New Signings and Player Props: Do Stats Travel When a Player Changes Clubs? (2,358 Transfers Analysed)
We tracked 2,358 transfers across 30 leagues and compared each player's per-90 numbers before and after the move with 3,219 players who stayed put. Shots travel well (r = 0.81 vs 0.87 for stayers), goals travel worst (0.58 vs 0.70), and five games at the new club tell you less than last season does. Moving into a big-five league cut shots by 22%; joining a weaker team cut them by 16%.
A winger joins a bigger club in August. Five games in, he's taking a shot more per match than last season, and his shots line has already moved. Is that the new normal, or five games of noise?
We found 2,358 transfers with a full year of league football at both clubs and compared per-90 numbers before and after. We did the same for 3,219 players who didn't move, to show how much noise a transfer adds.
Data: StatPair match database · 30 domestic leagues · moves from January 2025 to March 2026, plus summer 2026 signings in the big five · Updated: 13 October 2026
TL;DR
Stats travel, with extra noise. Shots per 90 before and after a move correlate at r = 0.81, against 0.87 for players who stayed.
Fouls lose the least from a move, goals the most. Fouls committed: 0.63 for movers vs 0.67 for stayers. Fouls won: 0.74 vs 0.78. Goals: 0.58 vs 0.70.
Five games at the new club are not enough. After 5 matches, new-club numbers predicted the rest of the season worse than old-club numbers (shots: 0.70 vs 0.81). They drew level at 10 matches and edged ahead at 15. A blend beat both.
Moving into a big-five league cut shots by 22% per 90 and goals by 34%. Leaving the big five added 19% shots. Fouls committed changed by just 3.1%.
Downward moves hurt more than upward moves help. Joining a clearly more dominant team added 9% shots per 90; joining a weaker one cost 16%.
New signings foul more at first: 1.32 fouls per 90 in their first five league games, 1.17 from game 16 on. Players who stayed showed a smaller early-season rise (1.22 vs 1.14).
How we measured it
The hard part is knowing which club a player played for in each match. Our records keep each player's club history, so we assigned every league appearance to whichever of the two teams he has been registered with. That worked for 643,236 of 652,304 appearances (98.6%); the rest, mostly old club against new club, were dropped.
We checked the method on summer 2025 moves: Florian Wirtz and Hugo Ekitike (to Liverpool), Viktor Gyökeres and Martin Zubimendi (to Arsenal), and Bryan Mbeumo, Matheus Cunha and Benjamin Šeško (to Manchester United). Each switched clubs in our data on his first competitive game for the new club, matching public reports.
A move starts with the first league match for the new club. Of 9,908 club changes detected, only 32 looked like glitches.
Before: old-club league matches in the 365 days before the move, minimum 600 minutes.
After: new-club league matches in the following 365 days (or until the next move), minimum 600 minutes.
Main sample: 2,358 moves, 1,450 in summer windows and 908 in winter windows.
Control group: 3,219 players at the same club from August 2024 to July 2026, with 600+ minutes either side of 1 August 2025.
Goalkeepers, cups and European games are excluded. Loans and loan returns count as moves.
r is the correlation between before and after: 1.0 means the old number predicts the new one perfectly, 0 means it tells you nothing.
Which stats carry over after a transfer
Stat (per 90)
r, movers
r, stayers
Typical gap, movers
Typical gap, stayers
Shots
0.81
0.87
0.43
0.34
Shots on target
0.75
0.81
0.22
0.17
Fouls won
0.74
0.78
0.38
0.32
Tackles
0.67
0.73
0.47
0.42
Fouls committed
0.63
0.67
0.38
0.32
Goals
0.58
0.70
0.10
0.08
Minutes per appearance
0.27
0.56
14.7 min
10.8 min
"Typical gap" is the average difference between the before and after rates.
Shots travel best overall. Moving costs about six points of correlation, as for shots on target and tackles. Still, only 42.1% of movers stayed within 25% of their old shot rate (stayers: 46.5%).
Fouls lose the least from a move, just four points. Fouls committed are less repeatable than shots for everyone, but a transfer barely makes that worse. See our most predictable stats study for within-season stability.
Goals and minutes are what a transfer scrambles. Goals fall from 0.70 to 0.58, the biggest drop of any count stat; minutes per appearance from 0.56 to 0.27.
Forwards are hit hardest. Their shots carried over at 0.49 for movers against 0.70 for stayers (452 and 391 players), their goals at 0.27 against 0.58. A striker's shot volume depends on the team around him.
How many matches until the new numbers are reliable?
For 1,036 movers with 25+ league appearances at the new club, we predicted their per-90 rate from game 16 onwards using the old-club rate, the first N games at the new club, or a 50/50 blend.
Stat
Old club only
First 5 at new club
First 10
First 15
50/50 blend after 10
Shots
0.81
0.70
0.81
0.85
0.86
Shots on target
0.74
0.57
0.72
0.78
0.79
Fouls won
0.70
0.58
0.70
0.75
0.76
Tackles
0.65
0.56
0.64
0.68
0.71
Fouls committed
0.54
0.44
0.54
0.61
0.61
Goals
0.58
0.43
0.55
0.61
0.66
Correlation with the player's per-90 rate from his 16th new-club appearance onwards.
The pattern is the same for every stat. After 5 games, last season is the better guide: for shots, the average error using the first five games was 0.62 per 90, against 0.45 using the old club. At about 10 games the two are level, and at 15 the new club edges ahead.
A blend is always best. Testing every weighting, the best share for new-club data was 10-30% after 3-5 games, 40-50% after 10 and 50-60% after 15. That echoes our early-season signal vs noise study, but more strongly, because a mover's old numbers come from a different team.
The settling-in period: more fouls, fewer minutes
For 759 summer 2025 movers with 25+ appearances, we compared the first 5 league games with game 16 onwards, against 1,939 players who stayed at their club in 2025/26.
Per 90
Movers: first 5
Movers: 16+
Stayers: first 5
Stayers: 16+
Fouls committed
1.32
1.17
1.22
1.14
Shots
1.35
1.34
1.28
1.27
Minutes per appearance
68.6
72.7
71.3
74.5
Everyone fouls more in August. New signings fouled 13% more early than from game 16 on; stayers, 8% more. So a bit less than half of the spike is about being new. Shots were flat, and new signings played about four fewer minutes per appearance at first.
Moving up or down: leagues and teams
Between leagues
Type of move
Moves
Shots
Shots on target
Goals
Fouls committed
Minutes per appearance
Same league
1,113
−3.0%
−2.8%
+5.1%
−0.6%
67.3 → 66.0
Big five to another big five league
129
−5.6%
−7.9%
−2.5%
−1.7%
66.5 → 65.0
Into the big five
182
−22.1%
−27.1%
−34.2%
+3.1%
71.7 → 61.0
Out of the big five
169
+19.2%
+25.6%
+47.4%
+3.1%
61.2 → 70.8
Between other leagues
765
−1.6%
−5.5%
−3.8%
−0.3%
67.0 → 67.5
Stayers (control)
3,219
+2.4%
+0.8%
−0.8%
+0.1%
69.5 → 69.7
Change in the group's average per-90 rate. Big five = Premier League, La Liga, Serie A, Bundesliga, Ligue 1.
Stepping up into a big-five league is the biggest shock in the data. Shots fell by more than a fifth, goals by a third, and players lost almost 11 minutes per appearance. For a striker on 3.0 shots per 90 in the Eredivisie, that points to about 2.3.
Stepping down works the other way: +19% shots and +47% goals, often for fringe big-five players who became starters.
Fouls committed hardly moved in any group, even though league levels differ: across outfield players, the Premier League (1.10 per 90) and Bundesliga (1.06) sit below La Liga and Serie A (both 1.25). Our fouls betting guide covers what referees and opponents add.
Joining a stronger or weaker team
We measured team strength by shot share: the team's percentage of all shots in its league matches.
Move
Moves
Team shot share
Shots per 90
Goals
Fouls committed
To a stronger team (+5 points or more)
697
45.1% → 55.3%
1.36 → 1.48 (+8.9%)
+8.6%
+3.6%
To a similar team
1,059
49.7% → 49.8%
1.35 → 1.32 (−2.4%)
+4.4%
+1.2%
To a weaker team (−5 points or more)
602
55.6% → 45.6%
1.47 → 1.23 (−16.5%)
−15.7%
−6.2%
A move to a stronger team does raise shot volume, but only by 9% for a ten-point jump in shot share. Moving down costs almost twice as much. Forwards joining stronger teams went from 2.40 to 2.64 shots per 90 (137 moves); those joining weaker teams fell from 2.72 to 2.24 (109 moves).
Summer 2026 signings: early numbers vs last season
Some of the biggest summer 2026 moves into the big five, all confirmed by public transfer reports. "Before" is the last 365 days of league football at the old club; "after" is 2026/27 so far. These are tiny samples of 4-8 games.
Player
Move
Minutes (old / new)
Shots
Fouls committed
Fouls won
Tackles
Enzo Fernández
Chelsea → Man City
2,617 / 328
2.17 → 1.65
1.13 → 1.65
1.44 → 2.47
1.51 → 1.37
Morgan Rogers
Aston Villa → Chelsea
2,835 / 523
2.38 → 2.93
1.30 → 1.03
1.40 → 1.38
1.17 → 1.38
Elliot Anderson
Nottingham Forest → Man City
2,974 / 505
1.12 → 1.07
1.63 → 1.25
2.18 → 3.56
2.75 → 2.50
Yan Diomande
RB Leipzig → Real Madrid
2,476 / 366
1.93 → 1.23
0.69 → 0.98
1.24 → 2.95
1.05 → 1.97
Anthony Gordon
Newcastle → Barcelona
1,660 / 468
2.17 → 2.88
0.60 → 0.38
2.01 → 0.77
0.98 → 0.96
Marc Cucurella
Chelsea → Real Madrid
2,440 / 430
0.63 → 0.00
1.55 → 0.21
0.85 → 0.42
1.88 → 3.77
Lee Kang-in
PSG → Atlético Madrid
1,461 / 457
2.16 → 3.94
1.11 → 2.17
1.17 → 1.77
1.05 → 2.17
Marcus Rashford
Barcelona → Man Utd (loan ended)
1,724 / 356
3.97 → 3.29
0.47 → 0.51
1.31 → 2.02
0.42 → 1.01
League matches only, per 90 minutes.
Some swings look dramatic: Cucurella's fouls down from 1.55 to 0.21, Lee Kang-in's shots almost doubled, Diomande's fouls won more than doubled. Expect most of that to fade. Across all 179 summer 2026 moves into the big five with 900+ old-club and 270+ new-club minutes (median 5 games), the before-and-after correlation is 0.73 for shots but only 0.40 for fouls committed. Rogers (+23% shots) and Gordon (+33%) fit the stronger-team pattern, but the average gain for that kind of move was 9%.
What this means for betting
Start from last season, then adjust for the move
Until about game 10, the old-club per-90 rate is your best starting point. Then adjust:
Into the big five: cut shots by a fifth to a quarter and goals by a third, and expect fewer minutes.
Out of the big five: expect more shots and more minutes.
To a clearly stronger team: add around 5-10% to shot volume, not more.
To a clearly weaker team: take off about 15-20%. This is the bigger effect.
Fouls committed and fouls won: stay close to the old rate. The risk is the market overreacting to a few games.
Blend, don't replace
After 5 games, give the new club about a quarter of the weight; after 10, about half; after 15, a little more. Our 10-match rule study found a similar balance for players who don't move.
Watch minutes and early foul spikes
Minutes per appearance carried over at just r = 0.27, so for props that need a start or 70+ minutes, the new role matters more than the per-90 rate. Early foul spikes can support a fouls over in the opening weeks, but expect them to fade. Our regression to the mean guide explains why.
Method notes
Team assignment uses each player's club history in our database. Matches where he had been at both clubs (1.4%) were dropped.
Robustness: with 1,200+ minutes on both sides (1,006 movers, 1,987 stayers), the ranking holds: shots 0.85 vs 0.90, fouls committed 0.67 vs 0.72, goals 0.67 vs 0.75.
Transfer date is the first league match for the new club, not the signing date. We can't tell loans from permanent deals.
Start data before April 2025 is missing in our database, so we used minutes per appearance, not start rate.
Tackles fell 7.6% among stayers between the two years, so judge tackle changes for movers against that.
Shot share measures team dominance, not quality.
The summer 2026 section is small-sample and descriptive. Nothing here is a causal model.
How to check this with StatPair
Open the player's match-by-match history and compare old-club and new-club games. Under 10 new-club games, lean on last season.
Use the home/away and half filters to spot a changed role, such as fewer second-half minutes.
Use the opponent filter to see how his numbers change against strong and weak sides.
Check referee stats before a fouls bet, and run the match through the AI Analyzer.
Add new signings to your Notebook and recheck them with the Trends Finder after game 10.
Q: Do player stats carry over after a transfer?
A: Mostly, yes. Across 2,358 transfers, shots per 90 at the old and new club correlated at r = 0.81, against 0.87 for players who stayed. Fouls won (0.74) and fouls committed (0.63) were almost as stable as for stayers. Goals carried over least well (0.58 vs 0.70).
Q: How many games before a new signing's stats are reliable?
A: About 10 league games. Before that, the player's numbers at his old club predict the rest of his season better than his new-club numbers. At 10 games they are level, at 15 the new numbers are slightly better, and a blend of the two beats either one.
Q: Do players take more shots after moving to a bigger club?
A: A little. Players who joined a team with at least five points more shot share took 8.9% more shots per 90. Players who moved to a weaker team took 16.5% fewer, so a move down has a bigger effect than a move up.
Q: Do stats drop when a player moves into a big-five league?
A: Yes, for attacking stats. Players moving into the Premier League, La Liga, Serie A, Bundesliga or Ligue 1 from other leagues lost 22.1% of their shots per 90 and 34.2% of their goals, and played almost 11 fewer minutes per appearance. Their fouls committed hardly changed (+3.1%).
Bottom line
A player's numbers come with him when he moves, with extra noise. Shots and fouls travel well; goals and minutes much less so. Old-club numbers are your best guide until about game 10, then blend. The big adjustments are for moves into the big five and moves to weaker teams. For this season's new signings, trust last season more than the first month.