2018 Service Error (Men’s)

It’s been about a year since I posted, so I figured my unpaid sabbatical from this blog should come to an end soon. I snatched the 2018 men’s data, parsed it, and decided that the controversial (if you were to survey volleyball parents) topic of service error should be the first thing I dive into a little.

*all analysis will be done only using the top 10 teams from the final AVCA poll and will only include matches when a top10 team played another top10 team.*

Service Error % vs. Ratio of Won Matches Against Top10 Teams

We’ll start with Service Error and its relationship with Winning or Losing the Match. You’ll quickly notice that LBSU doesn’t lose a lot of these big matches – and that they, Hawai’i, and UC Irvine were relatively low in service error this season. But in the bigger picture what you see is that a team’s service error percentage and their ability to win top10 matches is actually almost uncorrelated. Just looking at the chunk gathered around 50% of matches won level, you’ll notice huge swings in service error% for those teams – an indication that SE% really is hardly correlated to winning and losing in these matches for these teams.

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And if we alter the analysis to look at Sets Won or Lost in a top10 vs. top10 matchup, the trend holds about the same.

Opponent FBSO Eff vs. SE % per Match

The next chart we want to look at is how your team’s Service Error %  (x-axis) affects your opponent’s FBSO (y-axis). This is probably the best way to get the most bang for your buck in analyzing your team’s serving ability. While of course we are failing to standardize the level of play (Long Beach plays more top10 teams than Lewis does) and take into consideration rotational advantages (Long Beach’s Josh Tuaniga serves with a strong trio of blockers in front of him), this method of analysis remains pretty solid overall. In the viz, the label is the team who is serving and the goal is to have your team appear lower on the y-axis, indicating that your opponent is struggling more in the FBSO phase.

*we use FBSO efficiency (Team A 1st ball sideout – Team A 1st ball errors)/(Team B total serves) because a missed serve will be an automatic sideout, but a tough serve may force an attack error by the receiving team. The same reason we like attack efficiency rather than Kill Percentage – we want both sides of the coin, positive and negative since a point is scored each rally.

So what we see is that Long Beach (2018 Champs) consistently appears towards the bottom of the graph, indicating a strong ability to slow teams from siding out on the first ball. You’ll also see that their name doesn’t appear on the right half of the graph (higher service error) very often at all. Weird. UCLA on the other hand, captained by National Team Coach John Speraw, unapologetically takes the position that stronger serving is a must and the errors that come with it are acceptable. Obviously this line of reasoning isn’t total bs since the Bruins were the ones who almost defeated Long Beach in the national championship match. But we’ll spend more time looking at these two styles later on.

Opponent FBSO Eff vs. SE % per Set

Above is the same graph except broken out into Service Error% per team for each set. Just as with the per match data, there is a weak positive relationship between an opponent’s FBSO eff and your team’s service error %. If you just look at the 20% SE line, you can see that there are 19 sets alone that had 20% SE and have a wild range of FBSO Eff from over .700 to below -.100. Because this trend holds at most values of service error, the correlation cannot be described as anything above weak.

Opponent FBSO Eff vs. SE % per Season by Player

Here are the servers from 2018 on a top10 (in a match against another top10 team) that had at least 50 serves – making them regulars back at the service line.

Opponent FBSO Eff is again the metric we judge these players upon – and yes, as we mentioned previously, a player like Tuaniga might serve with DeFalco, Amado, and Ensing blocking so there may be some built-in bias…

Top servers will be lower on the chart and I’ll let you take a look on your own.

Opponent FBSO Eff vs. SE % per Match by Player

Opponent FBSO Eff vs. SE % per Match by Player (FBSO < .250)

Above are two charts, the top being how players perform on a per match basis (with 5 or more serves in the match). The bottom chart is just a zoomed in view for performances which held an opponent to an FBSO Eff of .250 or lower while the specific player was serving.

Top8

Finally, here is a per-team breakdown on servers. The trend lines are the correlation between SE% and Opponent FBSO Eff (more vertical meaning a more positive relationship). You’ll see the top servers on each team at the bottom again, as they hold opponents to the lowest FBSO Eff – regardless of their service error levels. These charts are for the entire 2018 season (in top10 vs. top10 matches). It becomes pretty clear for some servers why their FBSO eff numbers are so terrible as they are consistently located in the 30+% service error realm. Even if a 30% SE player was at a 10% ace level (an elite level in these top10 matches), the opponent would still be at .200 before you even consider how often receiving teams FBSO anyway. Missing your serve at that rate just makes being effective super difficult.

The final two teams are listed below:

ucla-lbsu

Here’s the strategy we were discussing earlier, visualized. UCLA in blue, LBSU in gold. You’ll notice that just on average, LBSU is getting teams into a worse position in terms of FBSO Eff. UCLA’s opponents average around the JT Hatch level of .400 FBSO Eff, partly due to the higher error from the service line from the Bruins. The interesting thing though is that we kind of get two clumps of servers, visualized below:

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With cluster 1 being high error servers with an average FBSO Eff around .420, while cluster 2 consists of lower error servers with, revolving around the .300 level or so. Naturally the majority of cluster 2 players are from Long Beach while cluster 1 primarily consists of UCLA kids. Yes, you could say that if Arnitz and Hess could just make more serves, the graph wouldn’t be skewed this way, but reality is that only Micah Ma’a is in LBSU territory in terms of effectiveness.

So I’ll leave you to draw you own conclusions, but just know that Long Beach only hit Jump Floats 17% of the time, while UCLA hit them 23% of the time. So it’s not that UCLA is hitting 100% Jump Spin and therefore missing more often – therefore the answer may lie elsewhere.

Possibly it’s LBSU’s use of changing the pace of the serve perhaps more than UCLA? Perhaps their athletes are more competent in the skill or less afraid to fail or can manage a poor toss better?

Personally, I think it’s because in these top10 matches Long Beach holds opponents to a 0.257 attack efficiency while UCLA holds them to a 0.308 efficiency. 50 points is big difference. So yes, the two teams have differing styles of serving, I believe it’s the block/defense portion that leads to the even larger split in opponent FBSO Eff.

If anyone who actually reads these posts has any specific type of analysis you’d like to see presented, just shoot me a note!

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