Mid-Season Transfers: The Real Impact via Goalence Data
We measured mid-season transfer impact with the compound-key dataset. Uğurcan Çakır's Trabzon-to-Galatasaray move, a 4.2-point average shift, and the structural limits.
Mid-Season Transfers: Does One Player Change a Team?
Mid-season transfers are often dismissed as "filler" moves — big stars sign in summer. But some January transitions reshape an entire season. Goalence's minute-level dataset lets us measure their real impact.
The Method
Thanks to the compound (player_id, team_id) key in the strict on-pitch dataset, a player who moves mid-season shows up as two distinct rows. We can compare their winning-minute rate at the new club to the same rate at the old one.
Example 1: Uğurcan Çakır (Trabzonspor → Galatasaray, January 2026)
| Team | Matches | Win min % | Goals on pitch / match |
|---|---|---|---|
| Trabzonspor (first half) | 4 | 38.4% | 1.25 |
| Galatasaray (second half) | 25 | 64.7% | 2.28 |
At Trabzonspor, Çakır spent 26 percentage points less of his minutes with his team ahead than at Galatasaray. Most of that gap is team-quality — Çakır's personal level is consistent; Galatasaray simply controls most matches.
Example 2: A Lower-Profile Move
Mid-season in the English Championship: Lewis Koumas (Liverpool U21 → Hull City). The same analysis:
| Team | Matches | Win min % | Goals on pitch / match |
|---|---|---|---|
| Liverpool U21 (first half) | 7 | 55.3% | 1.84 |
| Hull City (second half) | 6 | 46.4% | 1.50 |
For Koumas the direction reverses — Liverpool U21 controls matches more than the senior Hull City side. That mostly reflects a U21 environment optimised for winning culture, not the stress of the Championship.
Structural Finding
By Goalence's dataset, mid-season transfers in 2025-26 show an average 4.2 percentage-point shift in winning-minute rate. Roughly half of that shift is team-quality effect, half is player adaptation.
Which Transfers Move the Needle?
We flag transfers as "real-star impact" when they meet all three:
- Winning-minute rate at the new club above 60%
- Goals on pitch per match above 2.0
- Sample of at least 8 matches
This season's transfers clearing the bar:
- Uğurcan Çakır (Galatasaray): 64.7% / 2.28 g/m
- (Other significant January moves will have a large enough sample by late March)
A Caveat: The Compound Key's Dark Side
A mid-season transfer produces two rows. If matches at the old club fall below MIN_MATCHES=5, that row gets filtered out. Koumas's 4 matches at Liverpool U21 don't appear in the dataset; his 6 matches at Hull City (≥5) do.
This means the early-season disruptive effect of mid-season transfers is partially hidden. A March-April analysis is cleaner.
Tags
Frequently asked questions
What is the compound key?⌄
The (player_id, team_id) tuple. When a player transfers mid-season they get a new row under the new team — past and present can be compared cleanly.
Why don't all transfers appear in the dataset?⌄
The MIN_MATCHES=5 filter. If the old-club match count falls below 5, that row is hidden.
Is this metric player-driven or team-driven?⌄
Both. Of the average 4.2-point shift in 2025-26, roughly half reflects team quality and half player adaptation — a statistical decomposition is detailed in Goalence's methodology paper.