Sudamericana 2025 assists: volume leaders versus per-minute efficiency

Scope and method

The analysis uses the completed-match record for the 2025 CONMEBOL Sudamericana: 156 matches and 375 goals. The assist tables use player-season-team rows supplied by the Xtra-Stats football database. A player is shown once for the relevant team context, and recorded minutes are kept beside the assist count so the reader can see the workload behind every rate.

There are two views. The raw table is ordered by assists and keeps every leading line visible, including players with short tournament runs. The efficiency table retains only players with at least 450 recorded minutes and calculates assists divided by minutes, multiplied by 90. Values are displayed to three decimal places. That floor is a comparison rule, not a claim that 450 minutes is a universal definition of a regular starter.

The distinction matters in a knockout competition. A creator may produce quickly over a handful of appearances, while another may accumulate chances across a longer route. Showing both views makes that difference explicit instead of hiding it behind a single ranking.

Raw assist volume

M. Miljevic is the clear raw leader in this extract with five assists for Huracan. Gustavo Scarpa follows with four for Atletico-MG. The next cluster is made up of players on three assists, but their minutes range from 455 to 1,031. The count therefore captures contribution accumulated in the competition, not the speed at which it arrived.

The table also explains why a raw-only headline can be incomplete. Miljevic has two more assists than the next player, but the minutes column shows a much smaller workload. At the other end of the three-assist group, M. Barrios has more than 1,000 minutes. Both records are useful: one describes the total return, the other describes the volume of opportunity behind it.

Minutes-adjusted efficiency

After the 450-minute floor is applied, 18 rows remain. W. Mendieta moves to the top with three assists in 455 minutes, equivalent to 0.593 assists per 90. G. Abrego is close behind at 0.590. The rate is not a prediction of future output; it is a normalized way to compare the eligible workloads.

The top of the normalized table is tightly packed. A difference of 0.003 separates Mendieta and Abrego, so it would be excessive to describe that margin as a decisive superiority. The stronger finding is the change in lens: the top rate comes from a three-assist line, while the raw leader is not eligible because the recorded workload is below the floor.

Why the leaders diverge

Miljevic’s five assists are the clearest counterexample to treating assists per 90 as a replacement for the raw count. His 318 minutes are 132 minutes short of the threshold, so he is intentionally absent from the second table. That is a classification decision, not a claim that his contribution was unimportant. It says that a short run carries more uncertainty when projected onto a full 90-minute unit.

Mendieta shows the opposite profile. Three assists are not the largest total in the competition, yet 455 minutes place him just above the floor and produce the best eligible rate. Abrego is almost identical. Their proximity also shows why the displayed precision should be read as a reporting convention: small changes in minutes or event recording can alter the third decimal.

Scarpa provides a useful bridge between the lists. Four assists put him second in raw volume, but 962 minutes lower his normalized return to 0.374. That is still a strong total contribution, simply spread across a much larger workload. Once Caldas has several players in both the raw and rate views, illustrating how a team can supply different kinds of creative profiles.

How to read the comparison

Use the raw table when the question is who supplied the most final passes in the tournament record. It is the clearest view of accumulated output and is especially relevant to a team reviewing the total contribution from a campaign. It also preserves short-run performances that a minimum-minute rule would hide.

Use the rate table when the question is how frequently eligible players produced assists relative to their recorded time. It helps compare players with different workloads, but it should be read with the minutes column beside it. A rate based on 455 minutes does not carry the same exposure as a rate based on 1,071 minutes, even though both pass the floor.

For a balanced scouting or editorial read, start with both lists, then ask which workload, team role and route through the competition best explain the gap. The data supports a careful statement: Miljevic led the raw count, while Mendieta led the filtered rate. It does not support a single universal “best creator” label without a chosen definition of value.

Team context in the eligible group

The filtered table contains several players from the same clubs, which is useful context rather than noise. Club Guarani places Mendieta and Manzur in the eligible group, while Godoy Cruz places Abrego, Poggi and Andino there. Those clusters can reflect a team’s route, its set-piece responsibilities, or a shared attacking structure. The table records the output, but it cannot decide which explanation is correct.

Once Caldas also appears with multiple profiles. Barrios carries 1,031 minutes and three assists, while Garcia has three in 737. The equal totals hide a large difference in workload, and the normalized values separate them at 0.262 and 0.366. A reader interested in reliability may prefer the longer exposure; a reader interested in concentrated production may focus on the higher rate. Both readings are consistent with the same rows.

Atletico-MG and Lanus show the other side of the comparison. Scarpa’s four raw assists remain the second-highest total, yet the longer 962-minute workload puts him below the leading rate lines. Lanus has three players in the lower part of the eligible rate table despite strong minutes. That does not make the contributions unimportant; it illustrates how accumulated match time can coexist with a lower per-90 return.

These team patterns are why the tables keep team names visible. A player-level ranking detached from team and minutes can invite a false precision. Keeping those columns beside the result makes the comparison auditable and gives a natural next question: whether a creator’s rate was produced by role, schedule, or a particular team environment.

FAQ

Who led the raw Sudamericana assist table?

M. Miljevic led the supplied raw rows with five assists for Huracan, recorded in 318 minutes.

Who led after the minutes filter?

W. Mendieta led the 18 eligible rows with 0.593 assists per 90 from three assists in 455 minutes.

Why is the raw leader missing from the rate table?

The comparison keeps only players with at least 450 recorded minutes. Miljevic’s 318 minutes fall below that deliberately stated floor.

Does assists per 90 measure chance creation perfectly?

No. It normalizes recorded assists by time. It does not describe unconverted chances, role, opponents, or the quality of every event record.

Limits and interpretation

This comparison is descriptive. It depends on the supplied assist and minute records, the team-season grouping, and the 450-minute floor. Assist attribution can differ by data provider, and a knockout schedule gives players unequal routes and workloads.

Rates should therefore be read beside raw assists, minutes and team context. The tables do not infer future performance, replace match analysis, or turn a small workload into a certainty. They make two transparent views available so the reader can choose the one that fits the question.

There is also a selection effect in any floor-based table. A player who left the competition early may never reach 450 minutes, even if the assists arrived quickly. A player who advanced further receives more opportunities to add totals, but also more minutes in which the rate can settle. The raw list preserves the first story; the filtered list standardizes only part of the second.

In practical terms, the safest comparison is the paired statement recorded above: five assists for Miljevic in the raw view, 0.593 assists per 90 for Mendieta among eligible workloads. The numbers are strongest when their definitions remain visible.

That transparency is the intended result: readers can return to the same rows, check the workload and decide whether volume or efficiency fits their purpose.