Can training load monitoring be done without wearable devices?

Yes. Multiplying the Rating of Perceived Exertion (RPE) by training duration in minutes gives you the classic training load metric, which can be easily collected via a Google Form. Research shows it correlates well with objective metrics, though its drawback is relying on players to honestly log their data every day.

Can AI really predict injuries?

It can only provide probabilities, not absolute answers. Injuries are multifactorial events, so the model simply alerts you to pay closer attention to a specific athlete, while the final call remains with the medical and strength conditioning staff. Any claim of precisely predicting injuries should be met with skepticism.

What is the Acute-to-Chronic Workload Ratio?

It divides the training load of the past week by the average load of the previous four weeks. A high ratio indicates a sudden surge in short-term training volume and is a widely cited injury risk indicator. The academic community still debates its calculation method, making it suitable as a reference rather than the sole basis for decisions.

Is GPS tracking accurate for indoor sports?

No, due to poor indoor signal reception. Indoor sports require inertial sensors combined with venue positioning base stations, such as Ultra-Wideband (UWB) systems. The cost and deployment methods differ completely from outdoor setups, so facility conditions must be evaluated before implementation.

Managing Training Load with AI: Transitioning Sports Injury Prevention from Intuition to Data

Professional teams have long used wearable sensors to manage player load, but most Taiwanese squads still rely on athletic trainers' gut feelings. This article explains the principles of training load monitoring, what AI injury risk prediction can actually achieve, how teams without a budget can get started with a simple spreadsheet, and the three biggest misconceptions.

A strength and conditioning coach for a college team once told me something that left a deep impression: "Our players aren't beaten by their opponents; they're beaten by the extra practice from two weeks ago."

He was referring to that classic scenario: tension builds before a match, the coach ramps up the training volume, the players want to push harder, and sure enough, someone pulls a muscle a week before the game. Afterward, everyone says, "It was just bad luck."

In reality, that isn't luck. It's something that can be seen.

What Training Load Actually Measures

In sports science, "load" is divided into two types: External load is what you actually did (kilometers run, number of sprints, weight lifted), while internal load is how much your body absorbed (heart rate, perceived fatigue, recovery status).

Running five kilometers on a sleep-deprived day puts a completely different burden on the body compared to a well-rested day. This is why looking at the training schedule alone is never enough.

In professional teams, it's common practice for players to wear vests embedded with GPS and inertial sensors to track running distance, acceleration/deceleration counts, and impact load. Catapult is the most widespread name in this field, used by over 4,000 teams globally. For indoor sports where GPS signals can't reach, ultra-wideband positioning is used instead—the exact technology utilized by the NBA and NFL, provided by KINEXON.

The Role of AI Here Might Surprise You

Many people think the function of AI is to "predict who will get injured." That's a massive overstatement.

In practice, platforms like Zone7 integrate the data a team already has—training loads, fitness test results, and past injury histories—to calculate a risk level for each player and highlight which specific factors triggered the alert.

What's the difference? "This player will get injured next week" is a prophecy. "This player's load has remained consistently high for the past three weeks, and the last time a similar situation occurred, a muscle strain happened two weeks later" is actionable data. The former is unreliable; the latter is extremely useful.

Moreover, there is a prerequisite to this that is often overlooked: models rely on data. If a team hasn't systematically recorded training volume and injuries in the past, the first few months after adoption are spent just backfilling data, not receiving recommendations. This is why many teams buy systems and feel they are useless—it's not that the system doesn't work; it's that there's nothing for it to learn from yet.

How Teams Without a Budget Can Get Started

A set of wearable devices can cost hundreds of thousands of dollars, which most school and amateur teams in Taiwan cannot afford. However, the most valuable part of load monitoring doesn't actually require hardware.

Rating of Perceived Exertion (RPE) multiplied by training duration has been a staple training load metric in sports science for decades. The process is almost laughably simple:

Thirty minutes after each training session, have players fill out a subjective intensity score from 0 to 10.
Multiply this by the duration of the training in minutes to get the daily load.
Sum these up to calculate the weekly load.
Calculate the "acute-to-chronic workload ratio" (load of the most recent week ÷ average load of the previous four weeks).

The fourth metric is the frequently cited acute-to-chronic workload ratio. If this ratio is noticeably high, it indicates a short-term spike in training volume, signaling a time to take a closer look. While academia still debates the exact calculation method for this indicator, it is more than sufficient as a first warning light for grassroots teams.

A Google Form, a spreadsheet, and the willingness to spend five minutes looking at it every day are all you need to get started.

Three Common Misconceptions

First, data doesn't make decisions. When a system flashes a red light, it's reminding you to check on that player, not telling you to instantly drop them from the roster. Actual judgment must combine a physical therapist's palpation, the player's subjective feedback, and the importance of the match.

Second, match-day loads are frequently missed. Many teams only record training, but match intensity is often double that of training. Leaving this out distorts the entire cumulative load.

Third, recovery is harder to record than training. Sleep, nutrition, academic stress, relationship issues—these have a massive impact on adolescent athletes, yet they rarely appear in any load system. Experienced coaches know that when a kid is acting off lately, it usually has nothing to do with the training schedule.

The Reality in Taiwan

Professional sports in Taiwan have begun adopting load monitoring in recent years, but grassroots levels still largely rely on "physical therapists judging by experience." This isn't because coaches don't care, but rather a structural resource issue: a single physical therapist might be handling three teams simultaneously and simply has no time for long-term data tracking.

I think there are two pragmatic paths forward.

The first path is to save the data first. Even if you can't analyze it right now, use a simple form to record training volume and injuries. A year from now, you will possess a dataset that no one else has. The cost of doing this is nearly zero, but its value accumulates over time.

The second path is to partner with university sports departments. Taiwan's sports science research capacity is actually quite strong. Sports science students need real-world data for research, and teams need someone to analyze their data—it's a win-win combination. Rather than figuring it out alone, collaborating with a graduate student often gets you results much faster.

As for purchasing hardware, my advice is to ask one question first: Who is going to look at these numbers after we buy them? Without someone responsible for interpreting them, wearable devices are just a bunch of very expensive vests. That statement sounds harsh, but it's the trap I've seen the most teams fall into.

TheAI Academy Summary & Review

In sports injury prevention, the most expensive thing has never been equipment—it's the cost of having a star player sidelined for six weeks. Professional teams do the math clearly, which is why they buy; grassroots teams can't afford to, so they rely on experience—yet a huge chunk of this gap can actually be bridged with scrappy, low-tech methods.

Review: The true value of load monitoring doesn't lie in predicting the future, but in transforming "I feel like he's a bit tired" into "His load over the past three weeks is 40% higher than last month." The former is up for endless debate; the latter is actionable.

Specific advice for readers: Start collecting RPE this month. One form, five minutes after each training session, maintained for an entire season. When the day comes that you can look at the numbers and tell your coach, "His load last week was double his usual," you will understand the value of this practice—and you won't have spent a single penny to get there.

Further Reading: AI Sports Video Analysis Workflow for Grassroots Teams, Sports Data Analytics Career Guide, and for fitness enthusiasts, refer to the AI Fitness Plan Guide.

Sources

(This article is compiled based on public information and does not constitute medical or training prescription advice. For athlete injuries and illnesses, please consult a professional.)