Every statistic measured in volleyball relates to one of two things:
The first, a measure for the quality of any individual player’s touch of a ball as it relates to placement & speed to what transpires directly after it, or some combination therein as demonstrated by a number or proportional rate. (Serve, Pass, Dig, Set & Assist, Attack & Kill, Block)
The second always relates to measuring the success of team outcomes determined by a composite of these touches. (Side-Out Rate, First Balls, Team Hitting Efficiency For/Against, Rotational plus/minus, etc.) The former usually an attempt to separate the contribution of any individual component(s) to the latter.
Last week I made the case for individual plots across an opponent spectrum to be both “richer in content and precisely informing.” The same is true for “team” based measures as well. Skills driving team statistical measures are not performed in a vacuum, either. They are met by varying degrees of resistance determined by the same skills their opponents possess.
Some Examples:
[1] The “Delta H%” of a team across the whole season.
When a team hits .300 in a match to its opponent having hit .200, that is simply a delta of +.100 for that team and -.100 for the opponent. Hitting efficiency is one of only two statistics I know correlated to winning where the team having an edge wins at least 95% of the time. (The other is total points scored in the match! About 96% of all matches are won by the team who scored more points and about 95% are won by teams whose H% was higher. Pretty crazy proximity to those stats if you think about it for a second.) If two teams were to have equal H% for a match, we know it didn’t end in a tie, but we also know their strength in playing the game is nearly equivalent. So, when a “Delta H%” graph crosses the “x axis,” (the line where Delta H% is equal to .000), that should indicate any team’s D3 rank.
The gold graph above is for St. John Fisher in 2025 playing NCAA D3 teams among the better half of the landscape. (Top 60) It crosses the x axis right about the #15 Rank. The “Tuesdays with MORE” post two days ago showed it was ranked #12, #14, #15, #10 by the 4 experts, and #10 according to NPI. The highest red point seen between ranks [1, 30] is at the ordered pair (19th, .115) – The New Paltz match on Sunday afternoon at Fisher where they hit .333 to New Platz’s .218. A “Delta H%” equal to .115. i.e. .333-.218 = .115. Given New Paltz was ranked 19th by the T100 later that evening places this red point where it is seen on the plot above. (I dare say that red point will be shifting left soon as a consequence for SUNY New Paltz winning at home against Vassar last evening! Of course, the blue point just under the axis at #9 (Vassar) might also have a corresponding shift to the right, too? They did lose, after all.) Fisher had come back from down 6-8 at the turn in set #5 in the NP match on Sunday, so I was compelled to share the H% Delta being the best all year against a Top 30 program with its coach as some additional encouragement beyond what had to be his relief after the win. LOL
In preparation for this post, I decided to color code the same matches by their Home–Neutral–Away distinction to see what they each looked like as components of the main plot. The red plot (Home) crosses the axis at about #3, something I’d hope NYU wouldn’t argue after playing there the day before on Saturday. The gray plot (Neutral) crosses about 12th ranked which seems reasonably close to the overall rank on the whole, exactly what you’d expect. The blue plot (Away) is most influenced by losses to Vassar in Poughkeepsie and Juniata in Huntingdon crashing through near a #23 rank. The Vassar point under #9 on the axis mentioned earlier and Juniata’s very low blue point on the graph under its #7 rank in the T100. Juniata hit .566 that day so regardless of how effective the SJF offense had been, this point was destined to be the lowest on this graph for the year! (Actually, the lowest I have seen in 4 years’ time by the Cardinals!) And no, I never mentioned that to Fisher’s coach! If he just read it here, though, then “Sorry Matt!” LOL (I am hoping it is far enough in the rearview mirror to be more worthy of a laugh than a cry right now!)
[2] The Team H% Efficiency. Not only across a single season, but the last 4.
You can’t just look at a season long H% and believe it tells a truthful story! The reason is because matches played against worthy opponents often require twice as many points played and sometimes up to three times as many kill attempts, too. You could hit .400 in a 3-set match win against one opponent and then hit .250 in a 5-set match win against another the next day and the combined H% for the team will be well below .280 on this weekend, skewed much closer to the outcome against the more worthy opponent with its 3 times as many kill attempts. Maybe that doesn’t bother you at all? Acts like a weighted statistic in the direction of tougher resistance. However, what if the scheduling practice of a team is such that in subsequent years it competes with markedly different levels of opponents? That is almost certainly going to take place in the next couple of years with some programs, given the NPI’s failure to reward commensurate to its risk! So much of measuring is tilted toward growth. Growth from match to match, week to week, month to month, and even year to year. It affirms that which a program does in its preparation to succeed.
The plots above remind me of a child’s game, “One of these things is not like the other.” It is not a function of any difference in scheduling of opponents over these years because when excluding the Empire 8 teams Fisher is obligated to play, the mean rank of their opponents every year has consistently been #31 plus/minus 1 position. Always playing between 16 & 18 Top 40 opponents which represents somewhere between 56% & 60% of its total D3 opponents every year. If you tried to plan that kind of consistency, you likely couldn’t do it! So, what was going on in 2024?
In the field of economics there exists something called elasticity of demand. When a product’s demand doesn’t change too much, even as the price has large fluctuation, that product is said to be inelastic. Those whose demand varies greatly as even small changes occur in price are said to be elastic. Concert tickets are elastic because the demand for them changes significantly with their price points. Prescription drugs are inelastic because regardless of cost, they are essential and will be purchased. Look at ’22, ’23, & ’25 (so far) plots above. One might say they’re like inelastic products. Fisher’s offense able to maintain its .250 to .300 state of efficiency across a top 40 resistance. However, in ’24 the offensive efficiency, like elastic demand, varied greatly depending on the level of the opponent in this domain. Fisher would regularly play exceptionally efficient against the 2oth to 40th ranked team’s defensive pressure in ’24 but be far more impeded by those among the top 20 than previous years. Perhaps the ’25 plot is important to reaffirm this team is back to where it was in ’22 & ’23 when it qualified for the NCAAs?
One hypothesis to explain this graphical behavior might be the teams ranked between 20th to 40th last year being lesser skilled than the same interval the two years previous. Not a very strong one, given so much other evidence suggesting the exact opposite is true! Maybe it had to do with an injury to its most effective OH late in the ’24 season? Certainly, a factor, but not enough to create this large a discrepancy. Was this group too young with not enough experience having graduated a 4-time All American hitter the year before who erred less than half as much as every OH in America, save for the other two in the top 3 for this category? Maybe so. Was there some “It Factor” preventing the kind of efficiency it had come to expect previous years playing the top 20? A conference record of 5-2 would suggest that might not have been the case, either. I think you get my point. The stories a plot can tell are plentiful. Some preposterous and others not so much. Legitimate statistical inquiry in my experience often asks more questions than it answers, but it forces those with influence to ask the right ones. This is its secret power, I think.
[3] The Team Opponent’s H% Efficiency. Not only across a single season, but the last 4.
You can look at a contrasting team statistic for how efficient opponents are in their attacks. Pretty much for similar reasons as above to explain why season long opponent hitting efficiencies is a relatively useless stat! I think I’ll show the St. John Fisher’s 4-year history in the form of its plots since 2022 below to see how your impression of its story might differ than mine.
Remember earlier when I suggested there was lots of evidence to suggest top 40 teams over the years are actually getting better across time? That might be a partial explanation for the obvious trend seen in the above plots of H% against Fisher year after year. I might suspect the vertical displacement may account for some of this, but what about the rate of change (slope) across the domain of the top 40? Is the best getting better faster as it relates to the SJF defense’s ability to impede attacks over the years? Or is it more pointedly something to do with its ability to dig and/or block opponents of similar stature? Here we go again! If you are someone looking to get better who has influence over such things as it relates to any team, the questions asked and needed to be answered are placed squarely in your corner, now. The whole point in why you’d want to have these plots available to the stakeholders of a team, really. (Pun intended!) Let any solutions be driven by the pursuit of excellence by those for which it matters most. That is why plots like these may have some value. Me? I just find them interesting to dream up and then see if they go anywhere!
[4] A team interested in projecting First Balls, Live Balls, and overall Side Out Rates for the purpose of setting numerical goals and then doing the things required to be able to achieve them against any opponent on the spectrum.
About 10 weeks ago, I had a conversation with an opposing coach after a scrimmage. One I have known for almost a decade because he coached my son about that long ago. He was very excited to track live first-ball side-outs and set reasonable goals for his team performance in this category. He mentioned the team’s goal was to terminate 35% of live balls before their opponent could gain possession. (1BSO excluding terminal serves by the opponent.) I asked him, “Shouldn’t that goal be variable, dependent on who you are playing? And wouldn’t you want to be slightly more concerned with the rate of those originating from in-system passes? These questions and his willingness to go down the rabbit hole with me set the stage for what I think is a pretty cool data tracking system I am sharing below.
The T100 doesn’t rank D1 & D2 teams with its D3 focus which the team above does play against all, but Massey does this reasonably well. (The x axis coordinates.) Those who know which are which recognize the D3 teams generally off to the right, precisely where side-out performance becomes gradually better. Exactly as you’d expect when a D2 plays a D3 for the most part. This team’s next match is tomorrow against Dominican, NY (D2), a program Massey ranks #90 at the moment. This projects a team goal 38% side-out rate, a 59% Live-Ball Rate, and about a 65% overall side-out rate by looking above #90 on the main plots above. Victory is more likely attained if it can exceed those last two targets by just a hair.
I am using the finger measure graphics to demonstrate something I refer to as Marginal Terminal Win Rate & Marginal Live Ball Win Rate. These are the portions of the overall side-out rate, beyond First Balls, that contribute to side-out success. Scanning left to right you can see Marginal Terminal Win Rate shrinks slightly as teams become weaker. (“smaller hand” graphic) This is most probably a result of erroring relatively less than stronger teams because they are clearly not exerting serve pressure on this team’s receiving rotations like the better teams do. (Supported of course by the lower first ball rates playing higher skilled teams on the left.) The Marginal Live Ball Rate (“larger hand” graphic) is slowly growing from left to right because not only will this D2 team accumulate more First Balls, but when they don’t get them, the likelihood to generate transitional live ball win opportunities against these weaker teams is also enhanced.
How does it work?
A team representative observes every opponent’s serve in a match. (His team’s receiving opportunities.) He then touches either 2 or 3 buttons. The first is their rotation (1,2,3,4,5, or 6), the second is a “vowel – A, E, I, or O,” and the third, if the vowel wasn’t A(ce) or E(rror) is simply whether the side-out was achieved or not and if it happened on the first ball or later. (F1, F0, T1, T0) – The last button need only be touched if the vowel had been I(n system) or O(ut of system). The raw data looks like the table below on the right with its yellow cells. The following interpretation is then used to produce the plots above for this team to set its match goals:
These are just a few examples of team-based statistics that I had readily available in order to demonstrate the main point that plots across a strength of the opponent spectrum almost always offer a more complete accounting with a better chance to inform growth opportunities and enhance the learning process. When I coached swimming for 30 years, I always approached the craft in a way that would engage high school student athletes to interpret their own plots representing practice and meet performances. There really is no “team play” in swimming other than strategically preparing a line-up with the best chance to defeat a worthy opponent. You can bet the athletes understood their role and were a valuable part in the process to formulate the plan because it was they who had to go execute it to get the job done! You show them a numerical picture story in the form of a plot, teach them how to read it, and let their interpretation and/or imagination become a part of what drives their success.