Bringing (advanced) stats to junior tennis
Tennis players are often praised for their “work rate” — how much effort they put into moving around the court. But how exactly can we measure this? In team sports like soccer, work rate refers to off-ball contribution. Tennis is different, because in either singles or doubles, the work rate depends on how movement translates into winning points.
I’ve been observing this for years, but analyzing the data takes a lot of work. Getting match recordings can be difficult without using video systems, and I can only get footage from home games, not away. I wish high school tennis was more like basketball or football where filming matches was standard, but we’re not there yet and some coaches don’t allow it.
I record practices using Swingvision and show players the real-time data on their phones, which has been helpful. But for matches, I use a more laborious process of tagging video afterwards with Dartfish or Swingvision, less for efficiency and more to nerd out!
I drew inspiration from basketball’s “hustle stats” and soccer’s pressing intensity metrics. Why not bring that level of tactical insight to tennis? Here are the three metrics I created:
Work Rate quantifies effort through total distance covered per point. It’s scaled from 0-10, where 10 represents exceptional court coverage.
The formula is:
WR (0-10) = (Yards per Point / Maximum Expected WR) x 10
This shows how much ground a player covers in pursuit of each point, highlighting their physical exertion.
CLUTCH reveals effectiveness in pivotal moments. Using a baseline conversion rate of 40%, it measures performance on clutch points compared to expectations:
CLUTCH = (DPS% - Baseline%) x Conversion Factor
Positive scores indicate excelling under pressure, while negative scores reflect faltering.
NET Score
NET Score evaluates overall match efficiency by combining Work Rate and points won/lost. It assesses conversion of effort into points:
NET Score = WR x Conversion Score
Higher scores mean maximizing winning efforts with minimum exertion.
Seeing These Metrics In Action
Let’s examine a fictional match between Serena Smith and Taylor Jackson to demonstrate how these metrics work in practice, where the score was
The match score was 2-6, 6-3, 6-2 in favor of Smith.
Work Rate
- Serena Smith: 9.24
- Taylor Jackson: 9.35
Their nearly identical Work Rates show tireless movement and effort in covering the court.
CLUTCH
- Serena Smith: 3.84
- Taylor Jackson: -1.62
Serena Smith’s positive CLUTCH reveals her ability to capitalize on critical points. Smith’s negative score indicates she struggled during crucial moments.
NET Score
- Serena Smith: 87.34
- Taylor Jackson: 78.22
Doe’s superior NET Score demonstrates how she more efficiently converted effort into winning points compared to Smith.
The Benefits for Players and Coaches
Advanced analytics like these provide:
- A more complete evaluation of strengths/weaknesses
- Targeted insights to inform strategy and training
- Metrics tailored specifically for tennis’s dynamics
While the box score has its place, there is so much it fails to illuminate. Work Rate, CLUTCH, and NET Score shine a light on the hidden side of the game.
I drew inspiration from basketball’s “hustle stats” and soccer’s pressing intensity metrics.
Why not bring that level of tactical insight to tennis? Here are the three metrics I created:
Work Rate
Work Rate quantifies effort through total distance covered per point. It’s scaled from 0-10, where 10 represents exceptional court coverage.
The formula is:
WR (0-10) = (Yards per Point / Maximum Expected WR) x 10
This shows how much ground a player covers in pursuit of each point, highlighting their physical exertion.
CLUTCH
CLUTCH reveals effectiveness in pivotal moments. Using a baseline conversion rate of 40%, it measures performance on clutch points compared to expectations:
CLUTCH = (DPS% - Baseline%) x Conversion Factor
Positive scores indicate excelling under pressure, while negative scores reflect faltering.
NET Score
NET Score evaluates overall match efficiency by combining Work Rate and points won/lost. It assesses conversion of effort into points:
NET Score = WR x Conversion Score
Higher scores mean maximizing winning efforts with minimum exertion.
Seeing These Metrics In Action
Let’s examine a fictional match between Serena Smith and Taylor Jackson to demonstrate how these metrics work in practice, where the score was
The match score was 2-6, 6-3, 6-2 in favor of Smith.
Work Rate
- Serena Smith: 9.24
- Taylor Jackson: 9.35
Their nearly identical Work Rates show tireless movement and effort in covering the court.
CLUTCH
- Serena Smith: 3.84
- Taylor Jackson: -1.62
Serena Smith’s positive CLUTCH reveals her ability to capitalize on critical points. Smith’s negative score indicates she struggled during crucial moments.
NET Score
- Serena Smith: 87.34
- Taylor Jackson: 78.22
Doe’s superior NET Score demonstrates how she more efficiently converted effort into winning points compared to Smith.
The Benefits for Players and Coaches
Advanced analytics like these provide:
- A more complete evaluation of strengths/weaknesses
- Targeted insights to inform strategy and training
- Metrics tailored specifically for tennis’s dynamics
While the box score has its place, there is so much it fails to illuminate. Work Rate, CLUTCH, and NET Score shine a light on the hidden side of the game.
The Relativity of the Net Efficiency Tennis Score Across Levels of Play
This work rate model provides a framework to quantify player effort in tennis. It gives coaches an analytical edge to develop strategic game plans that maximize strengths and improve weaknesses. The metrics reflect both the physical and mental game - effort, efficiency, performance under pressure.
It’s important to note that the range of NET Scores will vary significantly across different levels of tennis. The standards for “Below Average” to “Excellent” are relative to the specific competitive tier.
At elite college or junior tennis, the baseline for an “Excellent” score is much higher than at high school level, given the greater intensity, athleticism and strategy involved. For a typical high school team, the effort and efficiency to achieve each NET Score category will differ. Coaches should calibrate expectations according to data from their competitive circles.
Moreover, collecting such detailed metrics may be challenging outside elite tennis. The resources to accurately measure distance, points played and conversion rates are often limited. While the NET Score offers intriguing insights, its applicability is greatest for programs that can support data-driven approaches.
In essence, the NET Score is a versatile but context-dependent tool. At grassroots levels, its principles can still inform coaching on movement and pressure conversion, even if the metrics are not rigorously tracked. The key is tailoring analysis to the players and teams at hand.