Introduction

The definition of forced and unforced errors belongs to the category of advanced statistical parameters. Unlike simple metrics, such as first serve percentage or number of break points, error classification depends on the context of the rally, shot power, and player level.

Even at top ATP and WTA tournaments, error classification remains subjective. Interpretation differs not only between tournaments, but also within the same tournament. Analysts and tagging operators often evaluate similar situations differently, which makes error statistics unusable for match analysis.

Since 2017, Tennis ComStat has been analyzing matches of various levels - from U14 junior tournaments to Grand Slam finals. To eliminate subjectivity and standardize the approach, we developed the TCS S.C.O.R.E. Framework - an error classification system that takes into account most of the possible factors affecting player actions. This framework evolved from our internal manual match tagging methodologies and has now become part of the training for Tennis ComStat's Computer Vision models.

➡️ Disclaimer: This is a beta version of the framework, which is currently being discussed with coaches, players, and analysts. If you find any inaccuracy or contradiction, or if you have a suggestion for improvement - leave a comment or write to us at info@tenniscomstat.com

Scope of Application

The TCS S.C.O.R.E. Framework is designed for tennis analysts, match taggers, coaches, and players who need a structured and objective method for analyzing errors in tennis. This framework is useful for:

🔹 Match taggers – helps standardize the video tagging process, ensuring data accuracy and minimizing subjectivity.

🔹 Analysts – allows working with clean and consistent data, conducting detailed match analysis, and providing meaningful recommendations to players and coaches.

🔹 Coaches – used for tactical match analysis, identifying players' weaknesses, and developing strategies.

🔹 Players – provides clear understanding of how they earn and lose points: through their own errors or opponent pressure.

🔹 CV/ML developers – serves as a foundation for training algorithms and automatic error recognition.

The framework helps all participants of the tennis ecosystem speak the same language and share a unified understanding of what makes an error forced or unforced.

The Problem

In tennis, most points are earned not through winners and aces, but through errors. The difference between them is not always obvious, but traditionally they are divided into:

• Forced Errors – occur under pressure from the opponent.
• Unforced Errors – occur without external pressure.

Three Levels of Statistics

Tennis metrics can be classified into three distinct categories:

1. Basic stats - there is no room for different interpretations, meaning there is no variability in counting. This includes first serve percentage, double faults, or converted break points. We cannot count them differently

2. Advanced stats - here different interpretations are possible, the definition of such indicators requires context analysis (forced/unforced errors, net play, approaches to the net).

👉This category of metrics includes not only forced and unforced errors. Even a player's success at the net or the effectiveness of passing shots can be viewed differently, depending on how the rally develops and the players' court positioning.

3. Complex Aggregated Metrics - calculated based on basic and advanced statistics (serve and return efficiency, shot quality, success in attack). Such metrics can take into account both one or two, as well as dozens, and even hundreds of parameters.

Alternative Approaches

There are also alternative approaches to classifying errors in tennis:

1️⃣ "There are no unforced errors"

Some coaches believe that any error is a consequence of external factors: a good shot by the opponent, tactical pressure, fatigue, an unfortunate bounce, or psychological state. In this approach, unforced errors are not distinguished, as each error is explained by the influence of external conditions.

2️⃣ "Attack and Defense"

This approach suggests not classifying errors, but analyzing them in the context of the rally: whether the player was attacking, defending, or transitioning to a neutral position. In this case, the difference between Forced and Unforced Errors loses its significance, and errors are evaluated based on the phases of the rally - an error in defense or an error in attack.

3️⃣ Error Spectrum

Some analysts propose a more complex classification, in which errors are not divided into two types, but represent a spectrum: from obvious unforced errors (such as missing an easy overhead smash at the net) to "almost winners," when a player only manages to barely touch the ball while attempting to return the opponent's shot.

Why is it important?

Despite alternative approaches, the term Unforced Error remains the standard in official ATP, WTA, and ITF statistics, and is also used by coaches and analysts.

The term Forced Error is used less frequently, but its logic remains unchanged:

💡Everything that is not an Unforced Error is considered a Forced Error.

Let's highlight five key aspects influenced by the ratio of forced and unforced errors:

📊Strategic analysis: Understanding whether an error was forced or unforced helps players analyze their opponents' strengths and weaknesses. Forced errors indicate that the opponent's strategy or skill level is effective, while unforced errors suggest possible issues with consistency or technique.

📈Focus on improvement: By identifying unforced errors, players can concentrate on improving their technique and psychological resilience without the influence of external factors. This approach helps reduce errors caused by lack of concentration or incorrect shot execution.

🎾Match strategy development: Knowing what type of error occurred helps players adjust their tactics during a match. If an opponent frequently makes forced errors, this may indicate the effectiveness of an aggressive playing style. If they make many unforced errors, a more conservative strategy might be more beneficial.

🧠Psychological impact: Awareness of the difference between these types of errors affects a player's mental state during a match. Reducing unforced errors helps build confidence, as the player sees progress in aspects under their control.

👟Training process efficiency: Coaches use this analysis to more precisely adapt training sessions – working on endurance and playing under pressure (to reduce Forced Errors) or improving shot technique and stability (to reduce Unforced Errors).

Thus, distinguishing between forced and unforced errors contributes to improving strategic thinking, increasing training efficiency, and developing psychological resilience, which is critically important for successful performance in tennis matches.