SwimSwam's 2028 Recruiting Database: Youth Swimming Data and the Load Question

On June 15, 2026, the official line of contact opened between American college swimming coaches and the group of athletes belonging to the high school graduating class of 2028. Before that date, those names existed only as rows of times scattered through the recruiting database that SwimSwam maintains in public. After that date, each row became a file that could be called, texted, and invited for a campus visit.

The average age of this group is 15 to 16. At that age, a swimmer may already have accumulated six to eight years of continuous training, with volumes of 30 to 45 kilometres per week at peak, plus two dryland sessions and one dedicated technique session. Their shoulder has completed hundreds of thousands of revolutions. Their lumbar spine has absorbed thousands of repeated extensions during butterfly and breaststroke sets.

I am writing this for a reason other than covering a product launch. That database sits at the intersection of three things I have tracked for my entire career: public data, training load, and a body that is not yet biologically finished.

How the SwimSwam recruiting database operates

SwimSwam is among the most widely read specialist swimming outlets today. Within that editorial team, Anne Lepesant is the lead writer on recruiting and the person who runs the outlet's recruiting database system. She maintains a separate page for each graduating class: 2026, 2027, 2028, and beyond.

The structure of each entry is simple. A row contains the athlete's name, their club or home team, their best time in their primary event, the date of their verbal commitment, and the university they have committed to attend. Some rows carry additional notes: a club transfer, a decommitment, an injury that forced a long absence, or a pause in competition.

It is worth being clear about the nature of the product. This is a media product. It is presented as a utility for fans, parents and coaches themselves, while also serving the outlet's traffic — an outlet where the person running this section is a principal member. Noting that is not an attempt to diminish the database. It only helps place it correctly: a tracking tool, not a medical record, not a development record.

The American college system is the soil that feeds this database. A swimmer entering college has four years of competition within the NCAA framework. Athletic scholarships in Division I are allocated under equivalency caps: women's swimming and diving has 14 full-equivalency scholarships per school, men's has 9.9. That number is divided across an entire roster, which means most athletes receive partial scholarships, not full ones.

The race therefore does not happen only in the pool. It happens on paper, where the cap is fixed while the number of applicants grows. In a market like that, information becomes an asset. Whoever knows earlier holds the advantage.

The college season is contested in 25-yard short course, known as SCY, while most international and regional meets are contested in 50-metre long course, known as LCM. That difference matters when reading data. A 50-second 100-metre freestyle in long course does not equate to a 44-second 100-yard freestyle in short course. Conversion tables exist, but they are approximations, and the largest error sits in events with many turns — where push-off technique and underwater work carry decisive weight.

That is the first reason I read this database with caution. A number entered into a table always arrives with the conditions that produced it, and those conditions are rarely recorded.

What a best time at 15 actually measures

When a 15-year-old swims a personal-best 200-metre individual medley, that result is a composite of at least four variables. The first is the timing of biological maturation. The second is training age. The third is technical quality. The fourth is competitive opportunity. The table records only the final result, not the weight of each variable.

Biological maturation timing is the most undervalued variable. Growth studies of adolescents typically use the concept of peak height velocity, the period when the body gains height fastest. In girls this usually falls around ages 11 to 13; in boys, around 13 to 15, with a fairly wide standard deviation. That means within one chronological age group, some athletes passed their growth peak two years ago while others have not entered it.

Height and arm span directly affect the length of each stroke cycle, and therefore the number of cycles needed to complete a length of the pool. A swimmer 12 centimetres taller can cut the number of cycles per 50 metres substantially without adding propulsive force. That biological gap is routinely misread as a talent gap.

Training age is the second variable. Two athletes with the same time at 15 can have entirely different training ages: one has swum six years, the other two and a half after switching from another sport. Their ceilings and their injury risks are not the same. An athlete with a short training age usually has large technical headroom, and their musculoskeletal system has not accumulated adaptive tissue changes — both an opportunity and a risk.

Technical quality is the third variable, and the only one that can improve quickly. In short events, turn technique and the number of underwater metres inside the 15-metre mark can produce half a second of difference over 100 metres. In distance events, stroke efficiency and stable cadence matter more. An athlete with good technique but low volume can match someone training half again as much.

Competitive opportunity is the fourth, and the most systemic. Racing in a highly competitive meet, with evenly matched opponents in adjacent lanes, with good recovery conditions and proper equipment, produces a gap the table cannot show. In swimming, the phenomenon of going faster alongside a strong opponent is called the lane effect. That effect is never recorded in the results column.

The body is a closed system, but data is the key that opens it. I use that line when explaining how load accumulates inside a body that nobody can measure from the outside. The recruiting table measures the output, not the cost.

Load: the number that never appears in the database

In the recruiting database there is no column for weekly training volume. No column for two-a-day sessions. No column for hours of sleep. No column for the number of meets in the past twelve months. No column for injury history.

That is the largest gap, and it is not a technical error by the people building the data. Those indicators belong to club-internal data and are not published. But the consequence is real: a reader of the table sees only the visible portion.

A 15-year-old swimmer may be training 35 kilometres per week during a general preparation block, dropping to 20 during a deload week, then rising to 40 before a meet. If they race six to eight times a year, each with a taper week and a post-meet recovery week, the total time actually spent under heavy load gets compressed considerably.

In swimming, volume does not rise linearly; it rises in steps. Coaches typically apply three-week build blocks with one deload week, or four-week builds with one deload. The common rule of thumb is not to increase a new week's volume by more than 10 percent above the previous highest week. When the competition calendar densifies, that rule is the first to bend.

At ages 13 to 16 the problem is more serious because the body is still growing. Long bones lengthen first; tendons and muscles adapt afterwards. During that phase mismatch, the same training volume can generate far more stress than for a mature athlete. Tendon attachment points around the shoulder, the knee and the pelvis are the most vulnerable sites.

In Vietnam, I once built a load-monitoring system for a football club in Hai Phong. In its first year, the system recorded 127 injury cases across 43 monitored players in a single season. The coaching staff at the time considered the approach overly defensive. Four months later, eight high-risk players were identified before their tissue damage became a long-term injury, and the team's injury-related days lost fell 23 percent compared with the first half of the season. That lesson transfers to swimming, because the underlying problem is identical: overuse injury forms before it shows.

The injury map at ages 15 to 17

The shoulder ranks first in every swimming injury statistic. Medical reviews of swimming typically place shoulder injuries at roughly 40 to 60 percent of all injuries. Most are overuse rather than contact injuries: supraspinatus tendinopathy, subacromial impingement syndrome, labral injury, and in adolescents apophysitis around the humeral head.

The mechanism is fairly clear. Freestyle and butterfly both involve repeated overhead pull phases thousands of times per session. When external rotation range is insufficient, the humeral head translates upward during the pull, narrowing the subacromial space. Each time that happens, the supraspinatus tendon is compressed. A 5,000-metre freestyle session can generate thousands of those small compressions.

The lower back ranks second. Butterfly and breaststroke require repeated lumbar extension. In adolescents the pars interarticularis is the weakest part of the vertebra, and spondylolysis is the characteristic injury of young butterfly swimmers. Early signs are usually only back pain on extension, pain during butterfly, and pain when sitting for long periods. Athletes often hide it because they fear being removed from the competition roster.

The knee ranks third, tightly linked to breaststroke. The whip kick generates rotational and opening forces at the knee. Surveys of breaststroke swimmers record a significant share who have experienced medial knee pain, involving the medial collateral ligament and the soft tissue around the joint. In some cases, synovial tissue becomes trapped in the joint line, causing pain and a clicking sound.

The ankle and foot also appear, particularly among breaststroke swimmers and those doing extensive fin kicking. Stiff training fins increase the moment of force at the ankle during kick sets.

What I want to emphasise is probability, not a list. For a 15-year-old training 35 kilometres per week, the probability of at least one episode of shoulder pain lasting more than two weeks in a season is high. That probability rises if volume increases quickly, if there is a strong growth spurt, or if the meet calendar is dense.

Every fall has a graph, and every graph has a breaking point. In swimming, that graph is usually the weekly training-volume graph, and the breaking point usually appears in the third or fourth consecutive week of rising load without a deload week.

What changes when data becomes public

A public data table changes the behaviour of everyone around it. Athletes compare themselves with the names on the same page. Parents compare their children with other people's children. Club coaches use the table as a yardstick to position their athletes. College coaches use it as a first shortlist.

In the other direction, public data shrinks the rumour market. Before public aggregate tables existed, information about which school was tracking a young athlete travelled through coach networks and club gatherings. Families with broad connections learned earlier. A public table flattens that information advantage to a degree.

In Vietnam, the information landscape for this age group is almost empty. No public database aggregates junior results by event, by age, by club, alongside injury status. Information exists as meet entry lists and is stored in fragments. The consequence is that when a young athlete improves quickly, nobody can place that improvement beside the athlete's own historical data.

That is why I argue the biggest gap in Vietnamese swimming at ages 13 to 17 is not in the pool. It lies in the absence of any standardised way to measure training volume, days of rest between meets, and episodes of shoulder or back pain at this age. Vietnam's leading swimmers such as Nguyen Thi Anh Vien and Nguyen Huy Hoang have had long careers and strong accumulated results, largely because load management and recovery were taken seriously enough during the decisive years.

At Lach Tray, I learned to read injuries from the first numbers. The lesson there was simple: without measuring training volume, any conclusion about the cause of an injury is only a guess.

The contrarian angle: the database does not create the race, it makes the race visible

The common critique of youth ranking tables is that the table itself creates the pressure and pushes athletes into injury. I disagree with the conclusion.

The American college recruiting race existed before any aggregate page. It exists because scholarship caps are fixed, because the number of schools with strong swimming programmes is finite, and because a place at a good school carries clear economic value. What a public database does is turn a dispersed process into an observable table. What can be observed can be optimised. What can be optimised can also be optimised badly.

The real problem lies elsewhere. The recruiting system is buying a curve, not a ceiling. At 15, the curve is heavily dominated by maturation timing, especially among boys. An early-maturing 15-year-old boy can be substantially faster than a late-maturing peer of equal talent, and that gap can reverse entirely after two years. If so, the recruiting market overpays for early maturers and underpays late developers, while injury risk is higher among early maturers because their bodies carry the same training volume on a skeleton that is still developing.

A second paradox: people assume that a faster time at 15 means a higher ceiling. Long-term tracking data from junior cohorts frequently shows the opposite in a meaningful share of cases. Deceleration in improvement appears in both early leaders and chasers, but among early leaders it is typically accompanied by two factors: higher training volume at a younger age, and less rest during the mid-season window.

A third paradox concerns the outlet itself. A media product operates on a traffic logic. Traffic rises when there is a ranking, when there is a leader, when someone falls behind. A recruiting database therefore has a natural tendency to foreground competition and blur process. That is not wrong for media. It simply means the reader must supply the missing half.

What to watch next

There is a simple test I want to run on the class of 2028 in three years: how many of the athletes who led the table at 15 are still in the leading group at 18, and among those who dropped out of that group, how many cases carry a note about injury or a pause in competition. If the injury rate among early leaders is significantly higher than among the chasers, the first question to ask is not about talent but about training volume at 15.

The second test is for Vietnam. Do we need a public database for ages 13 to 17, and if so, what columns must be added. My answer is yes, but with two columns SwimSwam does not have: weekly training volume and current injury status. A ranking table for 15-year-olds without load data merely repeats the mistake we are trying to correct.

Numbers stay silent, but their sequence always tells a story. A sequence of results placed beside a sequence of training volume and a sequence of rest days tells a very different story from a results column standing alone. The work of anyone handling data is to assemble enough columns, before anyone rushes to conclude that a 15-year-old is a talent or simply the product of maturation timing.