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2025–26 Serie A Preview
Analytics Over Allegiances
Samuel Rogers
August 18, 2025
The Takeaway:
The 2025–26 Serie A season kicks off with tectonic shifts in tactical identity, financial engineering, and a war of data vs. legacy. Milan’s €200M overhaul, Atalanta’s algorithmic scouting machine, and Napoli’s goal contribution efficiency—this season won’t be won on vibes.
📊 Topline Metrics That Matter
Club | Net Spend (€M) | Avg Age | xG Diff (2024–25) | Possession % | Press Success % | Squad Value (€M) |
|---|---|---|---|---|---|---|
Inter | +18.4 | 27.2 | +24.1 | 58.1% | 41.7% | 568.9 |
Milan | -74.5 | 24.9 | +11.3 | 61.2% | 43.5% | 612.0 |
Juventus | -11.3 | 26.5 | +7.8 | 54.7% | 39.1% | 487.4 |
Napoli | -38.6 | 26.1 | +15.6 | 56.9% | 45.0% | 499.0 |
Atalanta | -2.2 | 25.3 | +19.7 | 54.5% | 46.3% | 368.2 |
💡 xG Diff = Expected Goals For – Expected Goals Against
🧠 The Big Brains Behind the Touchlines
Inter (Inzaghi): Data-backed shape rotation. Highest xT (expected threat) from wingbacks.
Milan (Fonseca): Press-heavy vertical build. Top 3 in Europe in passes into final third per 90.
Juventus (Thiago Motta): Positional play maestro. xThreat model driven by overloads in Zone 14.
Atalanta (Gasparini): Serie A’s Moneyball lab—6 players scouted from under-23 leagues with sub-€8M tags.
🔥 Who’s Hot by the Numbers?
Victor Osimhen (Napoli)
2024–25 Goals: 18 | xG: 19.2 | NP Goals per 90: 0.74
💣 Finishing slightly under xG, but elite movement metrics.
Joshua Zirkzee (Milan)
Age: 24 | Dribbles Completed per 90: 3.9
🔥 Most progressive carries per 90 among Serie A strikers.
Teun Koopmeiners (Atalanta)
Key Passes per 90: 2.6 | Progressive Passes: 9.1
🧠 Tactically invaluable; 6.2 OBV (On-Ball Value) per match.
📉 Regression Watch
Roma: Overperformed last season by +9.3 in expected goal difference vs actual. Aging midfield with declining ground duel %.
Lazio: Shot conversion (13.9%) way above expected (10.1%)—prime for offensive drop-off.
📈 Breakout Candidates (By Data Trendlines)
Player | Club | U23 | P90 OBV | Note |
|---|---|---|---|---|
Matías Soulé | Juventus | ✅ | 0.42 | Returning from Frosinone loan, elite 1v1 winger. |
Tommaso Baldanzi | Roma | ✅ | 0.38 | High press-resistance, creative playmaker role. |
Giorgio Scalvini | Atalanta | ✅ | 0.35 | Top 3 Serie A in defensive actions per 90. |
💰 Serie A Economics Snapshot
Revenue Stream | Avg Per Club (€M) | YoY Growth |
|---|---|---|
Broadcasting | 110.2 | +3.1% |
Matchday | 29.4 | +5.8% |
Sponsorship & Comm | 65.1 | +8.7% |
Transfers Profit | 19.8 | -2.3% |
Inter, Milan, and Napoli are now above €500M annual revenue clubs—closing the European competitiveness gap.
🧬 Predictive Table (Blunt Model v1.3, xG + OBV-weighted)
Rank | Club | Projected Pts | Reason |
|---|---|---|---|
1 | Inter | 83.4 | Balanced xG/xGA, squad depth, OBV leadership. |
2 | Milan | 80.2 | Most improved OBV spine (Zirkzee, Reijnders). |
3 | Napoli | 77.8 | Osimhen, Kvaratskhelia still over xG curve. |
4 | Juventus | 73.1 | Motta-style system stabilizing. |
5 | Atalanta | 71.4 | Top-5 in all efficiency metrics, but depth limited. |
📌 Bottom Line
This is not your dad’s Serie A. The 2025–26 campaign is a case study in applied analytics:
Younger squads.
Data-led signings.
xG optimization.
Financial sustainability + algorithmic scouting.
If you're not reading OBV, xT, and progressive pass maps—you're already behind.
OBV in the context of sports analytics (specifically football/soccer) typically stands for:
🧠 On-Ball Value (OBV)
Definition:
OBV is a metric that quantifies the value of a player’s actions when they are on the ball. It assigns a number to each pass, carry, or dribble based on how much it increases (or decreases) a team's likelihood of scoring.
📊 Why OBV Matters
Most traditional stats like goals, assists, or even xG only tell part of the story. OBV captures how valuable a player is in progressing play, breaking lines, or setting up goal-scoring opportunities—even if they don’t get the assist or shot.
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