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The Identification of Valid Volleyball Performance Indicators in The Women’s Super 8s/ League 2015-20
Prologue
It all started when I wanted to know which statistics are the most important in volleyball. I looked into this subject for my Masters dissertation using data I collected in the Men’s Super 8s 2012-13 season. I then recently ran back the study using data I collected in the Women’s Super League 2019-20 season. This is where I wanted to see which performance indicators are related to success in volleyball. I was surprised to find that defence pct. and block-defence pct. are highly valid performance indicators, which I did not find with my Masters dissertation results. If I was to focus on these performance indicators in my analyses of performance then I would need some more evidence that these are truely valid performance indicators. This is because the amount of work needed to have these values for individual players is immense (they are currently calculated for the team only).
As for more evidence, I actually have five seasons worth of data that I can drawn upon. So I’m going to run back the study again but with new (or technically old) data.
Purpose of the Study
The purpose of the study is to identify valid volleyball performance indicators of all technical skills currently measured (see link) in the Women’s Super League/ 8s using the methods of final league points (Women’s Super 8s, 2017-18 only) and close set outcome (Women’s Super League/ 8s, 2015-20).
Introduction
Just as a reminder, performance indicators (PIs) normalises data into percentages, ratios or per set/ match averages. Performance indicators are when they are associated with a successful performance or outcome. Success in volleyball can be defined as final league points and winning or losing a close set (close set outcome, i.e. a set score with a 2 point difference, e.g. 25-23).
My first study investigated valid performance indicators in non-elite women’s volleyball showed strong evidence for points efficiency ratio, hitting pct., defence pct. and block-defence pct. as highly valid performance indicators. A limitation to the study is the dataset is based on the Women’s Super League, 2019-20 season. In that season, we don’t have the final league placings because of the league suspension due to the Covid-19 outbreak. Also, this is not a true league since the league splits in two after the first half of the season. So any correlations between the league points accrued and the various performance indicators may not be truely accurate. When comparing winning and losing teams in a close set, this was only based on 26 sets where the set score was within 2 points.
However, I have five seasons worth of match data in the Women’s Super 8s (2015-18) and Super League (2018-20) to drawn upon. I have much more close sets (a set score within 2 points) to find any differences between winning and losing teams. I also statted the second most matches in the Women’s Super 8s 2017-18 regular season (28 out of 56) so I should be able to find a correlation between performance indicators and final league points. Although I am missing half the data, the league was a true league in that each team played each other team twice, home and away, and it was a completed season.
Method
Matches were video-recorded and performance data statted with the DataVolley program. The type of peformance data collected are related to serve, reception, attack, block and free balls. Free ball/ overpass dig errors and setting errors were also collected. Performance indicators being tested can be seen at the end of the blog post. Since I don’t collect stats on defence, I used opposition attack zeros as a proxy measure for digs and opposition attack kills for missed digs.
Pearson’s correlation was conducted to identify correlations between the performance indicators and final league points. The sample consists of 28 matches (out of 56) played in the Women’s Super 8s league, 2017-18 season. Only regular season matches are included. Play-off and National Cup matches are excluded as it did not contribute to the league position. I have also reused the sample of 36 matches (out of 36) played in the first half of the Women’s Super League, 2019-20 season.
Wilcoxon signed ranks test was conducted to identify differences between winning and losing performances within a close set. I define a close set outcome as as any set score that finishes with a two point difference, e.g. 25-23, 26-24 etc. This should reveal the most critical performance indicators. The sample consists of 86 sets involving Women’s Super 8s and Super League teams from 2015 to 2020. These include sets taken from regular season matches, play-off matches, National Cup matches and one Legacy Cup match. Although the competition is different, the level remains the same, i.e. the top tier of women’s volleyball in England. I have excluded fifth sets as they only go up to 15 points.
Results
I will try and simplify the statistical test results. I will ignore the statistical test values (r-score for Pearson’s Correlation and z-score for Wilcoxon sign ranks test) and just show the p-values from those tests. What we are looking for is a p-value of less than 0.05 (typically written as p < 0.05). It basically means there’s a 95+% probability that there is an actual, or , correlation or difference. The smaller than p-value, the more likely that there is a correlation or difference.
It looks like there is very strong support for points efficiency ratio, hitting pct., block-defence pct. and defence pct as highly valid performance indicators. There’s stronger evidence these four performance indicators are correlated to final league points in the 2017-18 season compared to the 2019-20 first half of the season the p-value went down from p < 0.01 to p < 0.001 or p < 0.0001. For close sets from 2015-20, the evidence is much stronger. The p-value is p < 0.00001 for three of the performance indicators. This means there is a 99.999+% probability or higher that there is a difference and that the results did not come about by chance.
There is some support for service rating, hitting pct. after reception and hitting pct. in transition but it is not particularly strong. There is a lack of support for block efficiency pct. and reception rating as valid performance indicators. They do have signficiant results for the 2017-18 season but a p-value of less than 0.01 is not as strong as the other p-values.
Discussion
The study shows that the points efficiency ratio and hitting pct. are highly valid performance indicators. As I have stated in the last study, I suspect this the points efficiency ratio and hitting pct. to be valid performance indicators across many studies based on this simple premise: to win a match you need to win three sets and to win one set, you need to score 25 points before the opponent does. So the ability to concurrently score points and not lose points in errors should be critical to success. This is what the points efficiency ratio measures. And since most points are won and lost via the attack, the hitting pct. should be critical to success too.
What the results show again is strong support for the defence pct. and block-defence to be highly valid performance indicators. The evidence is particularly strong when defence pct. correlated to league points in the 2017-18 season (p < 0.0001) and when block-defence pct. is correlated to close set outcome (p < 0.00001). This means as well as the ability to attack effectively is stopping, so is stopping the opposition attack.
There was a signficant result for reception rating but it was for the 2017-18 season only and it wasn’t particularly strong with a p-value less than 0.01. Every coach knows that the better the quality of the pass, the better quality the set and the better quality the attack, i.e. a higher hitting pct. There’s even research studies out there to back this up. The stats from the 2019-20 season backs this up too – the better the pass, the higher the hitting pct.
So it would have make sense that the higher reception rating a team has, the better the hitting pct. will be and thus the more points that can be effectively scored. This seems not to to be the case. Even if we take the short cut of looking at hitting pct. after reception, it only has one significant result in the 2019-20 season and it’s only a p < 0.01. It looks like hitting pct. in transition is a better discriminator of success in volleyball as it had significant results across all three conditions.
Practical Implications
It would be nice to do a literature review on performance indicator studies in women’s volleyball just to compare results and see it reflects the results here. It could be that defence is more important in women’s volleyball, even at elite level. As it stands, it looks like I should focus more on the defence and block-defence in my analyses. Now I have to think about whether having these values for individual players is worth the extra work.
Performance Indicators Measured
Valid Performance Indicator Studies Series
This is a part of the valid performance indicator studies series, including an overview of all five studies that I did. Links are below.
6 | Overview of the five valid performance indicator studies 5 | City University London + Opponents (BUCS South Eastern Division 1), 2017-20, Set outcome 4 | IBB Polonia Men + Opponents (Men’s Super League), 2018-20, Set outcome 3 | Women’s Super 8s + League, 2015-20, League correlation (2017-18) + Set outcome 2 | Women’s Super League, 2019-20, League correlation + set outcome 1 | Men’s Super 8s, 2012-13, League correlation + set outcome (Masters Dissertation) 0 | Academic Literature Review, elite men’s volleyball (2004 – 2012)
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