🎾 Pt 4 - What happens at different levels of the competitive ladder — and how a player's skills shape the structure of a match.

This is the last in a series on differences in how tennis matches play out at different levels of the sport. I compare women’s and men’s recreational matches, Division 1 college matches, International Tennis Federation events, and WTA/ATP tour level events. Data can tell great stories: here’s one of them.

Four Key Takeaways

1. Rally patterns peak in length at the college level.

College matches have longer rallies than both recreational and pro matches: players are skilled enough to sustain exchanges but not yet able to finish points as efficiently as pros.

2. The forehand becomes the primary engine of improvement.

While for men, the forehand winner-to-error ratio shows steady improvements at different levels, women's forehand winners tend to dip at the D1 level. At the highest level, however, the forehand is clearly a strong weapon for both.

Women
	Rec 3.5 and 4.0	Rec - all	D1	ITF	WTA
BH winners/ errors	0.37	0.40	0.35	0.39	0.39
FH winners/ errors	0.47	0.43	0.42	0.52	0.56

3. Serve quality transforms the game at higher levels.

Double faults fall while overall serve strength improves dramatically (doubling for men!) comparing rec to pro play. Serving moves from “just starting the point” to being a true offensive weapon for a professional.

Men
	Rec 3.5 and 4.0	Rec - all	D1	ITF	ATP
Unreturn-ables per game	1.14	1.09	0.91	1.28	1.60
Serve Strength per game	0.68	0.70	0.68	1.05	1.37

4. Rec and pro tennis look similar on the surface—but for different reasons.

Both have many short rallies, but recreational players end points early because of errors, while pros end them early with more precision and power. College tennis sits in the middle, demanding more patience per rally. (see this great related study showing that juniors worked significantly more per point during an average match than their professional counterparts.)

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For Coaches

College-level players aren't “mini-pros”. They’re in a distinct developmental phase where technique and confidence in their serves and backhands can form a foundation for more skilled play.

For Young Players & Parents

The recreational players assessed here are mostly adults, not highly competitive tweens. But we can still glean that patience and fundamentals matter most for a competitive young player. The serve and forehand only become difference-makers later.

For Adult Recreational Players

You’ve heard it before, but it bears repeating: your rallies end quickly because of errors, not winners. If you just want to win the match, be consistent and patient. But if you are taking lessons and trying to improve, your backhand and your serve are probably going to fail you more before they actually turn into reliable weapons. Embrace that as part of the process.

For Tennis Fans

The data confirms what your eyes suggest—pros aren’t just more consistent, they're more efficient, turning neutral balls into winners quickly. College tennis, meanwhile, is a fascinating “sweet spot” where longer rallies and more active points are the norm, giving fans a blend of consistency and aggression.

Conclusion: Tennis as an Evolving System

As skills level-up, the game itself transforms: serves set the tempo of points, forehand winners lurk around every corner, backhands cease to be a liability, and rallies lengthen before shortening again. These levels mirror learning itself: first control, then confidence, then clarity. 

Whether you’re coaching juniors or tracking your own stats, these numbers show what your norm looks like. I hope it’s given you some things to think about with your game, and maybe inspired you to try to reach another level.

This has just been a (not so) quick exploratory analysis of some of this data. There’s more to come. Please stay tuned!

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Data Notes (for the nerds)

These data were supplied by SwingVision for recreational and D1 college matches and from the Match Charting Project for ITF and professional tour matches. This is an early analysis with this data–I haven’t even gotten to correlation let alone causation. It's an interesting exploration of the data nonetheless.

While I’ve tried not to talk about this as if a player moves through these levels sequentially (and I’m sure I slipped up a few times), that’s important to highlight. This is not a longitudinal study of players moving through stages, rather it’s comparing different levels which definitely do not correspond to ages. The 3.5 and 4.0 players might actually be the oldest skill level here–but age isn’t included in the dataset, so we don’t know.

Also, the SwingVision data come with several caveats, namely that errors are not categorized as forced or unforced. For example, for rally lengths, service winners are coded in such a way that it would be very difficult to tell the difference between a service winner that is barely touched by the returner and an unforced error by the returner. All of these shots are coded as 2-shot rallies and understate the number of 1 shot rallies using definitions that readers might have seen elsewhere. I tallied the MCP data the same way for comparability, but some nuance is lost. 

On a related note, the SwingVision and MCP data aren’t directly comparable for this and other reasons of data collection. SwingVision data are collected and tagged by AI through camera vision technology while MCP data are tagged by hand by tennis enthusiasts (including me). The SwingVision data consists of a few hundred matches from 2025, while the MCP data include several thousand matches from 2010 until this year. MCP data are on hard, clay, and grass surfaces. I don’t know what surfaces the SwingVision data are on but would guess mostly asphalt and Har-Tru. 

Both have data quality issues. But different data quality issues. I’ve tried to only make comparisons of percentages and ratios to avoid direct comparisons of the two, but in a couple of places I thought citing the direct counts made more sense and were justifiable. Feel free to yell at me in the comments. That’s what we’re all here for, isn’t it?