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Data Analysis6 min read

Volume Is Not Efficiency: Rereading 2024 J1 Strikers Through Shot Conversion

Words & data analysis | Choi Bong-jin (Far Post Analytics operator)

Léo Ceará was both the top scorer and the top shot-taker. Rank by shot conversion instead and Ryo Germain buried a third of his attempts, while 22-year-old Akito Suzuki climbs into the top three. We also show why a conversion leaderboard needs a sample filter.

The 2024 J1 scoring charts were topped by Kashima Antlers’ Léo Ceará with 21 goals. Open the detailed stats, though, and he also took the most shots of any J1 player in our dataset — 85 of them. The goals came with volume attached. Which raises a natural question: if you rank by efficiency — the rate at which shots become goals — how does the order change?

Every figure below is calculated directly from 2024 J1 data (API-Football, players with detailed stats collected). Conversion is defined as goals ÷ shots.

Most goals ≠ best efficiency

Start by placing the top scorers next to their shot totals and conversion. Ceará, the outright scoring leader, converts at 24.7% — solid, but not the best. The player at the top of this table is actually the runner-up in goals: Sanfrecce Hiroshima’s Ryo Germain, 19 goals from just 56 shots, a 33.9% conversion rate. He took 29 fewer shots than Ceará and finished only two goals behind.

Shot volume — highest totals
Léo Ceará
85
Takashi Usami
66
Yoshinori Muto
64
Marcelo Ryan
56
Ryo Germain
56
Yuma Suzuki
48

Source: API-Football 2024 J1. Calculated by Far Post Analytics.

2024 J1 top scorers — shots and conversion (source: API-Football)
PlayerClubGoalsShotsConv.
Léo CearáKashima218524.7%
Ryo GermainHiroshima195633.9%
Yuma SuzukiKashima154831.3%
Marcelo RyanFC Tokyo145625.0%
Yoshinori MutoKobe136420.3%
Takashi UsamiG-Osaka126618.2%
Rafael EliasKyoto114723.4%

The conversion trap: why you need a sample filter

It is tempting to rank by conversion alone, but sort with no conditions and the table becomes meaningless. It fills up with tiny samples — two shots, two goals (100%) — and even data artifacts where an incomplete shot count leaves goals higher than shots (for example, six goals off a single recorded shot). Those numbers are noise from sample size and data collection, not finishing skill.

So to use conversion as a scouting metric, you have to impose a minimum shot count. In 2024 J1, 36 players recorded 30+ shots, and that cohort averaged 18.4% conversion. That 18.4% is the baseline for judging a "meaningful" conversion rate — sit well above it and the signal is probably real.

Efficiency leaders: Germain, and 22-year-old Akito Suzuki

Filtered to 30+ shots, the conversion leaders look like this. Ryo Germain (33.9%) and Yuma Suzuki (31.3%) take the top two, finishing at roughly 1.7 times the league average.

The name to watch is third. Sanfrecce Hiroshima’s Akito Suzuki, at 22, scored 10 goals off 35 shots — a 28.6% conversion, and the only player in his early twenties on this table. His shot volume is modest (which is why he is absent from the goal-scoring race), but the quality of each attempt is among the league’s best. That lines up exactly with the finishing profile we flagged in Scouting Report #003 — near-post, reactive shots that attack the goalkeeper’s blind spots.

2024 J1 shot conversion leaders (30+ shots)
Ryo Germain
33.9%
Yuma Suzuki
31.3%
Akito Suzuki
28.6%
Marcelo Ryan
25.0%
Léo Ceará
24.7%

Source: API-Football 2024 J1, 36 players with 30+ shots. Calculated by Far Post Analytics.

How to use it in scouting

Read in isolation, conversion is dangerous; read alongside shot volume and age, it is powerful. Three types separate out: the volume finisher who shoots and scores a lot (Léo Ceará), the efficiency finisher who stays high on fewer shots (Ryo Germain), and the young efficiency finisher whose volume is still low but whose rate is already elite (Akito Suzuki). In the transfer market, those are three completely different bets.

The conclusion points the same way as our per-90 goals column. A raw scoring chart is an excellent season summary but only half a scouting tool. Read shots, conversion and age together, and you get closer to the ranking that matters — next season’s.

Figures in this article are based on 2024-season data provided by API-Football; ages are as of data collection. Per-90 metrics are our own calculations, and the smaller a player's minutes sample, the wider the margin of error. Every number here is a starting point for scouting — never a substitute for direct verification.

✍️ Choi Bong-jin

Operator of Far Post Analytics. I analyze scouting data for the J.League and Asian football. My goal is to find the next transfer-market star where Europe isn't looking.

About the operator