2,326 Match Case Study: How Women’s College Players Win Matches

We’ve studied 2,326 college women’s college matches from August 1, 2017, through 2019. Our objective was to find answers to some of these questions.

• What is the difference between match winners and losers?
• Which statistical categories are most important?
• What should a college coach or player focus on, to improve faster?
• How are women’s matches different from men’s?

Below are the results.

Note: We studied Men’s College Tennis last month here.

The Stats We Measured

Here’s how we performed this study.

We took data from 2,326 matches and divided the data into two categories.

1. Match Winner
2. Match Loser

For each match, we collected 233 stats per player. Here are 17 examples.

• Number of 1st Serves In
• Total # of 1st Serves Hit
• 1st Serve %
• 1st Serves Won
• 1st Serves Won %
• # Aces
• # Double Faults – Net
• # Double Faults – Wide
• # Double Faults – Long
• Return Errors – Combined Courts
• Return Errors – Deuce – 1st Serve
• Points Won on Net Appearance
• S+1 FH Point Won
• S+1 FH Point Won %
• 1-4 Contribution %
• 1-4 Win %
• Return Errors – Deuce – 1st Serve – Forehand

For more stats & data, sign up for our email newsletter to the right for announcements on a brand new data analytics product coming soon. You will be able to access big data on 10,000’s of matches. It will have match data from juniors, college tennis, all the way to top ATP & WTA pros.

With this amount of data, we can answer almost any question about match winners vs losers.

• How many more aces are they hitting?
• What is the 1st serve percentage for each?
• How many forehand errors do they make?
• How many backhand return errors do they make from the ad court?

The best way to figure out what to do with all this data is to start with the high-level metrics, then follow the biggest number differentials.

The Average Number of Points Per Match in College Tennis

On average, these matches had a total of 127 points.

• The match winner averages 69.8 points won.
• The match loser averages 57.2 points won.

The loser wins 45% of all the points. This shows you how small the margins are between winning and losing.

So the total difference, on average, is 12.6 points over the course of a match.

What areas of the game is the match winner winning these 12.6 extra points? Let’s find out.

The Difference Between the Winners & Losers

First, we have to study the high-level metrics to figure out where the differences are.

Here were the results.

When we look at this data, the biggest point differentials are on:

• 1st serve points won
• Returns errors

These two stats combine for 6.27 points during the match.

Let’s round 6.27 to 6.3.

If the match loser can win 6.3 more points, then the match winner will lose 6.3 more points. So we have to multiply by 2, and we get… 12.6.

Now the match is dead even!

Below we’ll analyze each of these stats further, and how a coach or player can use this information to practice.

First Serve Points Won

It’s not surprising that 1st serve points won is one of our metrics.

From these 2,326 matches, the combined first serve percentage was 63.5%. So, most points start with a 1st serve in the court.

Why, then, does the match winner win over 4 more points on their first serve?

Note: the differential might look off by 1/100th of a point, but this is simply a rounding issue. The differential is actually 4.37532…

What can the match loser do to close this gap? Two things.

• Decrease the 25.64 number = improve returning 1st serves (we’ll get to this next).
• Increase the 21.27 number = improve 1st serves.

We saw that the ace differential was 0.34, so that’s not going to help enough. Our data also shows us that over 80% of 1st serve returns are made for both players. So, there must be something going on after the return.

We know from our previous lessons that improving the first 4 shots and improving serve strategy are important.

This time we get to look at a larger data set for the serve +1 and return +1 data.

S+1 = the server’s 2nd shot, or the 3rd shot of the point.
R+1 = the returner’s 2nd shot, or the 4th shot of the point.

Note: This data accounts for points with 3+ & 4+ shots. So double faults, aces, and return errors are not counted here. Our next stat will look at return errors. We saw above that double faults have a -0.99 point differential per match.

Above, you can see that the match winner is creating a larger gap on S+1 and R+1 forehands.

So we have a few takeaways from this table.

• We should be practicing forehands as the S+1 and R+1 shots.
• We should know which serve & return locations improve our chances to get a forehand on the next shot.
  For example, serving wide on the deuce court might give me a better chance to hit a S+1 forehand than serving down the T.
• All 4 areas have greater than a full point differential.

This shows that college players should be practicing serve +1, and return +1 forehand and backhand patterns in practice.

These stats are all connected, so improving returns will help close the gap on 1st serve points won by bringing your opponent down.

For more stats & data, sign up for our email newsletter to the right for announcements on a brand new data analytics product coming soon. You will be able to access big data on 10,000’s of matches. It will have match data from juniors, college tennis, all the way to top ATP & WTA pros.

Return Errors

Next, we dive into return errors.

Match winners are making nearly 2 fewer return errors per match than losers. We’ll start breaking this down by deuce vs ad court, and 1st vs 2nd serves.

Returns errors are more common in the deuce court.

There are more return errors on 1st serves. This makes sense with the 63% first-serve percentage.

We also found that:

• Forehand return errors account for a larger differential than backhand errors.
• Forehand return errors in the deuce court account for the largest differential.

Women’s college coaches should definitely have their players practice improving their returns, we knew that.

But we can now prioritize returning 1st serves, especially to the forehand, and especially in the deuce court. This is where match winners are separating themselves.

Interestingly, for men, the difference was backhand returns made in the ad court.

What We Could Add to this Case Study

Any statistical analysis of tennis is going to have some gaps. These sorts of analyses are complex and must be taken in context with what we know about our sport.

A few questions we’ve left unanswered. Why are these differentials happening?

• Is the match winner simply hitting more S+1 forehands, so they’re winning more points there? OR are they making fewer errors on this shot?
• Are match winners making less forehand return errors because the opponent is making less 1st serves? OR do they have a lower error rate on their forehand returns?
• Does this data change for left-handed players?

We can certainly draw some conclusions from our data, but we need to be careful not to make connections that we don’t have data for yet.

Don’t assume, for example, that the return error differential is only because the match winner is a better returner. Perhaps they’re a better server, or more likely a combination of both.

If you see anything else we may have missed, comment below!

Key Takeaways: How to Win in Women’s College Tennis

Remember that the match loser won 45% of all points.

That’s 9 out of 20 points!

If the loser can get 1 extra point, every 20, the match is back to level.

After measuring 233 stats, we wanted to find the areas of the game that can help you as a coach or player, win that extra 5%.

For women’s college tennis, here are several guidelines.

• First serves are more important than second serves.
• Improve your footwork and court positioning so you can hit a forehand on S+1 & R+1.
• Practice your S+1 and R+1 forehand patterns most, but still work on backhands.
• Practice forehand returns, especially on the deuce side.

Make your practices more focused. Rather than simply “hitting balls,” work on these specific areas of the game to improve faster.

For more stats & data, sign up for our email newsletter to the right for announcements on a brand new data analytics product coming soon. You will be able to access big data on 10,000’s of matches. It will have match data from juniors, college tennis, all the way to top ATP & WTA pros.