What Is Point Differential And Why Does It Matter? - Part 2

In our earlier post on Point Differential, we talked about different ways of expressing the score:

• Total number of points (ex: 104 to 103)

• Point Differential (ex: 50.2% to 49.8%)

• Sideout Differential (ex: 56% to 52%)

While each of these has value, Sideout Differential probably tells us the most information, because, in addition to telling us the margin of victory (or defeat), it tells us a little bit about how that victory (or defeat) occurred. A 70% to 68% Sideout Differential tells us something different than a 50% to 48% Sideout Differential. But what else can Sideout (or Point) Differential tell us?

In terms of Sideout/Point Differential, there’s 6 scenarios that can happen:

1. We have a large positive differential and win the match.

2. We have a large negative differential and lose the match.

3. We have a small positive differential and win the match.

4. We have a small positive differential and lose the match.

5. We have a small negative differential and win the match.

6. We have a small negative differential and lose the match.

(There’s also the scenarios where the differential is zero and we either win or lose, but those are similar enough to scenarios 3 through 6 to lump them in together.)

Scenarios 1 and 2 aren’t too interesting. Well… maybe interesting in some ways, but in terms of statistical analysis, not so much. You were either much better or much worse than your opponent that day and there was a gap in fundamental skills. Small tactical adjustments aren’t going to erase a 7-point lead.

Scenarios 3 through 6 on the other hand, are a lot more interesting to us from a statistical perspective, and these scenarios are ones where little things can tip the balance of the match one way or the other.

These four scenarios recently played out in the NCAA Women’s Volleyball Final 4. Let’s take a look, using our handy GMS Stats app to produce the analysis.

In our post breaking down the Final Four match between Illinois and Nebraska, we saw scenarios 4 and 5 play out, depending on which side you were rooting for.

Illinois Point Differential Screen, GMS Stats app

Illinois outscored Nebraska, but lost the match. Or, if you’re a Nebraska fan, Nebraska got outscored, but won the match. This is uncommon, but it does happen. In a 5-set match, the team that scores more points only wins about 78% of the time.(*) On the flip side, we have the Stanford - Nebraska Championship Match:

Stanford Point Differential Screen, GMS Stats app

On average, the margin of victory in a 5-set match is about 5 points, so these two matches were close even by 5-set standards.

In the post on that match, we looked at some of the rotational matchups and how they played out. A lot of times, these matchups can be the difference in a close match. Rotational order, matchups, and clutch play are the three things I like to look at in close margins of victory.

The danger with looking at one match is that there’s a lot of randomness involved. Is that rotation in which you gave up a late-game run really a bad rotation, or did you just catch a hot server at the wrong time? This is why we like to take a multi-match view of things. In the GMS Stats app, we built that into the Wizard feature:

Point Differential Wizard Screen, GMS Stats app

This is the Wizard screen I pulled from a 5-match sample. I was coaching a professional team and wanted to see how our performance translated into wins and losses. For a single match, this translation is simple: if you outscore your opponent, you expect to win the match. If you get outscored, you expect to lose.

But things get trickier over multiple matches. If your Differential is 2% over a half-season, what do you expect your record to be? How about 5% or 10%? Fortunately, the Wizard feature does these calculations for us!

In this 5-match sample, we had a small but meaningful 2% edge over our opponents and that resulted in a 3-2 match record. Intuitively, we probably sense that this is about right. Maybe, we figure, we might be able to go 4-1, but 5-0 feels like a stretch. On the flip side, since we’ve outscored our opponents, we probably feel like 2-3 would be a letdown and 1-4 would indicate that something is really wrong.

Over time, this Point Differential-to-record translation can give us coaching insight. We can now imagine the previously mentioned scenarios reducing down to:

1. We are outscoring our record.

2. We are underscoring our record.

If we were 5-0 with a differential of only 2%, this would tell us some things as coaches. The first thing it would tell us: don’t get too cocky just yet, we’re probably getting lucky! It would also indicate that, while the results are good now, we should still be looking to make some changes if we want to keep the winning streak alive. There’s always the temptation to not make changes when the team is winning, but in this scenario, changes might still be warranted.

If we were 2-3 or 1-4 with a positive point differential, it tells us something different. First of all, it tells us that we’re not doing quite as bad as we think! So, while some changes are probably necessary, they might not need to be as drastic as the record might indicate. It also tells us that we’re probably losing some close games, and we have an opportunity to close them out better.

In this case, we look at our three Close Game Factors:

1. Rotation Order

2. Matchups and Scouting

3. Clutch Play

More on these factors in our next post!

(*) 5-set win % and margin of victory data provided by Volleytalk legend The Bofa on the Sofa.

2018 NCAA Championship Breakdown

In the 2018 NCAA Women’s Volleyball Championship, Stanford edged out Nebraska in a close 5-set match. We saw in this post that Nebraska won their semifinal match over Illinois despite being outscored by 1 point. The margin in the final was the same, but this time Nebraska wasn’t able to pull it out.

End of match screen. GMS Stats app.

The first two sets were close, but sets 3 and 4 saw each team trading blowouts. In an eerie recall of the semifinal vs Illinois, a challenge decided a crucial point late in the 5th set, but this time it didn’t go Nebraska’s way. Stanford won the 5th and earned their 8th National Championship.

In a match this close, we expect the margins to be thin statistically, and that was the case in this match.

Stanford Point Differential Screen

Both teams were at about 59% sideout for the match, which, while lower than their season averages, is also higher than either team typically allowed. This is common- seeing the overall sideout rate in a match between two top teams end up about halfway between what those teams sided out and what they allowed against most other opponents. Let’s break down the statistics more to see if they can tell us where the slim margins of victory came from. All screens and statistics courtesy of the GMS Stats App!

When I analyze a match, the first thing I do is look at the overall Point Differential, and see the Sideout level for the match as a whole. The next thing I like to do is look at the 3 Key Factors to Sideout. We’ll look at both the Stanford and Nebraska Sideout Key Factors to see the similarities and differences.

Stanford Sideout Key Factors

Nebraska Sideout Key Factors

Plenty of similarities here. Both teams passed well. Nebraska hit significantly better In-System (attacking after a Good Pass) than Stanford, but Stanford was better Attacking Out-of-System- after a Bad/Medium Pass or in transition. So Nebraska was In-System a lot, and hit well when they were. That’s usually a recipe for success. Let’s look at the defensive side of the ball to find out a little more information.

Stanford Opponent Sideout Screen

Nebraska Opponent Sideout Screen

Again, plenty of similarities. Nebraska dug a bit better while Stanford blocked better. Blocking can be a deceiving stat because while Stanford only outblocked Nebraska 10 to 9, they did so while only giving up 22 tools/block errors, while Nebraska gave up 34. Hitters on both teams scored off the block well, but Stanford was better here. However, this was compensated by Nebraska being the better defensive team. Again, percentages come in handy. Both teams had 69 digs, but Nebraska dug those 69 balls on 100 chances, while Stanford had 119 chances to dig.

Since Nebraska hit better on the match, we see, as we often do, that backcourt defense has a bit stronger of an effect (in NCAA Women’s volleyball) than blocking on the opponent hitting efficiency.

Finally, we see the serving. Both teams knocked the opponents Out-of-System at a similar rate. All told, the Key Factor statistics were close, as you might expect when the Sideout % (and thus, overall points scored) is so close.

So what was the difference?

If we walk it back to the first image in this post, we see there were 209 total points scored this match: 105 by Stanford and 104 by Nebraska. Since Nebraska out-hit Stanford, we’d expect them to be better within the rally, and that was true. If we take away service errors and aces, and isolate only the points where a rally took place (meaning at least one of the teams got a chance to attack), we see the following:

Total Rally Points: 182

Nebraska: 94 (72 Kills, 9 Blocks, 13 Stanford Errors)

Stanford: 88 (65 Kills, 10 Blocks, 13 Nebraska Errors)

So indeed, Nebraska was 6 points better within the rally. But now let’s look back at the No-Rally Points, where there was either an ace or a missed serve:

Total No-Rally Points: 27

Nebraska No-Rally Points: 10 (2 Aces, 8 Stanford Missed Serves)

Stanford No-Rally Points: 17 (9 Aces, 8 Nebraska Missed Serves)

So Nebraska was 6 points better within the rally, but Stanford was 7 points better when no rally happened at all! We find that this happens quite a bit- the team that wins the match was no better, or even slightly worse when, “volleyball happened,” but a substantial margin in the serve-pass game can often compensate for that.

With so much attention on Stanford’s size and power at the net, and the flashy digs by libero Morgan Hentz, it’s easy to forget that the serve-pass game so often dictates the winner and loser, even (especially?) at the highest levels.