that your team, and Suzy specifically has the serve for the next rally.

When Suzy serves the next ball long and out of bounds, I touch “Suzy”, the orange error button “serve” and then touch “enter” to record the error. The program automatically records Suzy’s serving error, records an error and loss of point to both Suzy and that rotation, increases the running score by a point to the opponent and now shows that the opponent now has the serve for the next rally.

You get the idea.

I think that most teams at the Junior Club and High School levels are working on consistency in skills and increasing the termination frequency of the offense. SoloStats123 collects all the data required to do that.

Cons for SoloStats123: If you do still want to collect standard stats like passing accuracy (e.g. 0-4 scale), attack efficiency (recording all attack attempts), or successful digs (playable balls from opponent attacks), then another program or tally sheets would need to be used in conjunction with SoloStats. Again, I believe this would need to be done by an assistant coach or parent to collect all the relevant stats like that while the head coach stay engaged in the play at hand.

OR, you could collect those stats by watching the video of the matches at a later time. Which is what I tend to do for passing and set distribution.

I hope this helps you in some small way. If you and others in your club would like a demo, I could probably work something out to show you in a session we could arrange sometime after Big South. Just let me know.

As you can tell I am a convert to SoloStats123 for my particular coaching needs!”

Editor’s note: This article appears in the September/October 2011 issue of Analytics Magazine.

By: Benjamin Alamar and Vijay Mehrotra

Over the past few years, the world of sports has experienced an explosion in the use of analytics.  In this three-part series, we reflect on the current state of sports analytics and consider what the future of sports analytics may look like.

We define sports analytics as “the management of structured historical data, the application of predictive analytic models that utilize that data, and the use of information systems to inform decision makers and enable them to help their organizations in gaining a competitive advantage on the field of play.” Our definition is both expansive (in the sense that it includes not only statistical models but also the broader information value chain that surrounds these models) and restrictive (because it excludes traditional analytics applications such as demand forecasting, revenue management and financial modeling, all of which are certainly relevant in the business of professional sports). Our framework for sports analytics is presented in Figure 1.

Figure 1: A framework of sports analytics.

Data management includes any and all processes associated with acquiring, verifying and storing data in an efficient manner. In a sports organization, data can come from a variety of sources and may be presented in many different forms.

As shown in Figure 1, the data management function will feed both the predictive analytics function and the information systems that support decision-makers. Given this crucial role, good data management is essential, and therefore missing, incomplete and/or inaccessible data inherently reduces the value of any other investments in analytics.

In many organizations, data is often stored in isolated silos, so that getting data is often not a smooth process. Different groups within an organization such as scouting or training may have extensive data on players that other groups either do not have access to or do not even know exist.

For example, the personnel group at one NFL team had been collecting extensive performance data on various groups of both opposing players and their own players. The coaching staff had no idea that the data existed, but when they did discover it, they had difficulty accessing it. The data resided in spreadsheets on the computers of the personnel group instead of being integrated into a common data archive. This is a common situation within professional sports organizations.

Predictive analysis, the next piece of the framework, is the process of applying statistical tools to data to gain insight into what is likely to happen in the future. In sports, this can involve the projection of the pro careers of amateur players, identifying how the strengths and weaknesses of an opponent will play out against your own team’s strengths and weaknesses, or assessing whether a free agent would fill a need on a team at an appropriate cost. Depending on the importance of the problem, the time until an answer is needed and the data available, these analyses can range from simple comparisons to extremely complicated and cutting-edge statistical analysis. The results of these analyses may feed directly into an intelligent information system that provides decision-makers with standardized results. Alternately, such results may be reported directly to a decision-maker for special projects that may be outside of any standard systems.

Information systems, the next component in the framework, are increasingly common in the world of sports. When designed and implemented correctly, such information systems typically allow for visualization and interactive analysis of relevant information from multiple sources in one place, organized in a meaningful way to provide insights for decision makers. For example, a cutting-edge sports information system might combine unstructured information from scouting reports, summary reports from multiple data sources and results from predictive models. Such a system not only provides a data-driven decision support platform and integrates data from multiple sources, but (as we will discuss in Part 2 of this series) also has the potential to fundamentally alter and enhance the way a decision-maker does his or her job.

Decision-makers are the ultimate customers for all components in the sports analytics framework. However, the modern professional sports organization typically has many different decision-makers, including the general manager, coaches, scouts, trainers, salary cap managers and other personnel executives. Decision-makers in different functional areas may utilize different data and models to tackle different types of questions. Conversely, as mentioned above, one key problem today is that decision-makers in one functional area (such as scouts) rarely have easy access to information generated by personnel in other areas (such as assistant coaches or salary cap managers).

To summarize, our definition and framework for sports analytics encompasses several different and related aspects associated with turning raw data into information that is valued by – and has an impact on – decision-makers in the world of sports.

An Explosion of Interest in Sports Analytics

Though still a nascent, unstructured field (as we will discuss in more detail in Part 3), interest and activity in sports analytics has been exploding in recent years.

While studies applying mathematical models to professional sports data can be traced back more than 50 years [1], it is important to remember what the world looked look like as recently as 2005 when the first issue of the Journal of Quantitative Analysis in Sports (www.bepress.com/jqas/) was published. At the time this journal was launched, only two or three NBA teams thought about using advanced statistics in connection with players and strategy. Michael Lewis’ seminal book [2], “Moneyball: The Art of Winning an Unfair Game,” about the Oakland A’s use of data and models had recently been published, and no one had yet thought seriously about the application of motion capture technology in the context of professional sports. Just six short years later, more than half of NBA teams now utilize the tools of analytics on the team side of the their operation, most MLB teams now consider analytics a normal part of baseball operations, and companies such as STATS LLC are installing cameras in NBA arenas and NFL stadiums to capture more and more data.

On a broader scale, the annual Sloan Sports Analytics Conference serves as a vivid symbol of the growth of sports analytics. The first Sloan conference took place in 2006 in a few classrooms on the MIT campus with less than 300 attendees. The 2011 conference was held at the Boston Convention Center and attracted more than 2,000 attendees.

An Explosion of Data

Data within a sports organization used to consist of individual box scores, player and team summary statistics, text-based scouting reports and raw game films. However, the data available to decision-makers has grown exponentially over the last 15 years.

Several factors have contributed to this explosion in data. Innovations in sports science, ranging from training routines to nutritional regimens, coupled with improved reporting from medical staffs and trainers have all come with their own data sets that are gathered and tracked somewhere within an organization. With improved communications via the Internet, the frequency and amount of information captured, stored and distributed by scouts and coaches at all levels has grown significantly. Thanks to increased computing power and reduced storage costs, historical data about the games themselves is now packaged into many different formats, with companies such as Stats LLC, StatDNA and Sports Data Hub emerging to provide organizations with high-quality historical data presented with unique summaries and indexes.

Finally, the advent of motion capture technology has expanded the data collected from each game. This technology tracks everything that moves on a field every 100th of a second. The impact of this is staggering for it transforms the amount of information captured for a single game from a few hundred rows of data to well over one million. Major League Baseball, the NBA and pro soccer teams have implemented this type of technology.

The result of all of this is clear: The world of sports generates far, far more data today than could have been imagined just a few short years ago. Dean Oliver, director of Publication Analytics at ESPN, has spoken of finding “data that can win championships.”

However, as the computer scientist Clifford Stoll has said, “Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom.” Too much time is still spent by analysts using their skills to try and answer questions that are not meaningful to decision-makers in pro sports. For example, very little is interesting about the next new statistic that ranks all NBA players. General managers do not lose sleep trying to figure out who the best player in the game is, as that information is neither accurate nor actionable. Conversely, Mark Cuban, owner and general manager of the Dallas Mavericks, has often cited studies that either predict or examine the effects of injuries as delivering useful and actionable information to his coaching staff and team.

In other words, despite the remarkable growth in the amount and variety of data available of examination and analysis, the world of sports analytics still faces the same ubiquitous challenge: How to get meaningful information into the hands – and minds – of the people who are in a position to make effective use of it.

In Part 2, we examine some of the predictive models that are being used to create actionable information in the world of sports today and the information systems that effectively deliver valuable information to decision-makers.

Benjamin Alamar (quantsports@gmail.com) is the founding editor of the Journal of Quantitative Analysis in Sports, a professor of sports management at Menlo College and the director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA. He is co-author of the annual “Football Outsiders Almanac” and a regular contributor to the Wall Street Journal.

Vijay Mehrotra (vmehrotra@usfca.edu) is an associate professor, Department of Finance and Quantitative Analytics, School of Business and Professional Studies, University of San Francisco. He is also an experienced analytics consultant and entrepreneur, an angel investor in several successful analytics companies and a San Francisco Giants season-ticket holder.

References

1.     Lindsey, G. R. “Statistical Data Useful for the Operation of a Baseball Team,” Operations Research, Vol. 7, No. 2, March-April 1959, pp. 197-207.
2.     Moneyball: The Art of Winning an Unfair Game.

Tom Black – Women’s Head Coach, UCSD Tritons (now Head Coach, Loyola Marymount University)

Explaining the Cauldron

There is no improvement without intensity. This is what draws us to the cauldron. If we want our players to improve, they have to play hard. If we want them to play hard, we have to get them to compete. If we want to create a competitive culture, then we need to measure things, record them, praise successes, and hold ourselves accountable for results. The cauldron, as created by University of North Carolina’s soccer coach, Anson Dorrance, gives us a foundation.

The cauldron records statistics for skills and competitive play, weighs each score, and assigns a subsequent ranking to each player. Would it be effective? I believed it would be. My assistant coach, Tom Haight, and myself would spend the next four years experimenting.

Early Failures, One Success

I had implemented the cauldron for about two years before becoming the Head Women’s Coach at UCSD. Before then, as an assistant at USC, I had instituted the cauldron into our men’s program. I was intrigued by it for two related reasons. First, was my exposure to it in a Gold Medal Squared Clinic, and Carl McGown’s claim that the competitiveness created in practice, via the cauldron, was largely responsible for their two national championship runs. The second was the statement made by the cauldron’s inventor, UNC Women’s Soccer Coach Anson Dorrance, who claimed that a naturally competitive player would be instinctively motivated to find ways to ensure they rise to the top of the list. This made sense to me on a fundamental level, I pitched it to our head coach, and we instituted it.

I consider the two years we had it at SC to be a lackluster success to moderate failure. The success was in the accountability it created. The guys were well aware stats were being kept, and they eagerly checked the door every 11 days to see the rankings. I considered this to be a tangible improvement. There was something pushing them to work hard in practice, to realize every day mattered.

But, there were several aspects that left me disappointed. The first was, the coaching staff wasn’t using it to make any decisions, nor as a teaching tool. We were never acknowledging when people excelled or surged ahead, and we were never calling people in when their numbers dropped. I was aware Dorrance said he never talked about the cauldron or addressed it specifically, but this didn’t sit well with me. We were taking very specific statistics that directly impacted the outcome of our matches. To not acknowledge improvement, or to seek to fix dips, seemed a ridiculous waste of information to me.

There was one instance that stuck with me. We had an Opposite complain about his lack of playing time to one of our assistant coaches. The coach asked if he had seen the cauldron lately, his hitting efficiency was well towards the bottom. The player replied he didn’t see how this was possible since his errors were relatively low. The coach, in turn, pointed out how he had nearly double the amount of neutral swings as any other outside hitter.

His kills skyrocketed. There was no other coaching involved. He was just more focused every time he attacked, and intent on scoring. There was accountability, and he was responding positively to it. This lonely example stayed with me. Here, the cauldron had been successful. The player was unhappy with his situation. He addressed the coach. The coach provided an objective, non-personal statement, along with information the player could see and understand. The player responded.

But there were negatives. The guys were obsessed on personal statistics, to the point of making sure their block was recorded, while their team was losing in the drill. The losing carried greater weight, and the whining and complaining escalated as a result. The best player on our team would have been the best, and played just as hard without it. The cauldron was doing nothing for him. The worst players also received little from the cauldron aside from the validation they were, in fact, the worst on the team. I didn’t consider any of these things positive, and I wasn’t surprised in the least to hear off-hand the players were relieved when a new assistant coach came in, and the cauldron was gone. I left SC, still feeling I needed a cauldron with me for the women’s program at UCSD, but that it would not work unless the coaching staff actively made it a part of the training environment, and closely monitored its behavioral effects on the team.

Re-Tooling the Cauldron

Ron Larsen, the current men’s national team assistant, pointed me in the direction of a book he had read by Aubrey Daniels entitled “Bringing out the Best in People.” He said it had deeply challenged many of the notions he previously held true regarding the cauldron. This made me excited to read it, as I was wondering a few things myself. The book was all about measuring performance, determining what rewards truly work to motivate people, what type of performance charts are actually effective towards improving productivity, and above all, how that is tied in to actually bringing the best effort out in those around you. I coupled this book with “Moneyball” the famed book about the success of the Oakland Athletics and their innovative, if not savagely Darwinist, method of statistic evaluation to achieve wins.

The central point of “Moneyball” resonated. Dr.McGown hits on it all the time. The naked eye simply cannot be trusted as an impartial observer. Numbers have to be used to see the truth of things. There are patterns we need to be made aware of, and we simply cannot tell the difference between a .250 and .275 hitter, though we know there is a significant difference between the two, especially over time. How many times have we put in a less efficient, but more physical, player in the line-up, only to realize after a few matches the player with the less-impressive package was helping us more, (sometimes just by hurting us less)? If we were able to measure this in practice, we might be able to play the right person sooner, and thereby help the team faster.

The points of Mr. Daniels’ book were more profound. He took exception to the statement “what gets measured, gets improved.” Not so, he comments. “What gets measured, recorded, and rewarded, gets improved.” This made sense. Obviously, a high cauldron ranking should reflect in playing time, if the cauldron is truly measuring things the way we want them to be measured. However, there is a more important point here. Daniels, as well as a number of leadership authors, goes on to explain that the negative compels people to do just enough to avoid punishment. The positive, however, propels people to look ahead and achieve more. This is what the cauldron was for, and was by far the most important “reward” it provided. If we were measuring performance, and did not go absolutely crazy every time a player improved in a key category, we weren’t using it correctly. We weren’t making sure the improvement stuck. We had this great tool in front of us that could really make an impact on a player. Instead of telling Sarah “Hey, Sarah, you’ve done a nice job making that change in your hitting, keep it up.” We can tell her, “Hey Sarah, you made that change we were discussing, and look, you’re hitting efficiency has improved 20 points! That’s a great job, keep it up!” We have measured what was important to us, we have recorded it, and most importantly, we have rewarded it in a meaningful way that will stay with the athlete. This was a profound change for me as a coach.

Can we do the same thing through matches and game stats? Yes, we can, and game stats of course matter more. But, consider holding players accountable for their improvement with the cauldron and then without it. There is a tangible effect occurring by having this objective statistical feedback everyday in our practice gym. Consider how much better our reserves can be now that we have something to show and talk to them about. We can show them, just by taking time to sit down with them, that their improvement and performance matters to us. As Dr. Marv Dunphy explains, every player has the desire to be addressed as an individual. If we measure, record, and reward their improvement, they will get better, and thus our overall quality of play improves, and everyone is being pushed a little harder.

A second point by Mr. Daniels created a dilemma. He stressed that productivity charts must not be competitive. That the point of these charts was to solidify the group to compete against outside organizations, not to create strife within your own. He goes on to explain that every individual within the organization must have a real chance to achieve a top-ranking, provided they do everything expected of them.

My assistant coach, Tom Haight, and myself took to grappling with these issues. Tom had created our cauldron for us at SC, and we held in common our interest of the statistics, and the ability to use them effectively. We had a few central questions:

“What was the ultimate point of the cauldron?”

“How do you reward properly?”

“Will playing time be based solely on the cauldron?”

“How do we post, or present, results?”

Answering the Questions

Of course, finding the answers to the questions is a continual process. There is always a better way out there, somewhere. But after a summer of wrestling with what we wanted the cauldron to do and say, we entered our first season at UCSD with a format we were proud of.

What is the ultimate purpose of the cauldron? Once we answered this fundamental question, the rest became easier. The effect of the cauldron is indeed to stimulate competition, but for us, this is not its purpose. The purpose of our cauldron is to stimulate constant improvement. An intense, competitive, environment is the necessary impetus for this improvement. Players cannot get better unless they are mindful and invested. Accountability promotes mindfulness, and competition spurs investment.

How do we reward properly? For awhile, I toyed with the ideas of making t-shirts and passing them out every eleventh day, but I believe this was missing the point. The principle comes down to the statement Mr. Daniels repeatedly hit home, that if we want something done, we need to positively acknowledge it every time it happens. Too often as coaches, we criticize often, say “good job” when a player finally gets it right, and then leave it alone, expecting it to be fixed forever. This is not the case. We need to reward good behavior, good technique, constantly in order to reinforce it, and hopefully, have it become ingrained. The objectivity of the cauldron can provide a player with real information they are improving. If bad techniques are being practiced, the cauldron should be reflecting that as well, and we can work to fix them. But when we do so, it’s critical we sit down with the player and objectively show him/her the numbers, with a solution as to how to improve. We never threaten a player with statistics or hang it over their head. We want them to be as enthusiastic as possible to receive this information.

Will our playing time solely be based upon the cauldron? No. It is a very valuable tool in the decision-making process, however, and if a player is ranking top in her position, and I’m not starting her, I better have clear reasons why, and I better communicate them to her. This happened a couple of times. Generally, it was because a particular skill was so low, that our line-up wouldn’t allow us to be that deficient and still be successful (i.e. an outside hitter ranked in the top-6 overall, but was in the lower half in hitting. We already had an L2 who ranked higher than her overall, and the L1 playing ahead of her ranked lower overall, but much higher in hitting. We needed a point-scorer in this position, and the lower-ranked player reflected this, even though her overall score was lower. The cauldron reflected what we knew, that the starter was a worse all-around player, but better hitter. There were clear reasons, therefore, for starting the lower-ranked player and we communicated that to the higher-ranked one. If she wanted to start, she needed to become a better attacker.)

Do we post everybody’s score publicly? This is the one aspect of our cauldron at UCSD, which I believe to be slightly unique. Tom Haight created individual folders for each player that “grab” data from the master sheet, and present each player with only their own information. On their sheets, they will see the following:

1. Statistics in each category: Competition, hitting, serve-receive, serving, blocking, setting, defense

2. Overall team ranking for each category (Provided they are in the top-8, otherwise, this category will appear blank.)

3. Overall position ranking for this category (Provided they are in the top-8, otherwise, this category will appear blank.)

We believe in this format for a few reasons:

1. “Become, don’t compare.” This is a saying of Marv Dunphy’s that we believe in. These sheets are a way to provide a player with their information and ranking, without them seeing anyone else’s. This keeps the player’s focus on themselves and what they need to do. Not on whether or not they are hitting 5 points higher than Sally.

2. Building on #1, we felt this is the best way to prevent the internal “competition” Mr. Daniel’s discussed in regards to productivity charts. We knew the goal was to indeed get our team as strong as possible to compete against the outside competition. However, there is a dilemma here, because we need this internal competition in our daily practice gym to get to the level we want to be. We need our players to get after each other. 6 of our 19 players are going to start, which is a major difference between a sports team and a business, where everyone is performing. The answer, we feel, is to keep each player’s focus on being as good as they possibly can be, and not whether or not they are better than their teammate. Again, the presentation of the folders with personal information only makes this possible.

3. The printouts are much easier to read than the master sheet. The player’s feel like they are getting something out of them.

4. The printouts present me with an opportunity to write notes in the margins, and meet with players in a positive arena to discuss objectively what’s happening with them on the court. This time is invaluable.

The Cauldron in Action

Our first season at UCSD, we inherited a team with no returning starters, and finished with a 19-8 record, tying for the final play-off position before being voted out by committee. This was a successful year in the eyes of our staff and players, and all involved stated there was an intensity in the gym that had never previously been there before.

There were still some of the negative effects I had witnessed at USC, though to a much lesser extent. There was still some obsession over stats at the wrong time. Many of the girls complained at the end of the year meetings that they were so worried about their own position, they never felt able to come together during matches.

While I was very concerned over these two complaints, Tom Haight was much more confidant. He pointed out throughout the season that what the team was experiencing was simply growing pains. Anson Dorrance cites his own freshmen’s unease with the cauldron, every year. He stated how these young players are looking for acceptance, and instead, are being told they will have their brains beat in unless they compete and play to win. In effect, our entire team was a group of freshmen learning how to manage their emotions for the first time in an intense competitive environment, and we would most likely only be hearing these complaints from the incoming freshmen class from now on.

Tom’s words proved true. Entering our second season, we built upon the previous year, and finished with a record of 26-3 as well as a national ranking of 5th. Every member of our team reported in their end of the year meetings they felt the team bonded, and functioned, very well on the court. Most players said they liked the cauldron, every player, including the freshmen who were uncomfortable with it, said they would rather have it than not. Nearly every returnee thought the team handled the competitive aspects of it better than the previous year. They felt they were able to negotiate the balance between competing like crazy and being a great teammate.

Final Thoughts

I spoke with one coach in our athletic department who felt the cauldron was used as a crutch to “do your coaching for you.” This would be a valid point if it were true, and in some cases it might be. For us, the cauldron doesn’t provide us with answers, it provides us with information to make better decisions and to give better feedback. It allows us to get better faster. Faster than we would without it. It doesn’t save us any time, and in many ways, creates more work for us. When I ask the question, though, will this make us better? I answer with an emphatic yes, so we do it.

The final important question might be, do I think the cauldron was the reason we were able to have success? I think it was a reason. I think the character and commitment of our young ladies was the biggest reason. I think the work ethic of our players and coaches was a very big reason. The importance of our cauldron, in my eyes, was we were able to reflect the values and cultures of our training gym in an objective manner that would spur continual improvement. Better than we could have done without it, and that made it worth it.

**The advent of the folders, and answers to the questions above**

**The result of the past two years. The feedback given, and conclusions**

For a Trevian to post an article written by a Titan, the material must be good stuff.  This probably means nothing to most of you unless you grew up on the North Shore of Chicago.  I attended New Trier High School, home of the Trevians.  Rob Beam, Head Coach at Oakland University in Rochester, MI went to Glenbrook South High School, home of the Titans.  Rob is one of the up and coming young coaches in NCAA and wrote a great piece: The State Championship Standard:  How Statistics Can Get You There. Considering I have a brother that also attended GBS, I’ll give Rob the props he’s due   The following is good stuff.  You can learn more about Rob and his Oakland University Golden Grizzlies here: http://www.ougrizzlies.com/sports/w-volley/oakl-w-volley-body.html

The State Championship Standard:  How Statistics Can Get You There

Formatting online did not do the charts in the article justice.  Click here State Championship Standard for a pdf of the article.

1. Introduction

Wikipedia defines statistics as “a mathematical science pertaining to the collection, analysis, interpretation, explanation, and presentation of data.”  In describing their use, the website continues, “Statistics provide tools for prediction and forecasting.”  While many people will stare blankly at me or perhaps run in the opposite direction when I admit this, but this is exciting news to me!  In fact, it is so exciting I think that I can help each of you move closer to a state championship based on understanding statistics.

In this session I will show you how to manipulate the information contained in a basic box score to make it more meaningful.  Along the way we will discover the factors or components that most strongly correlate with winning and losing.  We will also debunk long perpetuated volleyball myths.  And, finally, we will take this information and use it to shape 17 different aspects of our program including our drill designs, match evaluations, player interactions, and more.

2. A Statistical Caveat:  The best we can do is correlation

1. Definition of Correlation:  “A relationship in which two or more things are mutual or complementary.  In statistics, the degree to which two or more variables are related and change together.” (Encarta Dictionary)
2. Definition of Causality:  “Is the process of making something happen.  It denotes a necessary relationship between one event, cause, and another event, effect.”  (Wikipedia)
3. The unfortunate truth is that we cannot create causality.  Meaning, if we simply kill more balls, although that would be a good start as we will learn later, it will not cause a win.  At best an increase in kills/set would correlate strongly with winning.  While this is a small distinction, it is a necessary one.

3. Box Score Basics

• K = Kill (successful attack)
• E = Attack Error (unforced or stuffed attack)
• TA = Total Attempts (all attack attempts combined)
• SA = Service Ace (a serve that leads directly to a point)
• SE = Service Error (a serve that leads directly to an error)
• RE = Receive Error (a serve reception that leads directly to a point)
• Total Team Blocks (total number of BS (block solo) and BA (block assists)
• BE = Block Error (Net or centerline violation during a block motion)
• GEN BHE = General Ball Handling Error

4. The Stats Inside the Stats:

1. Positive Points = K+SA+TTB
2. Negative Points = E+SE+RE+BE+BHE
3. + Points/Set = (K+TTB+A)/# of Sets
4. – Ponts/Set = (E+SE+RE+BE+BHE)/# of Sets
5. Differential (Dif) = (K+TTB+A)-(E+SE+RE+BE+BHE)
6. Dif/Set = (K+TTB+A)-(E+SE+RE+BE+BHE)/# of Sets
7. Hitting Efficiency (Hit Eff) = (K-E)/TA
8. Kill % = K/TA
9. Error % = E/TA
10. SA:SE Ratio = SA/SE (1.00 = the number of SA = the number of SE, >1.00 = more SA than SE, < 1.00 = more SE than SA)
11. SA:RE Ratio = SA/RE (1.00 = the number of SA = the number of RE, >1.00 = more SA than RE, < 1.00 = more RE than SA)

5. The State Championship Standard

What if I told you that based on what we have already learned we have the information necessary to win a State Championship?  While it might be hard to believe, it is all there.  In order to accurately prepare for this presentation I evaluated 80 High School Matches at the State Semifinal and Finals levels in Michigan, Ohio, and Illinois.  In Chart A you will find the results of this data collection.  The whole spreadsheet is included as well and is sorted by Dif./Set.

In the left column you will see 8 critical statistics.  There are then four additional columns grouped by either “Correlation to Winning” or “Correlation to Losing.”  In the former we find the information that correlates with a team winning a match.  This is easily understood as “if you do this, you tend to win.”  In the latter, we find the number that correlates with losing.  Meaning, if you are achieving at or below these levels, you are likely to lose.

The “Absolute” number is the threshold number where every team that achieved that level of performance won.  While this is interesting information, it would be very difficult to train a team at these levels.  (Even the undefeated 2008 Penn State National Championship team barely reached the +11.3 Dif/Set mark.)  In two areas you will notice that there are question marks.  That means that in that statistic there was no threshold for performance that correlated with an outcome.

The “Strong” number is the more practical number.  This is the level of performance at which there was a high correlation with the corresponding outcome.  These “Strong” numbers are where I believe we should focus our attention.

6. What Statistics Matter Most?

Now that we have the standards at which we want to perform, it is important to know which statistics correlate the strongest with winning and losing matches.  Chart B lists the 3 statistics that correlate best with winning.  Chart C lists the 3 statistics that correlate best with losing.  Chart D lists in order the statistics and their cumulative strength or importance to winning.

Based on the analysis in Chart D, it turns out that the Dif./Set is the most important number that you can know.  It is also interesting to see that the +point side of this ratio finished second overall and is far more significant than the –points side.  The third most important number is Hitting Efficiency.  And surprisingly, the fourth most important measure of performance is a team’s Ace to Receiving Error Ratio.

7. Interpretations and Implications:  How do I apply this knowledge?

1.  Basic Losing Factor Analysis:  It is of utmost important to understand what factor or factors statistically correlate with a losing outcome.  Here is a real example to illustrate the utility of the numbers.  In 2008, we averaged a +4.63/set in Dif/Set. When I looked closer at our point production it was clear that we needed to score more points in the attacking component of that number.  Further examination revealed a need to increase our Hit. Eff. by increasing the Kill % side of the equation.  This led me to look at our set distribution to see if our highest Kill% attackers were receiving enough balls.  It turns out that less than 20% of our offense was set to our top 2 attackers, both middle hitters, in terms of Kill %.  In our spring season we worked very hard to correct this and we were able to increase our middle distribution to over 38% of our offense.  This led to a direct increase in Dif/Set as we were able to achieve a +7.56/set because of this new focus.

2.  Basic Winning Factor Analysis:  If you are fortunate enough to lead a team that is winning it is helpful to understand why you are winning.  By running these numbers you might find the areas of strength or advantage where you are beating your opponents.  For instance, in our wins last season we averaged hitting over .241 with 38.5% kills.  By using this number I can set a standard in practice for my starting group that will challenge them to rise to the occasion in training.

3.  The Serve-Pass Game:  The first myth that we are able to debunk is that an ace to service error ratio is important.  This particular myth is a favorite of TV broadcasters and I cringe every time I hear it.  It turns out that it is not at all.  In fact, it is so unimportant that the team with the best ace to service error ratio lost the match in my study!  What really matters is an ace to receiving error ratio.  Our teams must be trained so that they produce more points from the service line than they give up in the receive phase.  This means you can literally lob the ball in if you are the best passing team in the state.  But, if you make a lot of receiving errors, then a tough, point scoring service game is a necessity if you want to win.  And, if you are really gunning for a conference or state championship the combination of high ace numbers and low receive errors is essential.

4.  Point Scoring Mentality:  The second myth that we can expose as fraudulent is the one that suggests that, “the team that makes the fewest errors wins.”  This is simply not true.  In every way the data suggests that it is the team that produces the most points that will win. While it is true that limiting negative points is helpful, it is much better to focus on kills, blocks, and aces.  It is also true that attack error % was either last or second to last in each comparison.  It simply doesn’t correlate to winning.

5.  Feedback Control:  Because we understand the factors that correlate most strongly with winning we can choose the feedback that we give our athletes more accurately.  For instance, if I have an outside hitter that is struggling with her hitting efficiency, I am much more likely to focus on her finding a way to kill the ball.  The reason is simple, the statistics show that limiting errors will not as strongly correlate to winning a match.  I want to prepare this athlete to be on the floor and make a contribution so I will teach her a new shot or find a set tempo that works better for her in terms of Kill%.

6.  Match Evaluations:  Before our staff meets with the team after a match we always run the numbers.  When you have a zero-sum situation as we do in volleyball it is easy to exaggerate a win or a loss.  While winning is good, that outcome does not necessarily indicate a strong performance.  By way of example we won a match last year while hitting .118.  Conversely, all losses do not necessarily indicate a bad performance.  For example we lost a match with a Dif/Set of +8.33 and a Hit. Eff. of .182.  I should praise my team for a great performance in this case.

7.  Individual Meetings:  We can use this data on an individual level as well.  We track K%, E%, Hit. Eff., RE%, and several other numbers for each player in practice and in competition.  When we set goals at the beginning of a season or training cycle we reference these numbers.  During the middle of the season we look at trends over time and see what effect our training is having on a particular player’s performance.  In a meeting with a player that is struggling to crack the line-up we can have a rational, objective discussion about playing time.  We can identify the areas easily where they are falling short of our goal numbers and set up a training plan for them to make increases.  I find this is even helpful in selling the importance of strength and conditioning.  If an athlete is behind her teammates in Kill% one of my stock answers is “get stronger.”  In this case, if the athlete is committed to being on the court, they will attack the weights like never before.  While it took almost two years for every player to find comfort in the statistics, they like it now for these reasons.  Everyone in our program knows where they stand and they know I am committed to helping them reach the level of performance that will correlate with their success.

8.  Parent Meetings:  While parent meetings are rare in the collegiate ranks, they are a reality in high school and club volleyball.  Imagine having a tool at the ready to review an athlete’s performance against the known standard when playing time is being disputed.  In this way, we as coaches can soothe the relationship and provide tangible, objective evidence for our choices.  Statistics even lend themselves to follow-up conversations in that you can tell a parent that their daughter has improved her hitting efficiency since your last call.  In my experience this new information will recruit a challenging parent to your side.

9.  Team Building:  In a given season it is our job to get the absolute most out of our player’s collective talents.  One way to do this is to set goals for a team based on statistical performance.  In our program we will deliver a monthly update so the players can see the team’s progress toward our ultimate goal.  This is a way for the team to rally together and work towards success.  Another way to build a team is to make decisions based on the numbers.  Perhaps during tryouts we select a player for the varsity team solely because she has the toughest serve.  Another example is to track statistical performance by rotation and then start in the best side-out or point score rotation.  There are dozens of ways that a team can be optimized and the fun of understanding the numbers is arranging the pieces of the puzzle to earn the desired outcome.

10.  Program Building:  It could be argued that building a program, meaning creating a lasting tradition that stretches long after a single team has completed its’ season, is the ultimate in coaching.  The New York Yankees, the Pittsburgh Steelers, and the Detroit Red Wings are professional examples of this status.  By using this data we can track progress over multiple years.  We can use it to set expectations and standards for every team.  We can use it to develop our JV, freshman, and middle school players and coaches.  For instance, we know that our Dif/Set has gone up from -.22 in 2006 to +4.13 in 2007 to +5.55 in 2008.  That means we are growing and improving.  We can also see that our Kill % has increased while we have been able to decrease our E% in the same span.  On the down side we have identified our SA:RE Ratio as an area that needs attention.  Therefore, we have worked on developing tougher more consistent serves for our returning players and recruited incoming athletes to address this specific need.  In this way our program is developing from year to year based on our understanding of statistical performance.

11.  Practice Planning:  When we understand the factors that most highly correlate to winning, we can change the nature of our practice.  Perhaps more time should be allocated to certain skill areas.  For instance serving, serve receive, and attack are very important in terms of correlation to winning and could be given more time each day.  Another idea would be to always train around the numbers.  Instead of just going through a hitting line, have an individual player attempt to kill 3 of 9 tosses against a blocker and a defender.

12.  Drill Design:  When we design drills we can accurate set goals based on the numbers necessary to correlate with winning.  Here are a few of my favorite statistical based drills:

i.        “Ace and Replace” (SA:RE Ratio Focus):  Because SA:RE is of high importance we created this fun, simple drill.  Three players begin on Side A of the net.  The other competitors in the drill are serving on the opposite side of the net.  The passers get to stay on if they can successfully receive the ball from the server.  If they are aced (we define this as an “unsettable” ball or a “bad miss”, meaning a pass that lands outside of the court) they must exchange places with the server.  For every successful pass (not an ace) the passer makes they get a point.  For every ace the server get one point and they get to become the passer.  Because teams will serve away from the best passer once they figure out the game, it is helpful to rotate passers.  Another solution to this problem is to award points to all three passers when a successful pass is made.  A final solution would be to force the servers to alternate between all three passers.

ii.        “4 v 6 FBK”  (Kill% Focus):  We know that Kill% has a stronger correlation to winning than limiting Error%, so we created this uneven numbers game to work on our First Ball Kill % (FBK). (It could be played 6 v 6, but this is a drill we used during our spring season when we had smaller numbers.)  One team of 4 consisting of an outside or right-side, a middle, a libero, and a setter gets 10 opportunities to receive serve.  (Using a free or down ball would make it easier, but would focus on transition percentages.)  Their goal is to kill at minimum 4 balls out of this 10.  We allow the 4 side to cover a blocked ball and continued to play out that rally.  Also, we have the team of 6 transition any ball that is not killed to provide “game-like” scoring repetitions.

iii.        “Lambo” (Point Score Differential Focus):  If the ratio of +pts/set and -pts./set is examined it is approximately 2 to 1.  With that in mind, my friend Chris Lamb, head coach at Wichita State, created this game and I named it after him.  For this game the scoreboard starts at 3:3 for each team with the first “3″ representing the number of +points and the second “3″ representing the number of -points.  Each time a team scores a +point the first “3” moves up.  Each time they make earn a –point the second “3″ is moved down.  Play begins with a serve and score is kept for each team based on the result of each play.  When a team wins on the +side, they get a BIG POINT and when they lose on the –side a BIG POINT is awarded to the other team.  We most often play this game by staying in the same rotation until a team either wins or loses.  Once that happens we award the BIG POINT and then the team that lost rotates.  The team that is still playing stays in that rotation until they either win or lose.  We play for the most points through all 6 rotations.  Ties are decided by having each team pick a rotation and playing it out.

iv.        “Mary Jo” (+Point Production Focus):  This game presents two teams of six an opportunity to only focus on +point production.  It is best understood as an alteration to the scoring system and not the flow of play.  Meaning, each team plays regular rally score volleyball, but only +points in a row are recorded.

The game begins in the “open” state.  Any time during the “open” phase there is a positive point scoring action (kill, block, or ace), the responsible point scoring player, or players, becomes “hot.”

The “hot” player(s) must then score a second point in a row in order to earn a BIG POINT (one that is recorded on the scoreboard).  Any subsequent point would also result in a BIG POINT for her team.  If two or more players were involved in the point scoring action, then they are all “hot.”  Once any one of these players scores the next point, they become the only “hot” player and they may continue to try to score points for her team.

If the hot hitter’s opponent makes an error (attack error, block error, receiving error, ball handling error) the hot hitter remains “hot.”  If the hot hitter’s team makes any type of error (the previously mentioned plus service error) the game returns to the “open” phase.  If the hot hitter’s opponent scores a point (kill, block, or ace) then that player or players become hot.

v.        “5-2-15” (Hitting Efficiency Focus):  The numbers in this drill correspond to the number of kills, 5, the number of attack errors, 2, out of 15 attempts that a team must earn to win a point.  These numbers are one combination of our winning Kill%, 33%, Error%, 14%, and Hitting Efficiency, .172.  Obviously if a team meets the standard they will hit .200 and so we are training at a higher level than we need to execute in a match.  Here is how we run the drill.  Team A is trying to earn the “5-2-15”.  Team A receives serve from Team B and attempts to side-out.  The result of their attack is stated on the scoreboard.  At the termination of the rally they return to the same rotation and try to side-out again until they have a minimum of 15 attack attempts.  If at any Team A exceeds 2 hitting errors Team B wins.  If Team A earns 5 kills on the first 5 swings we continue playing to see if they can manage their offense.

Who’d a thunk it? Who’d a thunk that a brainstorming session with Mike Hebert at Minnesota would turn into a new statistic for volleyball? Really, it’s an old statistic with a positive spin, but it makes sense to players. It’s hard to teach the negative and easy to teach the positive. Thus came ‘In Play Efficiency’ or I.P.E. You can learn more about it on an old blog post: What is IPE?

Jay Van Vark and Marc Swindle, Assistant Coach at Air Force, have created a volleyball statistical software called iVolleyStats.  iVolleyStats is an iPhone application for coaches and fans to gather and report stats for your volleyball squad.  Within the application, iVolleyStats is now using IPE!  Voila!

I must admit, that’s pretty cool stuff   If you want to learn more about iVolleyStats, you can find it on iTunes here: http://itunes.apple.com/us/app/ivolleystats-match/id355306829?mt=8 or you can watch a bit about it from the video.

Consumers often choose products to purchase based on an affinity and trust with a vendor or the developer of a product. StatEasy is a great volleyball software program that has been developed by a passionate volleyball enthusiast, Mike Ressler. This software is going to be the next and best volleyball software on the market…oh yeah, and its FREE (take advantage of it now)!

First let’s talk about Mike. He found me through CoachRey.com and wanted to get me set-up with his software right away.   I checked out the video of StatEasy and was certainly intrigued. He was willing to get me set-up during his lunch break while he was on the road.  I told him I can wait a day until he returned…oh and where was he? He happened to be at Penn State setting them up with his software. Yeah, this software is good…real good.

More about Mike first! He has a computer science degree from Carnegie Mellon, he grew up playing in Pennsylvania, and went on to manage and be an assistant coach on the collegiate volleyball level. Mike is a computer programmer that knows and understand volleyball!

So about StatEasy, it uses a simple keystroke data entry as a match progresses. As simple as a player’s number and a short code for the skill. For example, for pass by #6, the simple keystrokes of “p 6″ would do it. When a match is complete, a stat sheet is created with information that is very similar (and even more detailed) than an end of set college stat sheet.

What is really cool is that you can tie in that match with video. So if you want to view all of #6′s passes for that match, simply click on the link for #6 in the passing column of the stat sheet. What is even cooler, is if you have multiple matches, you can choose any match #6 played in and view all those video clips.

In addition, StatEasy does not have to be used for matches at all. As a coach at Winthrop University, we use StatEasy for practice. It is very handy and often times more beneficial than match statistics. A great way to get real time data and make corrections in practice. Genius!

This is just an overview. You need to check-out what Mike and StatEasy are doing. He’s even working on voice recognition software for StatEasy, and maybe even considering video recognition, but I think that would be for Version 3.0 already slated to be called “StatEasiet!”, but that sort of an inside joke…sorta. I wouldn’t put it past Mike…

Check out StatEasy. I’ve used the Data Project volleyball software line and enjoyed using it, but it is very expensive software. StatEasy is the next generation volleyball statistical software at a much better price!

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As for all products I have used and mentioned on my website, I have never been paid by any vendor to discuss their product.  This information, like all information on my website, is completely my opinion.  My intention with my website is NOT to make money (as I do not even cover the annual hosting fee with banner ads), it is to spread my passion and knowledge of volleyball on to others.

Rotate 123 is a great volleyball rotation coaching tool that allows you to input numerous line-ups in a variety of offenses (5 – 1, 6 – 2).  Instead of writing down all 6 rotations by hand, crossing-out, and manipulating serve receive patterns, just enter your roster into the system and drag-and-drop into their positions.  Voila!   You can manipulate your line-up a million times from there with all the players in your roster.  It even includes libero entry (for any position) and defensive specialist entry.

The software was created and designed by two volleyball enthusiasts that coach club and high school volleyball.   So it has real/game-like functionality.  It’s been featured and promoted by the AVCA (a nice plug) and its FREE!  Yes, so far it’s free!  Jump on it now and check it out.

Click here to check out Rotate 123

After watching the incredible National Championship Match again last night (some incredible offense and defense!!!), I believe the box score tells us why Penn State beat Texas and won an unprecedented third National Championship – in a row!  As with most sports, it comes down to errors.  Texas, especially Destinee Hooker, showcased spectacular kills, but Texas committed the most errors.  Penn State kept the ball in play more (Team IPE).  As is said in the volleyball world, “Teams that win the serving and passing battle, win the match”.  Thus is true in the National Championship.

Following are statistics from the box score:

Team IPE or In Play Efficiency accounts for total points in the match divided by a team’s errors.  Its not an exact figure, but I have been using Team IPE throughout my Winthrop season as well as my juniors season and have found that a team that keeps the ball in play at least 80% of the time wins the match.  Penn State’s Team IPE was 83%, Texas’ Team IPE was 76%.

Penn State did all the little things better.  They served in, made clean ball contacts (Texas had 5 BHE’s – Ball Handling Errors), only had 1 blocking error, and less hitting errors.  Golf has a saying, “Drive for dough, putt for dough”.  Penn State putted better;  Russ Rose made the dough.

***As a side note, Alan Reifman, a professor at Texas Tech University has a great blog on volleyball and statistics.  He has an interesting analysis on the match too.  Visit his blog at: http://volleymetrics.blogspot.com/

I had a funny experience at the Monument City Classic Tournament. My Carolina Juniors 16s Team played the early match on Sunday morning. Our team arrived first at the court, chose the bench, and I sat down to sip my tea. On the adjacent court, a father turned around and stared at me awkwardly.  He exclaimed, “You’re Coach Rey!”. I said, “Last I checked.” I was taken aback. He went on, “I couldn’t sleep last night. Our team was playing well yesterday, but we just couldn’t buy a win. I thought, maybe some statistical information would help. So, I started looking up volleyball statistical software online in the middle of the night and came across your blog. And POOF, now you’re here!”. He said it was a bit surreal

I have received quite a few questions on volleyball stat software and I can only offer my advice based on the software I have used. At the University of Minnesota, I used the Data Project line of software, both Data Volley and Data Video.   I loved that software, but it can be quite complex and take quite a bit of time to enter all match information.  The University of Minnesota had a great Director of Volleyball Operations that would key the match live (where I would receive the information on a computer at the bench) and key incoming opponent match video when it arrived at their office. Its easy to do as a volleyball program if you have a Director of Ops, only a few matches a week to key in, and its your full time job   Its not so easy when you are a small club program (and its not cheap software either).  I know the USA National Teams use the software as well.

At Winthrop University we use Gamebreaker software. Its a decent software that requires keying of a match, but its all Mac based. I’m a PC guy and I’m not fond of the Mac functionality.  So to me, the software is fair.

I’ve looked into purchasing a couple softwares for my iPhone, like TouchStat, who University Athlete turned me on to.  I even went as far as to download it, but I have yet to use it.  I also sat in on a presentation by the owner of Volleyball Ace at the AVCA Convention.  It also appears to be a very nice software, but again, I have never used it.

I’m still a pencil and paper kind of guy and upload it to a spreadsheet later. I take all the stats on the bench (it keeps me from getting too animated with the refs and keeps a consistent demeanor for the team, ala Russ Ross).  I keep passing stats (on a 3 point scale), serving stats (on a 3 point scale), hitting, blocking, and then I register how each point ends.  I’m then able to get a breakdown of positive and negative points from how the points end and put together the Team IPE (which I believe needs to be above 80% to win a match.  I broke down the 2009 Women’s NCAA Championship Match to find Penn State’s Team IPE of 78.4% and Texas at 69.5%.  Penn State took care of the ball better, in all aspects of the game.  Thus Penn State wins a third National Championship).

Taking stats manually takes a little time, but once the spreadsheets are set-up, it works for me. There are many workbooks within my final spreadsheet, and they all tie in together. I am able to have team and individual stats broken out from it.  Although, I am actually shifting statistical philosophies for coming matches and concentrating more on defensive stats.  A greater breakdown of blocking statistics as well as digging statistics.  I will also be taking a shot chart of the opposing teams to see where our biggest weakness is on the court.

My current stat sheet I have been using: Volleyball Stats Match Chart

The stat sheet I will be using this weekend: Volleyball Statistics Match – Shot Chart

I hope these help you and if you have experience with volleyball statistical software, please keep us informed!